[{"id":"5eccd1c33778cd38fc032efd","name":"Actran","desc":"Actran是用于解决声学，振动声学和航空声学问题的高级声学软件。 Actran被汽车制造商和供应商，航空航天和国防公司以及消费产品制造商所采用，可帮助工程师更好地理解和改善其设计的声学性能。","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Actran.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2020","env":{},"loadCommand":""},{"additionalDesc":"","version":"4.2.5.1","env":{},"loadCommand":"module add AutoDock/4.2.5.1-GCC-5.2.0\nmodule add AutoDock_Vina/1.1.2_linux_x86"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5eccd32c3778cd3520626704","name":"Adams","desc":"机械系统自动动力学分析（ADAMS）是一种多体动力学仿真软件，其中包括用Fortran和C ++编写的用于虚拟原型开发（VPD）的求解器。","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Adams.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.2","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""},{"additionalDesc":"","version":"2018.1","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""},{"additionalDesc":"","version":"2018","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""},{"additionalDesc":"","version":"2015","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""},{"additionalDesc":"","version":"2014.0.1","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""},{"additionalDesc":"","version":"2013.2.0","env":{"STANDARD":"run-adams -f <command-filename>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5eccdf543778cd035c148b88","name":"Aermod","desc":"Aermod（美国环境保护局）是一种稳态羽状流模型，它结合了基于行星边界层湍流结构和缩放概念的空气扩散，包括对地表和高空源以及简单和复杂地形的处理。","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Aermod.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"14134","env":{"MPI":"mpirun -np <mpi-ranks> aermod-mp-v14134.exe","SMP":"mpirun -np <mpi-ranks> aermod-mp-v14134.exe","STANDARD":"aermod-sp-v14134.exe"},"loadCommand":""},{"additionalDesc":"","version":"09292","env":{"MPI":"mpirun -np <mpi-ranks> aermod-mp-v09292.exe","SMP":"mpirun -np <mpi-ranks> aermod-mp-v09292.exe","STANDARD":"aermod-sp-v09292.exe"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"610cab3751410505e4639570","name":"AlphaFold2","desc":"AlphaFold是Google旗下DeepMind开发的一款人工智能程序，它使用深度学习算法通过蛋白质序列来预测蛋白质结构","classifications":["LifeSciences","ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/AlphaFold.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.3.2","env":{},"loadCommand":"module add Anaconda3"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["610a54b351410505e463956e"],"actived":true},{"id":"5ecce9fd3778cd3544b8f30e","name":"Anaconda","desc":"Anaconda是针对数据科学和机器学习应用程序（大规模数据处理，预测分析，科学计算）的Python和R编程语言的免费开源发行版，旨在简化软件包的管理和部署。","classifications":["Aerospace","AcademicResearch","HeavyIndustry","ArtificialIntelligence","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Anaconda.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.10(python2)","env":{},"loadCommand":"module add Anaconda2/2019.10"},{"additionalDesc":"","version":"2024.02(python3)","env":{},"loadCommand":"module add Anaconda3/2024.02"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["60b992a9be44e382f82f0ce6"],"actived":true},{"id":"5ed49d203778cd43d47d7117","name":"Athena","desc":"Athena Athena是一个MHD代码，被广泛用于研究天体物理流动中的湍流\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/athena.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.2-impi-rt","env":{"MPI":"mpirun -np <cores> $ATHENAEXEC -i <input file>","STANDARD":"mpirun -np <cores> $ATHENAEXEC -i <input file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed86aa54e840d3e0cae2556","name":"AutoDock","desc":"AutoDock是一套自动对接工具。它旨在预测小分子（例如底物或候选药物）如何与已知3D结构的受体结合。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Autodock%20%26%20AutoDock%20%20%20Vina.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.2.5.1","env":{},"loadCommand":"module add AutoDock/4.2.5.1-GCC-5.2.0"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed49d9b3778cd42e8eef60c","name":"Autodock & AutoDock Vina","desc":"AutoDock AutoDock是一套自动对接工具。 它旨在预测小分子（例如底物或候选药物）如何与已知3D结构的受体结合,AutoDock Vina是用于进行分子对接的开源程序。 它是由Scripps研究所分子图形实验室的Oleg Trott博士设计和实现的。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Autodock%20%26%20AutoDock%20%20%20Vina.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.2.5.1","env":{},"loadCommand":"module add AutoDock/4.2.5.1-GCC-5.2.0\nmodule add AutoDock_Vina/1.1.2_linux_x86"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["5ed86aa54e840d3e0cae2556"],"actived":true},{"id":"62457020f0463100014f786f","name":"Avizo","desc":"","classifications":["HeavyIndustry","AutoMotive"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=avizq-90x90.png;base64,iVBORw0KGgoAAAANSUhEUgAAAFoAAABaCAYAAAA4qEECAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAAWqADAAQAAAABAAAAWgAAAABJfIu3AAAINklEQVR4Ae2YDXBcVRXH731vE0KTUENkCkGdQcZJ3U/KijjZbRrrlJmCRRTTWkrBAQpStAyDIzrCVKgwqDOOfDhAZFCLOtNGURxgbBkwpknK8NlsdhNGKgxC7UgaWptCwu6+e/zft3vfPpZks5u248ecO7N7z7n33PPu/d1zP94TghMTYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAseZgDzO/v/v3YfO6jjLDDKzZ2iPkcvzQHlBtXo4nFihBO3020tprcuMDPzGX3a0ciTS8QUlxBYi+Qkhaa8t7FtGRnb94Wj9Hqv25IjNUsq/CIv6Kvm0KlVWqlOSNpbXk1DXlJcdrQ7IPUQiIgQ1CBJhJZwHjtbnsWwvSewXpD42l895bR3hczo/StPOa0Rklz/Aqg8E0y/2j5WXz1ePRJZe5Aj1fUA+U0jxNylpcyY19Lv5+vtPtZvX1kFT6hoSRchS9mK22gE9qgdBOedqZDccqwEVt4n/mq0iHE1er4i+Ytn2jek9/UPVjrPmiI7Hr66bzqbfwHJepB9iCfvzSihApju0jv3q4CmtdW19fX3TWv9fSbFY55K8clahv2cU+kxvYl99vrW1focei7uKEWA4J25CUCFA5f7R9GBbteOrOaKnc2MXG8iAOv7hVnvHxASNOVQAjU60jL+dX40ObDWdCEUTVwklLoG+CB0dzYwMdZu68jwYTnxHSuEOAL4OjKaHbtU2oUjHt5CdL0jGM+nBZtMuHF56Afbtx4xeKUd/f58ZGfyS3yYUS3xWOOK2nJNP+su17OA3PpH9VyiS2Kum8mdDldjC3IQ+PlKQqvuvGTSR8h+C2zDbeTzq1VC441n04dPuY5V7KHqgBdGlqFvm1pFYHI12npFK9b9W3sVIpDPiUP4OTKSbAGazsUHZDwpysbJYYVlqgpT8s7Ery1uwxXnXL3A64K8PRhLrEAC/NNugFBJngHgCT9Bj6sREx/HchVit8VI7uR+4b02nBnrQv1LxHFJNoIsglno+LethT5ZiG2bbBY2OdkSjXeFUqi+t6yXJ2zGYZUVbyyH1dcg3FnUvU+R80ygY9BHR1HSv0WfLU6nBZ1C3vLy+q6ur4a2J3E4TgejFpGywthi7SCTZgi1P32gKB7qUuxrqQ5974YWenLEJRjvuwkRsMjr69Ejwk22re3t7nVog6/bYhqpPjnT80fxKZnjXs6a1Leu3QwbjQnIo51310unBJzH3z5k6mF0RjZ7XWNKFiMWWn44ZWeuVSdmT2b3jbU+vQQDkwPhEDhNPblAASl5acnX6uf43jBuHxBWAvMDotrBu8UPW5S3N8mZMkO+soXoN2bSpJa8adCKRaJZKXGqco/O/MrLOEb1v4iQcKJXR+nh8lTcQS4rbTR2W5IeUeme90XWeV9lNGHidljEp2YBV/2Mt15rgW44fyD6E/MJSW9qQSQ38qaRDkuSdE3oiFi8+1df3guXg4OAk+vK8106Kczy5RqFq0IcOy8uw/JtK/mkDDomX/T90Kmzq9d42lT24xuhY4n8EQXcr0WUkxbWmbuXKlSdg77/S6KDw8PDw0/tKevUSDrefYFl5k2hZ4mYcvr8o94AXDf+NYXK2SMU2/LppizEtiseXnmb0WvKqQWMZbvQ7xkM/gqjR92f/r8VvA5ze9oGoIbyiu1dAbYN20Wg08Rkt/33fkS8ia9UykgrY8ocFsbb/YLhjs39PFZa8P50a8lbS+7xJcXJJpwocKFuyEyKXO/H4bR14O1uGaA6aB2KWH9eDmPEnZcbYgea5oSXJmNGD7adtR9TvNTou/vrlBmbOVaYMvrcNDw/81ejV5oD8Ddh+z7OX8tFQe5s+dGdOVOoHDBZg4tG1DyYlpPu+YGqc9oWHjFxLHqjGGK/A15Xs5KQtG9ekUjvfKZWVJNyZL0S0P2pKyKGvQXa3Cb08w9HEnaToQV2PJb4GLwr34A67vGivZF1gS1GuOgvHkuuVo+4yDbB6dp/U1LZ2tu2gaKf3XjcI0N+68JKuT0H3HdhCdHd326Mv74ubE15f/zK9ve+LcPPMufIKS6bQVO9JuKVf5DmyxK9ng6xtgu1tj+NQ/Idnr8Q6/w3jhLrQVnTYPf0xwAV4G/stbN1owt/2Wr+ThGLJJCbuIZ+PvY0nNqzavbt3yuvDDELAtn+KYnyzKqZ87qtGNPnY2L616KMX0VjV3mQam2rzOSN6Oqs26Bk3DqVFFb+e6SgKhZM/R6e+W2hDzQ4duQTyz7Sur1BY5j+CeLfWsWQ/rnMkJesDtxXE6v9JqbuxNErjkHLhkampncFIxwecYCJfNW+lw8P9L+Egvw/Pd1cronYj9MMN9SdvmW6etMREFpAlbj6FeEZw7LGtRj2h80pzRjS8Xq6vP/qHSH2m0sdt04O6QEBvDWbFQZTeoahtFjaf/iD24n9q2SQMpLfWaHbbkjzV+NA5wJ2CJ589409YT/ptuy9esQkHtH7ld19S0Pbb0+9NHKQD2cMIrh7vliXFU02NJy2rtJL9fmeSZzwAZjI81mWhSBIfZ9SdRb/Ym+1I5qVdo7M9B986DmHw+MYhc/iY0zCb3XzKY7FEKOfQlzHZKwB3MQLqPUn0Lp7VZ1tiKw7nAX1rmo9vbsMEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwgX8DtyHz+VWJXdUAAAAASUVORK5CYII=","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"9.0.1","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1648717856401,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed49ef23778cd49440c2290","name":"Bamtools","desc":"BamTools提供了用于处理BAM文件的程序员API和最终用户工具包。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.5.1","env":{"SMP":"bamtools [sub-command] [options]","STANDARD":"bamtools [sub-command] [options]"},"loadCommand":"module add BamTools/2.5.1-GCC-8.3.0"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed49f3c3778cd01280f2ff6","name":"BCFtools","desc":"BCFtools是用于处理BCF和VCF文件的工具的集合。\n","classifications":["LifeSciences","AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.10","env":{"STANDARD":"bcftools"},"loadCommand":"module add BCFtools/1.10.2-GCC-8.3.0"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed49f923778cd3e3456b307","name":"Bedtools","desc":"总体而言，bedtools实用程序是一款非常好用的工具，可用于各种基因组分析任务。 使用最广泛的工具可以进行基因组运算：即在基因组上设定理论\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bedtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.29.2","env":{"SMP":"bedtools [sub-command] [options]","STANDARD":"bedtools [sub-command] [options]"},"loadCommand":"module add BEDTools/2.29.2-GCC-8.3.0"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed49fd43778cd3268c9f1d1","name":"BLAST+","desc":"基本本地比对搜索工具（BLAST）是一种针对速度进行了优化的序列比较算法，用于搜索DNA和蛋白质序列数据库以实现对查询的最佳本地比对。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/BLAST%2B.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.9.0(IntelMPI)","env":{"SMP":"blastx -i <input-file> -o <output-file> <other-options> -num_threads $SLURM_CPUS_ON_NODE","STANDARD":"blastx -i <input-file> -o <output-file> <other-options>"},"loadCommand":"module add BLAST+/2.9.0-iimpi-2019b"},{"additionalDesc":"","version":"2.2.31","env":{"SMP":"blastx -i <input-file> -o <output-file> <other-options> -num_threads $SLURM_CPUS_ON_NODE","STANDARD":"blastx -i <input-file> -o <output-file> <other-options>"},"loadCommand":"module add BLAST+/2.2.31"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4a02e3778cd07709fbb64","name":"Blender","desc":"Blender是一个免费的开源3D动画套件。 它支持整个3D管道建模，装配，动画，模拟，渲染，合成和运动跟踪，甚至包括视频编辑和游戏创建。 高级用户可以使用Blender API的Python脚本来定制应用程序并编写专用工具。 这些通常包含在Blender的未来版本中。 Blender非常适合受益于其统一渠道和响应式开发过程的个人和小型工作室。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Blender.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.81","env":{"SMP":"blender -b <input-file> -t $SLURM_CPUS_ON_NODE -P <batch-script> -o // -F <output-format> -x 1 -a","STANDARD":"blender -b <input-file> -o -P <batch-script> // -F <output-format> -x 1 -a"},"loadCommand":"module add Blender/2.81-foss-2019b-Python-3.7.4"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4a0ec3778cd3ab458a6fc","name":"BOXERMesh","desc":"BOXERMesh是一种高级的，高度自动化的，非结构化的网格划分器，具有强大的支持功能，可以导入基于标准的和流行的特定于供应商的CAD文件。 BOXERMesh可在数分钟内以任意复杂度和大小的几何图形提供数百万个高质量的单元网格。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/BOXERMesh.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.4.0","env":{"MPI":"mpirun-boxerserver -np <mpi-ranks> --hostfile $HOME/machinefile.openmpi boxerserver --script <script-filename> --accept-eula","SMP":"mpirun-boxerserver -np <smp-ranks> boxerserver --script <script-filename> --accept-eula","STANDARD":"boxerserver --script <script-filename> --accept-eula"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4a15a3778cd11447253e5","name":"Burrows-Wheeler Aligner","desc":"BWA是一个软件包，用于将低差异序列映射到大型参考基因组（例如人类基因组）上。 它由三种算法组成：BWA回溯，BWA-SW和BWA-MEM。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Burrows-Wheeler%20%20Aligner.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.7.17","env":{"STANDARD":"bwa <command> <options>"},"loadCommand":"module add BWA/0.7.17-iccifort-2019.5.281"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5edb41333778cd4ec086a409","name":"BWA","desc":"BWA是一个软件包，用于将低差异序列映射到大型参考基因组（例如人类基因组）上。它由三种算法组成：BWA回溯，BWA-SW和BWA-MEM。第一种算法设计用于Illumina序列读取，最高可达100bp，而其余两种用于更长序列，范围从70bp至1Mbp。BWA-MEM和BWA-SW具有类似的功能，例如长读支持和拆分对齐，但是通常建议将BWA-MEM（最新的BWA-MEM）用于高质量查询，因为它更快，更准确。对于70-100bp的Illumina读数，BWA-MEM的性能也优于BWA回溯。","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.7.17(Intel MPI)","env":{},"loadCommand":"module add BWA/0.7.17-iccifort-2019.5.281"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4a3d83778cd29b46a251f","name":"Cadet","desc":"CADET色谱分析和设计工具包\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/cadet.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.1.2(latest MATCH)","env":{"STANDARD":"##MODIFY YOUR JSON \n## under \"CADETPath\" enter: cadet-cli\n##under \"tempDir\" enter: tmp\n## under \"baseDir\" enter RESCALE_CADET_DIR \n## REPLACE JSON NAME ex NSGA3_dextran.json\nsed -i \"s|RESCALE_CADET_DIR|${PWD}|g\" ${PWD}/<JSON NAME>\npython -m CADETMatch --match -j $PWD/<JSON NAME>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4a5bf3778cd4a1cfaac15","name":"Caelus","desc":"Caelus是开源计算连续体力学解决方案的下一步发展。 Caelus是OpenFOAM®的派生产品，但已进行了重组，以建立更强大的基础，以构建您的开源CFD解决方案。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Caelus.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"8.04 CPL(Intel MPI)","env":{"MPI":"caelus tasks -f <yaml-file>","SMP":"caelus tasks -f <yaml-file>","STANDARD":"caelus tasks -f <yaml-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4a8033778cd26bcfb5332","name":"Caffe","desc":"Caffe是一个深度学习框架，考虑了表达，速度和模块化。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.0.0-intel","env":{"STANDARD":"caffe [train|test] --model <model_file> --solver <solver_file>"},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate caffe \n"},{"additionalDesc":"","version":"1.0.0-gpu","env":{"STANDARD":"caffe [train|test] --gpu 0 --model <model_file> --solver <solver_file>"},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate caffe-gpu \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4a8533778cd4ac83e5d5b","name":"Caffe2","desc":"Caffe2是一个轻量级，模块化和可扩展的深度学习框架\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Caffe2.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.8.2,CUDA 9,Python2","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4aa4b3778cd029470dd43","name":"Calculix","desc":"CalculiX是用于解决现场问题的软件包。 使用的方法是有限元方法。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Calculix.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.15","env":{"SMP":"export OMP_NUM_THREADS=<threads>; calculixMT <job-name>","STANDARD":"calculix <job-name>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4ab633778cd366887b699","name":"Calpuff","desc":"CALPUFF是由指数科学家开发的先进的非稳态气象和空气质量建模系统。 它由模型开发人员维护，并由Exponent分发。 该模型已被美国环境保护局（USEPA）在其《空气质量模型指南》中采用，作为评估污染物的长距离迁移及其对联邦I级地区的影响的首选模型，并根据具体情况进行评估。 某些涉及复杂气象条件的近场应用。\n","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.8.5","env":{"SMP":"calpuff <input-file>","STANDARD":"calpuff <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4ac5a3778cd27643de220","name":"CASE Piston","desc":"CASE（用于发动机的汽缸套件分析系统）是一个完整的软件工具，用于对内燃机的环组和活塞进行参数研究。 它用于预测漏气，气缸磨损，能量损失以及许多其他动态和摩擦学现象。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/CASE%20Ring.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.3","env":{"STANDARD":"casepiston.exe <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4ad373778cd03dc6d496b","name":"CASE Ring","desc":"CASE（用于发动机的汽缸套件分析系统）是一个完整的软件工具，用于对内燃机的环组和活塞进行参数研究。 它用于预测漏气，气缸磨损，能量损失以及许多其他动态和摩擦学现象。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/CASE%20Ring.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.3","env":{"STANDARD":"casering53.exe <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4aea23778cd47a011ccfa","name":"Chainer","desc":"Chaineris是一个用于深度学习模型的基于Python的独立开源框架。 Chainer提供灵活，直观和高性能的方式来实现各种深度学习模型，包括最新模型，例如递归神经网络和变分自动编码器。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Chainer.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.0.0 with CUDA 9.2,CUDNN7.1,py3.7","env":{"MPI":"# You're in a basic conda environment. You can conda install or pip install additional packages.","SMP":""},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"626788e5f7a67e0001deb0e0","name":"ChemOffice","desc":"","classifications":["AcademicResearch","Medicine"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=chemoffice.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2021","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1650952421709,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"61b19f2e1a50dd0001f99a4e","name":"CLion","desc":"","classifications":["CustomSoftware"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=clion.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2021.2.3","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1639030574948,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4afe53778cd2ab0502493","name":"Code_Aster","desc":"Code_Aster是用于土木和结构工程，有限元分析和数值模拟的软件包。 它最初由法国EDF公司开发，并作为开源软件发布。 它的本构律，有限元，载荷类型和非线性模拟（材料，大变形和接触）特别丰富。\n","classifications":["HeavyIndustry"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/code.aster.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"14.5.13","env":{"MPI":"source ${MPI_ROOT}/bin/mpivars.sh\nsource /program/code-aster-14.5.13/14.5.13/share/aster/profile.sh\nas_run <input>.export","SMP":"source ${MPI_ROOT}/bin/mpivars.sh\nsource /program/code-aster-14.5.13/14.5.13/share/aster/profile.sh\nas_run <input>.export","STANDARD":"source /program/code-aster-14.5.13/14.5.13/share/aster/profile.sh\nas_run <input>.export"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"626a55f386c70300017a66ed","name":"ColabFold","desc":"快速蛋白结构预测软件，使用 MMseqs2 替代 MSA，能够快速精准预测包含复合体在内的蛋白结构","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=微信图片_20220428165118.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.5.2","env":{},"loadCommand":"module add ColabFold/1.5.2"}],"labels":[],"favoriteTime":0,"createTime":1651135987384,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4b16b3778cd300ca95915","name":"ComWAVE","desc":"ComWAVE能够使用十亿个元素的巨大网格，以高精度模拟各种问题，例如结构中的超声波检查和医疗处理。 分析求解器采用体素有限元方法，并且可以在群集PC上高速执行基于大规模有限元方法的超声传播分析。 此外，它是一款具有划时代意义的超声分析软件，具有从CAD（例如IGES和DXF）导入模型数据的功能，并且可以使用Windows上的GUI以统一的方式执行建模，分析执行和可视化。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/ComWAVE.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"10.0(MPICH3)","env":{"MPI":"./run_job.bat","SMP":"./run_job.bat","STANDARD":"./run_job.bat"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b2553778cd4b84f81c1e","name":"Contam","desc":"CONTAM是一个多区域室内空气质量和通风分析计算机程序。\n","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Contam.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.2.0","env":{"MPI":"contam-x-3.2-gcc.exe","SMP":"contam-x-3.2-gcc.exe","STANDARD":"contam-x-3.2-gcc.exe"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b2c33778cd1864b6fb3b","name":"CONVERGE","desc":"CONVERGE是一种商业计算流体动力学（CFD）程序包，其中装有用于喷雾，湍流（包括LES）和燃烧的物理模型。 由于CONVERGE具有在运行时自动生成高质量正交和适合人体的网格的能力，并且还由于其自适应网格细化的实现方式而广泛用于内燃机设计领域。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/CONVERGE.png","docUrl":"2029/CONVERGE","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.4.30","env":{"MPI":"converge","SMP":"converge","STANDARD":"converge"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b3023778cd4cbc0a5333","name":"CONVERGE 3.0","desc":"CONVERGE是一种商业计算流体动力学（CFD）程序包，其中装有用于喷雾，湍流（包括LES）和燃烧的物理模型。 由于CONVERGE具有在运行时自动生成高质量正交和适合人体的网格的能力，并且还由于其自适应网格细化的实现方式而广泛用于内燃机设计领域。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/CONVERGE.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.0.13","env":{"MPI":"converge","SMP":"converge","STANDARD":"converge"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b3553778cd42e86bdb6b","name":"Cosmotherm","desc":"CosmoThermCOSMOtherm是用于液体的预测性质计算的通用工具，并以独特的方式结合了量子化学和热力学\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/cosmotherm.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2020","env":{"MPI":"cosmotherm <file.inp>","STANDARD":"cosmotherm <file.inp>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b3ab3778cd2754ee332d","name":"CP2K","desc":"CP2K是一个量子化学和固态物理软件包，可以对固态，液态，分子，周期性，材料，晶体和生物系统进行原子模拟。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/CP2K.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"7.1.0","env":{},"loadCommand":"module add CP2K/cp2k-7.1.0"},{"additionalDesc":"","version":"8.2","env":{},"loadCommand":"module add CP2K/8.2-foss-2021a"},{"additionalDesc":"","version":"2023.1","env":{},"loadCommand":"module add CP2K/2023.1-foss-2021a"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed4b42c3778cd4b3cb5adfc","name":"Cradle SC/Tetra","desc":"SCRYU / Tetra是注重可用性的软件，其特定目标是轻松实现复杂几何的计算。 它提供了自动网格生成和基于向导的界面，可引导用户逐步完成设置过程。 除了模拟标准的流体流动和传热应用外，SC / Tetra还可以计算各种现象，例如化学反应，用于移动和/或旋转边界（ALE）的任意拉格朗日欧拉方程，人体温度调节（JOS），自由表面和声学 。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Cradle%20scSTREAM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"14","env":{"MPI":"sct14 -hpc <input-file> $RESCALE_CORES_PER_SLOT","SMP":"sct14 -hpc <input-file> $RESCALE_CORES_PER_SLOT","STANDARD":"sct14 -std <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b4a43778cd06f8add1e2","name":"Cradle scSTREAM","desc":"scSTREAM使用结构化的网格在网格化和计算速度上提供非凡的性能。 对于不需要精确再现精细几何曲率即可预测准确流动结构的应用，其性能可以最大化。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Cradle%20scSTREAM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"20","env":{"MPI":"stsol2020 -hpc <input-file> ${RESCALE_CORES_PER_SLOT}:1","SMP":"stsol2020 -hpc <input-file> ${RESCALE_CORES_PER_SLOT}:1","STANDARD":"stsol2020 -std <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed4b4fb3778cd1254e76ef8","name":"Crunch CFD","desc":"CRUNCH CFD是具有多物理场仿真功能的非结构化网格流求解器。 提供了湍流，空化，燃烧和低温的高级模型。 CRUNCH CFD还提供大量的仿真工具，包括非理想的EOS，网格运动/自适应，混合LES / RANS和详细的化学反应。\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Crunch%20CFD.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.1.0","env":{"MPI":"crunch-cfd","SMP":"crunch-cfd","STANDARD":"crunch-cfd"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5edb435d3778cd382c16c350","name":"cuDNN","desc":"NVIDIA CUDA深度神经网络（cuDNN）是GPU加速的用于深度神经网络的原语库。它提供了DNN应用程序中经常出现的例程的高度优化实现。这些发行说明描述了cuDNN 8.0.0预览版和更早版本的关键功能，软件增强和改进以及已知问题。","classifications":["MachineLearning","ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/cuDNN.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"7.6.4-CUDA10.0","env":{},"loadCommand":"module add cuDNN/7.6.4.38-CUDA-10.0.130"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5ba134e840d4abcb0cd70","name":"Digimat","desc":"Digimat软件套件是一种先进的多尺度材料建模技术，可加快复合材料和结构的开发过程。 Digimat用于在微观水平上进行材料的详细分析，并推导适用于微观和宏观水平多尺度耦合的微观机械材料模型。 Digimat材料模型提供了将处理模拟与结构有限元分析相结合的方法。 这意味着要考虑到加工条件对最终生产零件性能的影响，朝着更具预测性的模拟方向发展。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Digimat.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018.1","env":{"MPI":"# No Setup Required","SMP":"# No Setup Required","STANDARD":"# No Setup Required"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bb514e840d35e847cb98","name":"DualSPHysics","desc":"DualSPHysics基于名为SPHysics（www.sphysics.org）的“平滑粒子流体动力学”模型。 开发该代码是为了研究自由表面的流体现象，在这种情况下可能难以应用欧拉方法，例如波浪或溃坝对近海结构的影响。 DualSPHysics是一组C ++，CUDA和Java代码，旨在处理现实生活中的工程问题。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/DualSPHysics.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.0(GPU)","env":{"SMP":"# Make sure your definition file ends with '_Def.xml'\nexport name=$(find * -maxdepth 0 -name '*xml' -exec basename {} _Def.xml \\;)\nexport dirout=${name}_out\nmkdir ${dirout}\n$gencase ${name}_Def $dirout/$name -save:all\n$dualsphysics $dirout/$name $dirout -svres -gpu"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5ba6a4e840d21605acd76","name":"Dytran","desc":"Dytran是一种显式的有限元分析（FEA）解决方案，用于模拟短期事件（如撞击和碰撞），并分析结构在这些事件中经历的复杂非线性行为。 Dytran使您能够研究设计的结构完整性，以确保最终产品更有机会满足客户安全性，可靠性和法规要求。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Digimat.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2017","env":{"MPI":"dytran jid=<job_id> ncpus=$RESCALE_CORES_PER_SLOT dmp=yes hpmpi=yes bat=no","SMP":"dytran jid=<job_id> ncpus=$RESCALE_CORES_PER_SLOT bat=no","STANDARD":"dytran jid=<job_id> bat=no"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bac84e840d3fd4de20d8","name":"Easy5","desc":"MSC Easy5是高级控制与系统仿真套件，可以精确地模拟控制系统，液压系统（包括热效应），气动，气流，热，电气，机械，制冷，环境控制，润滑或燃料系统以及采样数据/ 离散时间行为。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Adams.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018.0.3","env":{"STANDARD":"easy5x.ksh "},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bbb74e840d27fc5a32fa","name":"EDEM","desc":"EDEM是用于散装物料模拟的高性能离散元方法（DEM）软件。 EDEM仿真和分析使工程师能够解决各种行业中处理和处理散装固体材料的设备的设计，原型设计和优化中的复杂问题。\n","classifications":["HeavyIndustry"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/EDEM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2020.0","env":{"SMP":"edem","STANDARD":"edem"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bc354e840d25f8cc26fe","name":"Elmer","desc":"Elmer是主要由CSC-IT科学中心（CSC）开发的开源多物理场仿真软件。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Elmer.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"8.4","env":{"MPI":"mpirun ElmerSolver","SMP":"mpirun ElmerSolver","STANDARD":"ElmerSolver"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"6819e28084dbfd0001432cb7","name":"ESMFold","desc":"ESMFold 是 Meta AI（Facebook AI Research, FAIR）开发的蛋白质结构预测工具，基于大规模语言模型（LLM, Large Language Model）。","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/jpeg;name=ESMFold.jpg;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.0.0","env":{},"loadCommand":"module add Anaconda3\nsource activate esmfold"}],"labels":[],"favoriteTime":0,"createTime":1746526848261,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5bc904e840d2e94611a26","name":"EXA PowerFlow","desc":"","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/EXA%20PowerFlow.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.4a","env":{"MPI":"powerflow [--discretize|--simulate] <input-file>","SMP":"powerflow [--discretize|--simulate] <input-file>","STANDARD":"powerflow --(discretize,decompose,simulate) <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bcf14e840d36f4958db9","name":"FastQC","desc":"FastQC是用于高通量序列数据的质量控制工具。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FastQC.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.11.9","env":{"SMP":"fastqc --threads $SLURM_CPUS_ON_NODE <fastqc_file1 fastqc_file2 .. fastqc_fileN>","STANDARD":"fastqc <fastqc_file1 fastqc_file2 .. fastqc_fileN>"},"loadCommand":"module add FastQC/0.11.9-Java-11"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5bdbb4e840d2f4cb0a158","name":"FE-SAFE","desc":"FE-SAFE是有限元模型疲劳分析软件的技术领导者，能够满足其用户最苛刻的应用。 它直接与所有领先的FEA套件连接，并被全球运输和移动性，航空航天与国防，一般制造，发电，海洋和海上工业中的领先公司用来确定疲劳寿命并优化设计。 fe-safe以其准确性，速度和易用性而闻名。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Abaqus.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"FE-SAFE 2016 with Abaqus 2016","env":{"SMP":"run-fesafe <macro-file>","STANDARD":"run-fesafe <macro-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5be164e840d42089ce3bf","name":"Feko","desc":"Feko是一款综合的计算电磁（CEM）软件，广泛用于电信，汽车，航空航天和国防工业。 Feko在单个许可证下提供了多个频域和时域EM解算器。 这些方法的混合能够有效分析各种电磁问题，包括天线，微带电路，射频组件和生物医学系统，将天线放置在大型电气结构上，计算散射以及研究电磁兼容性（ EMC）。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Feko.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.3","env":{"MPI":"runfeko <input-file> -np $RESCALE_CORES_PER_SLOT --machines-file $HOME/machinefile","SMP":"runfeko <input-file> -np $RESCALE_CORES_PER_SLOT --machines-file $HOME/machinefile","STANDARD":"runfeko <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5be7b4e840d49dcd3d710","name":"FEMZIP","desc":"FEMZIP工具专用于压缩模拟结果。 有多种版本适合崩溃模拟，NVH和CFD领域中常见的各种数据格式。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FEMZIP.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"FEMZIP-P 8.85","env":{"SMP":"femzip -I <input-file> -O <output-file>","STANDARD":"femzip -I <input-file> -O <output-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bec84e840d053c783acb","name":"FEMZIP-CFD","desc":"FEMZIP-CFD工具专用于压缩模拟结果。 有多种版本适合崩溃模拟，NVH和CFD领域中常见的各种数据格式。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FEMZIP.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"FEMZIP-OPENFOAM 6.1.3009","env":{"SMP":"femzip -I <input-file> -O <output-file>","STANDARD":"femzip -I <input-file> -O <output-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bf2c4e840d1644143dd2","name":"FENICS","desc":"FEniCS是一个流行的开源计算平台，用于求解偏微分方程（PDE）\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FENICS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.1.0","env":{"MPI":"mpirun -n $RESCALE_CORES_PER_SLOT python <input_name>","SMP":"mpirun -n $RESCALE_CORES_PER_SLOT python <input_name>","STANDARD":"python <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bf854e840d2dd0c84484","name":"FFTW","desc":"FFTW是一个C子程序库，用于在一个或多个维度上计算任意输入大小以及实数和复数数据（以及偶数/奇数数据，即离散余弦/正弦变换）的一维或多维离散傅立叶变换（DFT） 或DCT / DST）。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FFTW.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.3.8(Intel)","env":{"SMP":"# No command needed","STANDARD":"# No command needed"},"loadCommand":"module load FFTW/3.3.8-intel-2018b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5f2144e840d3ae09d4eb4","name":"FINE/Open","desc":"FINE / Open是一个用于模拟流体流动的流动集成环境。\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Marine.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"9.2","env":{"MPI":"hexstreamdp_parallel <computation>.run -steering <computation>.steering -niversion open92 -p4pg <computation>.p4pg -fullflex","SMP":"hexstreamdp <computation>.run -steering <computation>.steering -niversion open92","STANDARD":"hexstreamdp <computation>.run -steering <computation>.steering -niversion open92"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f25c4e840d4b2806090a","name":"FINE/Turbo","desc":"FINE / Turbo是一种流量集成环境，用于在内部和外部涡轮机械应用中使用亚音速流向高音速流态的不可压缩和可压缩流体进行旋转和非旋转流动分析。 FINE / Turbo被全球涡轮机械界公认为最快，最准确的CFD套件。 高度可扩展的3D Navier-Stokes流量求解器可以使用非线性谐波（NLH）技术以可承受的成本提供不稳定的流量信息。\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Marine.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"14.2","env":{"MPI":"fine-turbo -p <project-filename> -m <mesh-filename> -c <computation-index>","SMP":"fine-turbo -p <project-filename> -m <mesh-filename> -c <computation-index>","STANDARD":"fine-turbo -p <project-filename> -m <mesh-filename> -c <computation-index>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f2be4e840d1c5cd27baf","name":"Fire Dynamics Simulator(FDS)","desc":"Fire Dynamics Simulator（FDS）是用于低速流动的开源大涡流仿真（LES）代码，重点在于火灾产生的烟雾和热量。\n","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Contam.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"6.7.4","env":{"MPI":"#SBATCH --ntasks-per-node=1  #每节点启动的进程数量 \n#SBATCH --cpus-per-task=4    #每进程使用的CPU核心数, 需同OMP_NUM_THREADS保持一致 \nulimit -s unlimited \nexport OMP_NUM_THREADS=4  #每进程使用的CPU核心数,最大值为一个节点的所有核心数量 \nexport I_MPI_PIN_DOMAIN=omp #如OMP_NUM_THREADS=1, 则需要使用#注释此行 \nsrun -n <总进程数> fds <input.fds> \n ","SMP":"#SBATCH --ntasks-per-node=1  #每节点启动的进程数量 \n#SBATCH --cpus-per-task=4    #每进程使用的CPU核心数, 应设为节点核心数-1, 需同OMP_NUM_THREADS保持一致 \nulimit -s unlimited \nexport OMP_NUM_THREADS=4  #每进程使用的CPU核心数, 应设为节点核心数-1,  \nexport I_MPI_PIN_DOMAIN=omp \nsrun -n 1 fds <input.fds> \n","STANDARD":"fds <input.fds>"},"loadCommand":"module add fds/FDS6 \n "}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"6246a6909d7f8f000116a5aa","name":"FLACS","desc":"","classifications":[],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=FLACS.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"9.0","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1648797328824,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f3df4e840d4da4316a92","name":"Flux","desc":"Flux抓住了机电设备的复杂性，可以精确地优化其性能，效率，尺寸，成本或重量，从而为最终用户带来更好的创新和价值产品。 助焊剂模拟电磁静态，稳态和瞬态条件，以及电学和热学性质。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Feko.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.1.1(Intel MPI)","env":{"MPI":"# Select Application Type. The Options are Flux2D, Flux3D, FluxSkewed and FluxPEEC \n$FLUX_EXE -application <your-appication-choice> -batch -runPyInSilentModeAndExit <Input_Python_Script>","SMP":"# Select Application Type. The Options are Flux2D, Flux3D, FluxSkewed and FluxPEEC \n$FLUX_EXE -application <your-appication-choice> -batch -runPyInSilentModeAndExit <Input_Python_Script>","STANDARD":"# Select Application Type. The Options are Flux2D, Flux3D, FluxSkewed and FluxPEEC \n$FLUX_EXE -application <your-appication-choice> -batch -runPyInSilentModeAndExit <Input_Python_Script>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f4424e840d3eb893aaa4","name":"Foam-extend","desc":"泡沫扩展项目是用于计算流体动力学（CFD）的OpenFOAM开源库的分支。OpenFOAM是一个C ++工具箱，用于开发自定义数值求解器以及用于解决连续力学问题（包括计算流体动力学（CFD））的预处理/后处理实用程序。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/foam-extend.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.1","env":{"MPI":"srun -n $SLURM_NTASKS foamExec <module> <input-file> -parallel","SMP":"srun -n $SLURM_NTASKS foamExec <module> <input-file> -parallel","STANDARD":"foamExec <module> <input-file>"},"loadCommand":"module add OpenFOAM-Extend/4.1-20191120-intel-2019b-Python-2.7.16"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5f6ce4e840d34b440c734","name":"FreeFem++","desc":"FreeFem ++是偏微分方程求解器。 它有自己的语言。 freefem脚本可以解决2D和3D中的多物理场非线性系统。 涉及多个物理分支的PDE（2d，3d）问题，例如流体-结构相互作用，要求在多个网格上进行数据插值，并需要在一个程序中对其进行操作。 FreeFem ++包括一种基于2 ^ d树的快速插值算法和一种用于在多个网格上处理数据的语言。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/FreeFem%2B%2B.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.61-avx512(GCC)","env":{"MPI":"mpirun -np <NBPROC> FreeFem++-mpi <edp-file>","SMP":"mpirun -np <NBPROC> FreeFem++-mpi <edp-file>","STANDARD":"FreeFem++ <edp-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f7c84e840d1b9046b65a","name":"GAMESS (US)","desc":"通用原子和分子电子结构系统（GAMESS）是通用的从头开始的分子量子化学软件包。 GAMESS（美国）可以执行许多常规计算化学计算，包括Hartree-Fock，密度泛函理论（DFT），广义价键（GVB）和多构型自洽场（MCSCF）。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GAMESS%20%28US%29.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018.08.02 R2","env":{"MPI":"rungms <input-filename> <mpi-ranks>","SMP":"rungms <input-filename> <smp-ranks>","STANDARD":"rungms <input filename> 1"},"loadCommand":"module load gamess/gamess"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5f8204e840d1344300657","name":"GATK","desc":"GATK工具包提供了多种工具，主要侧重于变体发现和基因分型。 它具有强大的处理引擎和高性能的计算功能。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GATK.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.1.5","env":{"STANDARD":"java -jar $GATK -T <analysis_type> -R <reference_sequence> -I <input_file>"},"loadCommand":"module add GATK/4.1.5.0-GCCcore-9.3.0-Java-11"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5f8814e840d4e58360f76","name":"GCC","desc":"GNU编译器集合包括C，C ++，Objective-C，Fortran，Ada，Go和D的前端，以及这些语言的库（libstdc ++，...）。 GCC最初是作为GNU操作系统的编译器编写的。 GNU系统开发为100％免费软件，在尊重用户自由的意义上说是免费的。\n","classifications":["Aerospace","AutoMotive","AcademicResearch","Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GCC.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"9.3.0","env":{"STANDARD":"# Your build script"},"loadCommand":"module add GCC/9.3.0"},{"additionalDesc":"","version":"8.3.0","env":{"STANDARD":"# Your build script"},"loadCommand":"module add GCC/8.3.0"},{"additionalDesc":"","version":"7.3.0","env":{"STANDARD":"# Your build script"},"loadCommand":"module add GCC/7.3.0-2.30"},{"additionalDesc":"","version":"5.4.0","env":{"STANDARD":"# Your build script"},"loadCommand":"module add GCC/5.4.0-2.26"},{"additionalDesc":"","version":"4.9.3","env":{"STANDARD":"# Your build script"},"loadCommand":"module add GCC/4.9.3-2.25"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5f98b4e840d1ecc9b757a","name":"Gerris","desc":"Gerris是用C编写的开源计算流体动力学（CFD）程序包，用于解决二维或3-维不可压缩的Navier-Stokes方程。 Gerris的实现和多相流（包括表面张力）的精确公式化是该软件与昆虫同名的原因。\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Gerris.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"12.06.13-2D","env":{"MPI":"mpirun -np <mpi-ranks> gerris2D <input filename>","SMP":"mpirun -np <smp-ranks> gerris2D <input filename>","STANDARD":"gerris2D <input filename>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed86afd4e840d4b64a7fcfb","name":"Go","desc":"Google开发的一种静态强类型、编译型、并发型，并具有垃圾回收功能的编程语言。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GO.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5fa514e840d453cd78a70","name":"GPAW","desc":"GPAW：DFT以及投影仪增强波方法之外的功能。 GPAW是基于投影机增强波（PAW）方法和原子模拟环境（ASE）的密度泛函理论（DFT）Python代码。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GPAW.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.1.0","env":{"MPI":"mpirun -np <mpi-ranks> gpaw-python <input-file>","SMP":"mpirun -np <mpi-ranks> gpaw-python <input-file>","STANDARD":"gpaw-python"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5faa34e840d35ac23e3dd","name":"GROMACS","desc":"GROMACS（用于化学模拟的格罗宁根机器）是一种分子动力学软件包，主要用于模拟蛋白质，脂质和核酸。 GROMACS是可用的最快和最受欢迎的分子动力学软件包之一，可以在CPU和GPU上运行。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GROMACS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2021.5-foss-2021b-PLUMED-2.8.0","env":{},"loadCommand":"module add GROMACS/2021.5-foss-2021b-PLUMED-2.8.0"},{"additionalDesc":"","version":"2021.5-foss-2021b-CUDA-11.4.1-PLUMED-2.8.0","env":{},"loadCommand":"module add GROMACS/2021.5-foss-2021b-CUDA-11.4.1-PLUMED-2.8.0"},{"additionalDesc":"","version":"2024.3-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2","env":{},"loadCommand":"module add GROMACS/2024.3-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2"},{"additionalDesc":"","version":"2023-foss-2021b-CUDA-11.4.1","env":{},"loadCommand":"module add GROMACS/2023-foss-2021b-CUDA-11.4.1"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5fafb4e840d32549f90a9","name":"GT-SUITE","desc":"GT-SUITE是Gamma Technologies（GTI）的产品，Gamma Technologies（GTI）是一家专门致力于引擎和车辆行业的专业软件公司。 GT-SUITE用一个软件工具即可处理多种车辆和发动机技术应用。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/GT-SUITE.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018.3","env":{"MPI":"gtsuite -m:off [-t:<No of cores>] <input-file>","STANDARD":"gtsuite -m:off [-t:<No of cores>] <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"635a0fffcbcff40001607e18","name":"HelixFold","desc":"HelixFold 以提高训练和推理速度并减少内存消耗，通过算子融合、张量融合和混合并行计算提高了性能，而内存通过重新计算、BFloat16 和内存读/写优化，与原来的 AlphaFold2（用 Jax 实现）和 OpenFold（用 PyTorch 实现）相比，HelixFold 只需 7.5 天即可完成完整的端到端训练，使用混合并行只需 5.3 天，而 AlphaFold2 和 OpenFold 都需要大约 11 天，HelixFold 节省了 1 倍的训练时间，在 CASP14 和 CAMEO 数据集上验证了 HelixFold 的准确性可以与 AlphaFold2 相当。","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/jpeg;name=HelixFold.jpg;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"dev","env":{},"loadCommand":"module add Anaconda3\nsource activate helixfold\nexport LD_LIBRARY_PATH=/public/software/.local/easybuild/software/Anaconda3/2020.02/envs/helixfold/lib"}],"labels":[],"favoriteTime":0,"createTime":1666846719545,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"62f0dd6454d1a9000146e135","name":"HelixFold-Single","desc":"HelixFold-Single从近3亿的无标注蛋白质数据中提取信息，建模蛋白质之间的关系，从而将MSA同源信息隐式的学习在预训练大模型中，进而有效地替代MSA信息检索模块，极大地提升了结构预测的速度，模型推理的速度平均提升数百倍。","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/jpeg;name=微信图片_20220810151943.jpg;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"dev","env":{},"loadCommand":"module add Anaconda3/2020.02\nsource activate helixfold-single\nPROGRAM_DIR=/public/software/.local/easybuild/software/helixfold-single\nexport LD_LIBRARY_PATH=/public/software/.local/easybuild/software/Anaconda3/2020.02/envs/helixfold-single/lib"}],"labels":[],"favoriteTime":0,"createTime":1659952484080,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5fb5f4e840d4b8010fd94","name":"HELYX","desc":"HELYX®是基于ENGYS自己的增强版OpenFOAM库的全面的通用计算流体动力学软件解决方案，用于工程分析和优化.ENGYS从事FOAM和OpenFOAM的人员可以追溯到1999年，在开发方面提供了无与伦比的知识和专业知识 ，该工具在工业上的集成和应用。 我们利用这一经验，提供结合了两全其美的独特产品：商用工具的成熟功能，支持和可靠性，以及经济高效，可扩展的开源解决方案的固有优势。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/HELYX.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.5.0(Intel MPI)","env":{"MPI":"<your openfoam command>","SMP":"<your openfoam command>","STANDARD":"<your openfoam command>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5fbdc4e840d35a86283b8","name":"HMMER","desc":"HMMER用于在序列数据库中搜索蛋白质序列的同源物，以及进行蛋白质序列比对。 它使用称为轮廓隐藏Markov模型（轮廓HMM）的概率模型来实现方法。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/HMMER.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.2.1-intel","env":{"MPI":"srun <hmmer command>","SMP":"srun <hmmer command>","STANDARD":"<hmmer command>"},"loadCommand":"module add HMMER/3.2.1-iimpi-2019b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed86c3b4e840d4f24a21d41","name":"Htslib","desc":"C库的PacBio fork用于读取/写入高通量测序数据。该软件包包括实用程序bgzip和tabix\n","classifications":[],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/htslib.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.10.2","env":{},"loadCommand":"module add HTSlib/1.10.2-iccifort-2019.5.281"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5ffce4e840d27f87e44d3","name":"Intel Parallel Studio\n","desc":"上传MPI源代码，并使用Intel MPI编译器进行编译。\n","classifications":["AcademicResearch","CustomSoftware"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bring%20Your%20Own%20Intel%20%20%20MPI%20Software.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2020a","env":{"MPI":"icc","SMP":"icc","STANDARD":"icc"},"loadCommand":"module add intel/2020a"},{"additionalDesc":"","version":"2019b","env":{"MPI":"icc","SMP":"icc","STANDARD":"icc"},"loadCommand":"module add intel/2019b"},{"additionalDesc":"","version":"2018b","env":{"MPI":"icc","SMP":"icc","STANDARD":"icc"},"loadCommand":"module add intel/2018b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed86cab4e840d55a438ad1f","name":"java","desc":"Java是一种广泛使用的计算机编程语言，拥有跨平台、面向对象、泛型编程的特性，广泛应用于企业级Web应用开发和移动应用开发。\n","classifications":[],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/java.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"11.0.2","env":{},"loadCommand":"module add Java/11.0.2"},{"additionalDesc":"","version":"1.8.0_241","env":{},"loadCommand":"module add Java/1.8.0_241"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed600e14e840d5030649d32","name":"JOCTA","desc":"J-OCTA是用于材料研发的集成仿真系统。它是支持多种材料（例如橡胶，塑料，薄膜，涂层和电解质）开发过程中从原子级到微米级的机理理解和材料性能评估的软件。它可以用作知识发现工具，以了解仅凭实验结果无法完全掌握的复杂特性和现象。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/JOCTA.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.1 OPEN MPI","env":{"MPI":"mpirun -machinefile $HOME/hosts -np <rank> vsop -C -Dx,y,z -F <input-filename.ctl> -p <input-filename.pot> -b <input-file.top> <input-file.par> <output-file.out>","SMP":"mpirun -machinefile $HOME/hosts -np <rank> vsop -C -Dx,y,z -F <input-filename.ctl> -p <input-filename.pot> -b <input-file.top> <input-file.par> <output-file.out>","STANDARD":"mpirun -machinefile $HOME/hosts -np <rank> vsop -C -Dx,y,z -F <input-filename.ctl> -p <input-filename.pot> -b <input-file.top> <input-file.par> <output-file.out>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed6017d4e840d26c025c8b2","name":"Julia","desc":"Julia是用于技术计算的高级，高性能动态编程语言，其语法为其他技术计算环境的用户所熟悉。它提供了复杂的编译器，分布式并行执行，数值精度和广泛的数学函数库。\n","classifications":["ArtificialIntelligence","AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Julia.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.4.1","env":{"MPI":"srun -n $SLURM_NTASKS julia <input-filename>","SMP":"julia -p $SLURM_CPUS_ON_NODE <input-filename>","STANDARD":"julia <input-filename>"},"loadCommand":"module add Julia/1.4.1-linux-x86_64"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"60b992a9be44e382f82f0ce6","name":"Jupyter Notebook","desc":"Jupyter Notebook是基于网页的用于交互计算的应用程序。其可被应用于全过程计算：开发、文档编写、运行代码和展示结果。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/JuypterNotebook.png","docUrl":"2029/jupyter-notebook","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"6.1.1","env":{"STANDARD":""},"loadCommand":"module add Anaconda3/2020.02;source activate;conda activate rdkit;jupyter notebook"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"6278f078611342000136842e","name":"JupyterLab","desc":"jupyterlab包含了jupyter notebook的所有功能，并升级增加了很多功能。其支持python、R、java等多种编程语言及markdown、letex等写作语言及公式输入，可以集编程与写作于一身，非常适合于代码学习，笔记记录、演示及教学等  。","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=879fd3773d90b90207c8db68900285c.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.4.0","env":{},"loadCommand":"#!/bin/bash\nmodule add Anaconda3\nsource activate jupyterlab\njupyter-lab\n"}],"labels":[],"favoriteTime":0,"createTime":1652093048374,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed602044e840d511097cb2a","name":"Keras","desc":"Keras Keras是用Python编写的高级神经网络API，能够在TensorFlow，CNTK或Theano之上运行。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Keras.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"keras-2.2.4(tensorflow-1.15.3) ","env":{"MPI":"# This is a conda environment with keras-gpu installed.\n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate keras-2.2.4  \n"},{"additionalDesc":"","version":"keras-2.2.4-gpu(tensorflow-1.15.3-cuda10) ","env":{"MPI":"# This is a conda environment with keras-gpu installed.\n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate keras-gpu-2.2.4  \n"},{"additionalDesc":"","version":"keras-2.3.1(tensorflow-2.2.0) ","env":{"MPI":"# This is a conda environment with keras-gpu installed.\n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate keras-2.3.1  \n"},{"additionalDesc":"","version":"keras-2.3.1-gpu(tensorflow-2.2.0-cuda10) ","env":{"MPI":"# This is a conda environment with keras-gpu installed.\n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate keras-gpu-2.3.1  \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed73ebb4e840d28f0e9054a","name":"LAMMPS","desc":"LAMMPS（大型原子/分子Massivley并行模拟器）是由Sandia National Laboratories用C ++编写的开源分子动力学模拟器，是为并行机设计的。它可以使用各种力场和边界条件对原子聚合，生物，金属或中尺度系统进行建模，并且易于扩展。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/LAMMPS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3Mar2020-kokkos(openmpi)","env":{},"loadCommand":"module add LAMMPS/3Mar2020-foss-2020a-Python-3.8.2-kokkos"},{"additionalDesc":"","version":"3Mar2020-kokkos(intelmpi)","env":{},"loadCommand":"module add LAMMPS/3Mar2020-intel-2019b-Python-3.7.4-kokkos"},{"additionalDesc":"","version":"23Jun2022-kokkos(openmpi)","env":{},"loadCommand":"module add   LAMMPS/23Jun2022-foss-2021a-kokkos\n"},{"additionalDesc":"","version":"23Jun2022-foss-2021a-kokkos-CUDA-11.3.1","env":{},"loadCommand":"module add LAMMPS/23Jun2022-foss-2021a-kokkos-CUDA-11.3.1"},{"additionalDesc":"","version":"23Jun2022-foss-2021b-kokkos-CUDA-11.4.1","env":{},"loadCommand":"module add LAMMPS/23Jun2022-foss-2021b-kokkos-CUDA-11.4.1"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed7416f4e840d0b70d2ef2c","name":"LIGGGHTS","desc":"LIGGGHTS基于经典分子动力学模拟代码LAMMPS，并针对一般的颗粒和颗粒传热模拟进行了改进\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.8.0","env":{"MPI":"mpirun lmp_mpi < <input-file>","SMP":"mpirun lmp_mpi < <input-file>","STANDARD":"lmp_mpi < <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed743db4e840d3f242e6542","name":"M-Star CFD","desc":"M-Star CFD是一种建模和仿真工具，可提供对复杂流体流动的高度准确和详细的见解。该工具旨在满足工程专业人士和高级研究人员的需求。\n","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/M-Star%20CFD.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.7.59","env":{"MPI":"source setup_mstar \nexport mstar_LICENSE=$HOME/work/shared/<license-file>.lic\nmpirun -np $RESCALE_CORES_PER_SLOT --prefix ${MPI_ROOT} mstar-cfd -i <input.xml> -o out","SMP":"source setup_mstar \nexport mstar_LICENSE=$HOME/work/shared/<license-file>.lic\nmpirun -np $RESCALE_CORES_PER_SLOT --prefix ${MPI_ROOT} mstar-cfd -i <input.xml> -o out","STANDARD":"source setup_mstar \nexport mstar_LICENSE=$HOME/work/shared/<license-file>.lic\nmstar-cfd -i <input.xml> -o out"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed744ed4e840d27148833fa","name":"MADYMO","desc":"MADYMO（数学动态模型）是用于分析和优化乘员安全设计的全球标准软件。它的准确性，效率和多功能性无可匹敌。使用MADYMO，研究人员和工程师可以在开发过程的早期对安全设计进行建模，彻底分析和优化。这减少了构建和测试原型所涉及的费用和时间。采用MADYMO还可以最大程度降低在开发后期进行设计更改的风险。对于新的或改进的车辆模型和部件，MADYMO可以降低成本并大大缩短产品上市时间。","classifications":["PetroleumExploration","AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/MADYMO.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"7.7","env":{"MPI":"madymo77 -mpp -nrproc <mpi ranks> -i <input file>","SMP":"madymo77 -nrproc <mpi ranks> -i <input file>","STANDARD":"madymo77 -i <input file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed745944e840d53686bc12a","name":"Marc","desc":"Marc是通用的有限元（FE）程序，设计用于结构，热，电和磁场问题的线性和非线性分析。此外，它还可以处理热力学，电热和磁动力耦合分析。对于非线性和瞬态问题，Marc通过提供自动负载增量和时间步长功能使您的分析更加轻松。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Adams.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2014.2","env":{"MPI":"run_marc -j <input-filename> -nps $RESCALE_CORES_PER_SLOT -host $MARC_HOSTFILE -b no -v no","SMP":"run_marc -j <input-filename> -nts <smp-ranks> -b no -v no","STANDARD":"run_marc -j <input-filename> -b no -v no"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed74da34e840d22904cdf92","name":"MEEP","desc":"MIT电磁方程传播（MEEP）代码是由MIT开发的免费有限时域（FDTD）仿真软件包，用于与MIT光子带本征模式软件包一起对电磁系统进行建模。\n","classifications":["Aerospace","PetroleumExploration","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/MEEP.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"parallel-meep","env":{},"loadCommand":"module add Anaconda3\nsource activate parallel-meep"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"6214b2fe60d54a00016e934e","name":"MEGA","desc":"分子进化遗传学分析（MEGA）软件已经成熟，可以包含大量计算分子进化的方法和工具。","classifications":[],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=e0a03f2407d190fb4eaa1ab3c4524ea.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"11.0.11","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1645523710414,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed7544d4e840d3af0cc5b9a","name":"Mental Ray®","desc":"NVIDIA的MentalRay®是功能丰富的高性能3D渲染软件，可基于先进的光线跟踪技术创建具有卓越品质和无与伦比的真实感的图像。高度优化的并行渲染引擎可确保在多核和多处理器机器上以及跨机器的分布式网络上实现最佳可伸缩性，以实现最佳整体性能。\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Mental%20Ray%C2%AE.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.12.2.9","env":{"SMP":"ray <mi filename>","STANDARD":"ray <mi filename>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed756e54e840d0d5079c407","name":"Miniconda","desc":"Miniconda具有最少依赖关系的conda环境。您可以conda或pip安装所需的内容。\n","classifications":["HeavyIndustry","Aerospace","AutoMotive","ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Miniconda.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.7.12.1","env":{"MPI":"# This is a conda environment. \n# You can conda-install or pip-install more packages.","SMP":"# This is a conda environment. \n# You can conda-install or pip-install more packages.","STANDARD":"# This is a conda environment. \n# You can conda-install or pip-install more packages."},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed7581c4e840d4da4fe1868","name":"MixIT Server","desc":"MixIT是下一代协作混合分析和放大工具，旨在利用实验室和工厂数据，经验相关性和先进的3D CFD模型促进全面的搅拌釜分析。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/MixIT%20Server.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.0.26","env":{"SMP":"ssh -C localhost \"Xvfb $DISPLAY -screen 0 1920x1200x24 > /dev/null 2>&1 &\"\nstartServer","STANDARD":"ssh -C localhost \"Xvfb $DISPLAY -screen 0 1920x1200x24 > /dev/null 2>&1 &\"\nstartServer"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed759034e840d0764e64699","name":"Moldflow","desc":"设计师，工程师和分析师使用Moldflow塑料注射成型仿真软件来改善塑料零件设计，注射模具设计和制造工艺\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Moldflow.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2017","env":{"MPI":"runstudy <study>","SMP":"runstudy <study>","STANDARD":"runstudy <study>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed759584e840d2c8c21f163","name":"Mozilla Firefox","desc":"Mozilla Firefox是由Mozilla Foundation及其子公司Mozilla Corporation开发的免费的开源Web浏览器。\n","classifications":["AcademicResearch","CustomSoftware"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Mozilla%20Firefox.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"Latest","env":{},"loadCommand":"module load Firefox/44.0.2"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed75a224e840d4a6063ce14","name":"MrBayes","desc":"MrBayes是一个程序，用于跨各种系统发育和进化模型的贝叶斯推理和模型选择。 MrBayes使用Markov链蒙特卡罗（MCMC）方法来估计模型参数的后验分布。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/MrBayes.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.2.6(OpenMPI)","env":{"MPI":"mpirun -np $RESCALE_CORES_PER_SLOT mb <input file>","STANDARD":"mb"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed75ab74e840d53205da80d","name":"MSC Nastran","desc":"MSC Nastran（NASA结构分析）是原始的商业Nastran有限元分析产品，并且继续发展并扩展其功能，包括动力学转子动力学，非线性，热，高冲击，流体-结构相互作用和疲劳分析。\n","classifications":["Aerospace","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Adams.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019","env":{"MPI":"msc20190 nastran <input-file> dmparallel=$RESCALE_CORES_PER_SLOT bat=no sdir=/tmp","SMP":"msc20190 nastran <input-file> parallel=$RESCALE_CORES_PER_NODE bat=no sdir=/tmp","STANDARD":"msc20190 nastran <input-file> bat=no sdir=/tmp"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed75b7a4e840d333c836caa","name":"Mxnet","desc":"Mxnet是用于深度学习的灵活高效的库\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Mxnet.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.5.0","env":{},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate mxnet  \n"},{"additionalDesc":"","version":"1.5.1(cuda10.1)","env":{},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate mxnet-gpu  \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed75bfe4e840d340c4d9e66","name":"NAMD","desc":"NAMD是并行分子动力学代码，旨在对大型生物分子系统进行高性能仿真。基于Charm ++并行对象，对于典型的仿真，NAMD可以扩展到数百个内核，对于最大的仿真，可以扩展到200,000个内核。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/NAMD.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.13(Intel MPI)","env":{},"loadCommand":"module add NAMD/2.13-intel-2018b-mpi"},{"additionalDesc":"","version":"2.14(GPU)","env":{},"loadCommand":"module add NAMD/2.14-fosscuda-2021b"},{"additionalDesc":"","version":"2.14-foss-2020a-mpi","env":{},"loadCommand":"module add NAMD/2.14-foss-2020a-mpi"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed75c9b4e840d5110975138","name":"Network Simulator","desc":"网络模拟器是离散事件网络模拟器\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Network%20Simulator.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.26(OPEN MPI)","env":{"MPI":"mpirun -np <mpi-ranks> mb <input file>","STANDARD":"waf"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed75dd64e840d4aacb09edf","name":"NEURON","desc":"NEURON是用于为单个神经元和神经元网络建模的仿真环境。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"iv-19,nrn-7.5","env":{"MPI":"mpirun -np $RESCALE_CORES_PER_SLOT -machinefile $HOME/machinefile python <script.py> --filename=<input-file>","SMP":"mpirun -np $RESCALE_CORES_PER_SLOT -machinefile $HOME/machinefile python <script.py> --filename=<input-file>","STANDARD":"mpirun -np $RESCALE_CORES_PER_SLOT -machinefile $HOME/machinefile python <script.py> --filename=<input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed764754e840d33182c442e","name":"Nexus Suite Reservoir Simulation","desc":"Landmark的Nexus Suite水库模拟为水库工程师提供了从头到尾评估，验证，规划和执行资产开发优化所需的集成建模功能。借助更快的运行时间和多储层建模功能，Nexus工具可提高模型准确性和资产团队的生产率，从而产生集成解决方案，有助于提高对关键决策和生命周期管理策略的信心，以实现最佳资产开发。\n","classifications":["PetroleumExploration"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Nexus%20Suite%20Reservoir%20%20%20Simulation.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5000.4.10.1","env":{"MPI":"standaloneEM64_5000_4_10_1.exe <input-file> -c <case> -s <study>;\nmpirun -np $RESCALE_CORES_PER_SLOT nexusEM64_5000_4_10_1.exe -s <study> -c <case>","SMP":"standaloneEM64_5000_4_10_1.exe <input-file> -c <case> -s <study>;\nmpirun -np $RESCALE_CORES_PER_SLOT nexusEM64_5000_4_10_1.exe -s <study> -c <case>","STANDARD":"standaloneEM64_5000_4_10_1.exe <input-file> -c <case> -s <study>;\nnexusEM64_5000_4_10_1.exe -s <study> -c <case>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5edb43fe3778cd26102cdad4","name":"NIST","desc":"NIST测量支持从最小的技术到最大，最复杂的人工创造物，从如此微小的纳米级设备到数以万计的人发丝就可以安装到抗震的摩天大楼和全球通讯网络中。","classifications":["Semiconductor"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/nist.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"FDS6.7.4-0-  SMV6","env":{},"loadCommand":"module add FDS6.7.4-0-  SMV6"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed764e04e840d196c846277","name":"NLopt","desc":"NLopt是一个用于非线性优化的开源库，除了提供各种其他算法的几种原始实现之外，它还为各种免费的优化例程提供了通用接口。 NLopt具有针对多个编程环境的绑定，包括C，C ++，Fortran，Matlab，Octave，Python和R等。\n","classifications":["PetroleumExploration"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/NLopt.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.6.1","env":{"MPI":"cc <source-filename> -o <object-filenames> -lnlopt -lm && srun -n $SLURM_NTASKS <executable-filename>","SMP":"cc <source-filename> -o <object-filenames> -lnlopt -lm && srun -n $SLURM_NTASKS <executable-filename>","STANDARD":"cc <source-filename> -o <object-filenames> -lnlopt -lm && <executable-filename>"},"loadCommand":"module add NLopt/2.6.1-GCCcore-9.3.0 \n module add OpenMPI/4.0.3-GCC-9.3.0\n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed765654e840d434844f7c9","name":"Nolearn","desc":"nolearn是一个机器学习库，在其他神经网络库的顶部提供了类似于scikit-learn的界面。该软件包中还包括Theano兼容的Lasagne神经网络库。 Theano提供了底层GPU支持。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Nolearn.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.7","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed765cc4e840d51a07d7a24","name":"NVIDIA CUDA","desc":"上传并运行您自己的CUDA应用程序作为输入文件\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/NVIDIA%20CUDA.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"10.0(cuDNN 7.6)","env":{"MPI":"e.g. nvcc main.c; ./a.out","STANDARD":"e.g. nvcc main.c; ./a.out"},"loadCommand":"module add cuDNN/7.6.4.38-CUDA-10.0.130"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed7664d4e840d2e94437df9","name":"NWChem","desc":"NWChem是一个从头算计算化学软件包，具有对量子化学和分子动力学功能的内置支持。 NWChem由太平洋西北国家实验室（PNNL）的Molecular Sciences软件小组开发和维护，旨在在有效处理大问题的能力以及对可用并行计算资源的使用方面实现可扩展。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/NWChem.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"7.0.0(Intel MPI)/UCX 1.6.0","env":{"MPI":"srun nwchem <input-filename>","SMP":"srun nwchem <input-filename>","STANDARD":"nwchem <input-filename>"},"loadCommand":"module add NWChem/7.0.0-intel-2019b-Python-3.7.4"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed766a64e840d130c861734","name":"Octave","desc":"八度GNU八度是一种高级语言，主要用于数值计算\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/octave.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"octave-5.1.0","env":{"STANDARD":"place holder"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed76c2d4e840d4e4c1f78ed","name":"OpenFOAM","desc":"OpenFOAM是领先的免费CFD开源软件，由OpenFOAM基金会拥有，并根据通用公共许可证（GPL）独家发行。 GPL使用户可以在许可条款内自由地修改和重新分发软件，并保证可以继续免费使用\n","classifications":["Aerospace","AcademicResearch","AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenFOAM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"V1912(Intel MPI)","env":{},"loadCommand":"module add OpenFOAM/v1912-intel-2019b"},{"additionalDesc":"","version":"v2212-foss-2023a","env":{},"loadCommand":" module add OpenFOAM/v2212-foss-2023a"},{"additionalDesc":"","version":"v2406-foss-2023a","env":{},"loadCommand":"module add OpenFOAM/v2406-foss-2023a"},{"additionalDesc":"","version":"v2206-foss-2022a","env":{},"loadCommand":"module  add OpenFOAM/v2206-foss-2022a"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed76cf74e840d1640a52aa8","name":"OpenFOAM+","desc":"OpenFOAM是自2004年以来主要由OpenCFD Ltd发布和开发的免费，开源CFD软件。它在商业和学术组织的大多数工程和科学领域拥有庞大的用户群。 OpenFOAM具有广泛的功能，可以解决从涉及化学反应，湍流和热传递的复杂流体流到声学，固体力学和电磁学等各种问题。\n","classifications":["AutoMotive","Aerospace","AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenFOAM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"V1912+(Intel MPI)","env":{"MPI":"srun -n $SLURM_NTASKS <mpi-ranks> foamExec <module> <input-file> -parallel","SMP":"srun -n $SLURM_NTASKS foamExec <module> <input-file> -parallel","STANDARD":"foamExec <module> <input-file>"},"loadCommand":"module add OpenFOAM/v1912-intel-2019b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed76e1d4e840d54043f84de","name":"OpenMC","desc":"OpenMC是专注于中子临界度计算的蒙特卡洛粒子传输模拟代码。它能够基于具有二阶曲面的构造实体几何来模拟3D模型。粒子相互作用数据基于ACE格式的横截面，也用于MCNP和Serpent Monte Carlo的代码中。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpanMC.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.6.0","env":{"MPI":"mpirun -f $HOME/mpd.hosts -n <mpi-ranks> openmc --threads <smp-ranks>  $PWD","SMP":"openmc --threads <smp-ranks> $PWD","STANDARD":"openmc $PWD"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed5bf854e840d2dd0c84458","name":"OpenMM","desc":"Openmm用于分子模拟的高性能工具包。将其用作应用程序、库或灵活的编程环境。\n","classifications":["DynamicsSimulation"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenMM.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"Openmm","env":{"SMP":"# No command needed","STANDARD":"# No command needed"},"loadCommand":"/public/software/.local/easybuild/software/Anaconda3/2020.02/envs/rdkit/bin/openmm-setup"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed76f144e840d4a54983469","name":"OpenMOC","desc":"OpenMOC是用于反应堆物理临界计算的特征方法中性粒子传输代码。它能够基于具有二阶曲面的构造实体几何来模拟2D模型。作为OpenMOC项目的一部分，积极追求针对多核，多核和GPU的高性能并行求解器。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenMOC.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.1.2(GPU)","env":{"SMP":"python your-script.py","STANDARD":"python your-script.py"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed7717f4e840d05a8182447","name":"OpenMx","desc":"OpenMx是用于基于密度泛函理论（DFT），守恒范式伪势和伪原子局部基函数的纳米级材料模拟的软件包。 OpenMX中使用的方法和算法及其实现都经过精心设计，可在基于MPI或MPI / OpenMP混合并行性的并行计算机上实现大规模的从头算起的电子结构计算。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenMx.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.8.5","env":{"MPI":"#Bring your own DFT_DATA directory and specify DATA.PATH in your input file\nmpirun -np <mpi-ranks> openmx <input>","SMP":"#Bring your own DFT_DATA directory and specify DATA.PATH in your input file\nmpirun -np <smp-ranks> openmx <input>","STANDARD":"#Bring your own DFT_DATA directory and specify DATA.PATH in your input file\nopenmx <input>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed771e14e840d5648a2943a","name":"OPTIS SPEOS","desc":"OPTIS SPEOS是一种灯光和人类视觉仿真软件。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018.0.0.256","env":{"MPI":"mpiSPEOS.sh -sv5 <input-file>","SMP":"runSPEOS.sh -sv5 <input_file>","STANDARD":"runSPEOS.sh -sv5 <input_file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed772534e840d43b817f87e","name":"OPTRAN","desc":"CDH / OPTRAN是一种软件解决方案，使NVH工程师能够使用CDH AG开发的高效程序执行自动化NVH分析和后处理\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OPTRAN.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.4.1","env":{"MPI":"#This software has to be used with AMLS \nexport PARALLEL=$RESCALE_CORES_PER_SLOT \nexport AMLS=yes\n","SMP":"#This software has to be used with AMLS \nexport PARALLEL=$RESCALE_CORES_PER_SLOT \nexport AMLS=yes\n cp ${AMLS_BIN} .","STANDARD":"#This software has to be used with AMLS \nexport PARALLEL=$RESCALE_CORES_PER_SLOT \nexport AMLS=yes\n"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5edb3d7a3778cd37485a998d","name":"ORCA","desc":"OCRA（一键式Ruby应用程序）从Ruby源代码构建Windows可执行文件。该可执行文件是一个自解压，自运行的可执行文件，其中包含Ruby解释器，您的源代码以及任何其他需要的ruby库或DLL。","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/ORCA.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.0.0","env":{},"loadCommand":"module add ORCA/5.0.0-gompi-2021a"},{"additionalDesc":"","version":"5.0.0-static","env":{},"loadCommand":"module add ORCA/5.0.0-gompi-2021a-static"},{"additionalDesc":"","version":"5.0.1","env":{},"loadCommand":"module add ORCA/5.0.1-gompi-2021a"},{"additionalDesc":"","version":"5.0.1-static","env":{},"loadCommand":"module add ORCA/5.0.1-gompi-2021a-static"},{"additionalDesc":"","version":"5.0.2","env":{},"loadCommand":"module add ORCA/5.0.2-gompi-2021a"},{"additionalDesc":"","version":"5.0.2-static","env":{},"loadCommand":"module add ORCA/5.0.2-gompi-2021b-static"},{"additionalDesc":"","version":"5.0.3","env":{},"loadCommand":"module add ORCA/5.0.3-gompi-2021b"},{"additionalDesc":"","version":"5.0.4","env":{},"loadCommand":"module add ORCA/5.0.4-gompi-2022a"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed5bf854e840d2dd0c84719","name":"ovito","desc":"OVITO是一个针对原子和粒子模拟数据的科学可视化和分析软，作为分析、理解和说明仿真结果的有力工具，它已在越来越多的计算仿真研究中发挥作用。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Ovito.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"ovito","env":{"SMP":"# No command needed","STANDARD":"# No command needed"},"loadCommand":"/public/software/.local/easybuild/software/Ovito/ovito-basic-3.5.3-x86_64/bin/ovito"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5edb41f33778cd28344265c3","name":"PaddlePaddle","desc":"飞桨（PaddlePaddle）以百度多年的深度学习技术研究和业务应用为基础，集深度学习核心框架、基础模型库、端到端开发套件、工具组件和服务平台于一体，2016 年正式开源，是全面开源开放、技术领先、功能完备的产业级深度学习平台。","classifications":["MachineLearning","ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/paddlepaddle.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.8.1","env":{"STANDARD":"# This is a conda environment with tensorflow-gpu installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate paddle-1.8 \n"},{"additionalDesc":"","version":"1.8.1-gpu","env":{"STANDARD":"# This is a conda environment with tensorflow-gpu installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate paddle-gpu-1.8 \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"61e174eb3c4d3f00014b7252","name":"ParallelFold","desc":"Alphafold 的修改版本，用于划分 CPU 部分（MSA 和模板搜索）和 GPU 部分，这可以在预测多个结构时加速。","classifications":["LifeSciences","ArtificialIntelligence"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=微信图片_20220114210344.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"main","env":{},"loadCommand":"module add CUDA/11.0.2\nmodule add Anaconda3\nsource activate alphafold"}],"labels":[],"favoriteTime":0,"createTime":1642165483635,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed7732e4e840d2dfc70f1fa","name":"ParaView","desc":"ParaView是用于交互式科学可视化的开源多平台应用程序。可以使用ParaView的批处理功能以3D交互方式或以编程方式完成数据浏览。\n","classifications":["AutoMotive","Aerospace","LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/ParaView.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.6.1-py3-intel_mpi","env":{"MPI":"srun -n $SLURM_NTASKS pvbatch <paraview_script.py>","SMP":"pvpython <paraview_script.py>","STANDARD":"pvpython <paraview_script.py>"},"loadCommand":"module add ParaView/5.6.2-intel-2019b-Python-3.7.4-mpi"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed776004e840d453c37d8fe","name":"Particleworks","desc":"Particleworks是用于模拟流体运动的领先软件。我们先进的基于粒子的模拟器可以轻松地在各种工业环境中创建和分析3D模型-从汽车工业的油晃荡和冷却到医药和塑料的混合和捏合。凭借直观的界面，超快速的求解器和强大的可视化工具，Particleworks为您提供了分析运动以优化工程流程所需的所有工具。\n","classifications":["AutoMotive","Aerospace","HeavyIndustry"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Particleworks.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"6.2.2","env":{"MPI":"OMP_NUM_THREADS=$((RESCALE_CORES_PER_SLOT/RESCALE_SOCKETS_PER_SLOT)); mpirun -machinefile $MACHINEFILE -np $RESCALE_SOCKETS_PER_SLOT app.solver.double -p . -k simd -n $OMP_NUM_THREADS"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed86e2b4e840d31cce61c10","name":"perl","desc":"拉里·沃尔（Larry Wall）的实用摘录和报告语言\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/perl.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.30.2","env":{},"loadCommand":"module add Perl/5.30.2-GCCcore-9.3.0 "}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed7765b4e840d4df0d8d9d5","name":"PETSc & SLEPc","desc":"便携式科学计算可扩展工具包（PETSc）是由Argonne国家实验室开发的一套数据结构和例程，用于通过偏微分方程建模的科学应用的可扩展（并行）解决方案。 PETSc是世界上使用最广泛的并行数值软件库之一，用于偏微分方程和稀疏矩阵计算。\nSLEPc，特征值问题计算的可扩展库是一个软件库，用于并行计算大型稀疏矩阵的特征值和特征向量。可以将其视为PETSc的模块，该模块为不同类型的本征问题提供求解器，包括线性（标准和广义）和二次以及SVD。还包括以下软件包：Hypre，METIS，MUMPS，ParMETIS，ScaLAPACK，SuiteSparse和SuperLU","classifications":["AutoMotive","Aerospace","HeavyIndustry"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/PETSc%20%26%20SLEPc.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.5.2","env":{"MPI":"mpirun -f $HOME/mpd.hosts -n <mpi-ranks> <executable> <input-file>","SMP":"mpirun -f $HOME/mpd.hosts -n <smp-ranks> <executable> <input-file>","STANDARD":"<input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"625e1a0286c70300017a66e0","name":"PFC","desc":"","classifications":["AcademicResearch","HeavyIndustry"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=7d6051774cd0ec20824e917bc5f92d8.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.0","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1650334210387,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed776ca4e840d1ee0900405","name":"Picard","desc":"Picard是一组用于处理高通量排序（HTS）数据和格式（例如SAM / BAM / CRAM和VCF）的命令行工具\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Picard.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.8.1","env":{"STANDARD":"java <jvm args> -jar $PICARD <picard tool> <options>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed778be4e840d1164aeadfa","name":"PLplot","desc":"PLplot是一个跨平台软件包，用于创建科学绘图，其绘图（UTF-8）绘图符号和文本实际上仅受用户计算机上安装的支持Unicode的系统字体的限制。\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"5.15.0","env":{"SMP":"pltek","STANDARD":"pltek"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77abf4e840d22e4e63fca","name":"PreonNode","desc":"PreonLab是多年研究和开发的结果，专注于实现最佳流体模拟的关键因素：效率，可用性和可靠性。\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/PreonNode.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.1.4","env":{"MPI":"#one instance of PreonNode per node\nmpirun -np <# of nodes> -machinefile $HOME/machinefile PreonNode <input-file>","SMP":"PreonNode <input-file>","STANDARD":"#one instance of PreonNode per node\nmpirun -np <# of nodes> -machinefile $HOME/machinefile PreonNode <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77b4a4e840d28a8f790f3","name":"PyFR","desc":"PyFR是一个基于Python的开源框架，用于使用Huynh的Flux Reconstruction方法解决流式架构上的对流扩散类型问题。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/PyFR.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.8.0(Intel MPI)","env":{"MPI":"# Make sure you supply <input file>.msh and <input file>.ini as input files\nINPUT=<input file>\nPARTITIONS=${RESCALE_NODES_PER_SLOT}\npyfr import ${INPUT}.msh ${INPUT}.pyfrm\npyfr partition ${PARTITIONS} ${INPUT}.pyfrm .\nmpirun -machinefile $HOME/machinefile -np ${PARTITIONS} pyfr run --backend openmp --progress ${INPUT}.pyfrm ${INPUT}.ini","SMP":"# Make sure you supply <input file>.msh and <input file>.ini as input files\nINPUT=<input file>\nPARTITIONS=${RESCALE_NODES_PER_SLOT}\npyfr import ${INPUT}.msh ${INPUT}.pyfrm\npyfr partition ${PARTITIONS} ${INPUT}.pyfrm .\nmpirun -machinefile $HOME/machinefile -np ${PARTITIONS} pyfr run --backend openmp --progress ${INPUT}.pyfrm ${INPUT}.ini","STANDARD":"# Make sure you supply <input file>.msh and <input file>.ini as input files\nINPUT=<input file>\npyfr import ${INPUT}.msh ${INPUT}.pyfrm\npyfr run --backend openmp --progress ${INPUT}.pyfrm ${INPUT}.ini"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77bd84e840d2b4898e5c9","name":"Pylearn2","desc":"Pylearn2是一个机器学习库。它的大部分功能都建立在Theano之上。这意味着您可以使用数学表达式编写Pylearn2插件（新模型，算法等），Theano将为您优化和稳定这些表达式，并将其编译到您选择的后端（CPU或GPU）。\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Nolearn.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"dev-fe9beee(K520 GPU)","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77c4d4e840d2e445b3342","name":"PyMBAR","desc":"PyMBAR是多状态Bennett接受率（MBAR）方法的Python实现，用于估计期望值和自由能差。它包括Alchemical-Analysis，这是一个开放工具，用于实施一些建议的实践，用于分析炼金术的自由能计算\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Nolearn.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.0.0-beta2","env":{"STANDARD":"alchemical-analysis -t <temperature> -p <pressure>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"610a54b351410505e463956e","name":"PyMOL","desc":"PyMOL是一个分子三维结构显示软件，适用于创作高质量的小分子或是生物大分子（特别是蛋白质）的三维结构图像","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/PyMOL.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.5.1","env":{},"loadCommand":"/public/software/.local/easybuild/software/Anaconda3/2020.02/envs/pymol/bin/./pymol"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed77cc64e840d449cfb3fbe","name":"Pytorch","desc":"Pytorch是流行的Torch7深度学习环境的python端口\n","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Pytorch.png","docUrl":"2029/pytorch","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"pytorch-1.0.0-cuda9.0-cudnn7.4-anaconda3","env":{"MPI":"# This is a conda environment with pytorch installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate pytorch \n"},{"additionalDesc":"","version":"pytorch-1.5.0-cuda10.2-cudnn7.6-anaconda3","env":{"MPI":"# This is a conda environment with pytorch installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate pytorch-1.5 \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["60b992a9be44e382f82f0ce6"],"actived":true},{"id":"5ed77d384e840d4d8c704cff","name":"Quantum ESPRESSO","desc":"Quantum ESPRESSO（用于电子结构，仿真和优化研究的opEn源软件包）是一套集成的开源计算机代码套件，用于纳米级的电子结构计算和材料建模。它基于密度泛函理论，平面波和伪势。\n","classifications":["AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Quantum%20ESPRESSO.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"6.5(Intel MPI)","env":{"MPI":"srun -n $SLURM_NTASKS [cp.x|neb.x|pw.x] -i <input-filename> -ni <img-ranks> -nk <k-point-ranks> -nb <band-ranks> -nt <task-ranks> -nd <diag-ranks>","SMP":"srun -n $SLURM_NTASKS [cp.x|neb.x|pw.x] -i <input-filename> -ni <img-ranks> -nk <k-point-ranks> -nb <band-ranks> -nt <task-ranks> -nd <diag-ranks>","STANDARD":"[cp.x|neb.x|pw.x] -i <input-filename> -ni 1 -nk 1 -nb 1 -nt 1 -nd 1"},"loadCommand":"module add QuantumESPRESSO/6.5-intel-2019b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed77da74e840d446852a160","name":"R","desc":"R是用于统计计算和图形的免费软件编程语言和软件环境。 R提供了各种各样的统计和图形技术，包括线性和非线性建模，经典统计测试，时间序列分析，分类，聚类等。 R可以通过功能和扩展轻松扩展。\n","classifications":["ArtificialIntelligence","AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/R.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.0.0(Open MPI)","env":{"MPI":"srun -n $SLURM_NTASKS R --slave CMD BATCH <input-filename.R>","SMP":"srun -n $SLURM_NTASKS R --slave CMD BATCH <input-filename.R>","STANDARD":"Rscript <input-filename.R>"},"loadCommand":"module add R/4.0.0-foss-2020a"},{"additionalDesc":"","version":"3.6.3(Open MPI)","env":{"MPI":"srun -n $SLURM_NTASKS R --slave CMD BATCH <input-filename.R>","SMP":"srun -n $SLURM_NTASKS R --slave CMD BATCH <input-filename.R>","STANDARD":"Rscript <input-filename.R>"},"loadCommand":"module add R/3.6.3-foss-2020a"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed77e024e840d3fa85ed648","name":"RadTherm","desc":"RadTherm是专业的热建模工具，旨在进行全面的热管理设计和分析。 RadTherm提供了多种模型来分析3-D传导，多次反射辐射和对流（稳态或瞬态）。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/RadTherm.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"10.5","env":{"SMP":"radtherm np <smp-ranks> -run <input-file>","STANDARD":"radtherm -run <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77e544e840d4af8866225","name":"Ray","desc":"","classifications":[],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Ray.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.3.1(Intel MPI)","env":{"MPI":"srun ray","SMP":"srun ray","STANDARD":"ray"},"loadCommand":"module add Ray/2.3.1-iimpi-2019a"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed77ed94e840d09e06eea26","name":"RecurDyn","desc":"RecurDyn是专注于多体动力学（MBD）的CAE软件，同时还提供多物理场解决方案。 RecurDyn技术基于递归公式，因此具有卓越的计算效率。核心是多柔体动力学（MFBD），它在集成器级别结合了多体动力学求解器和非线性有限元方法求解器。 RecurDyn支持其他多学科的CAE技术，例如控制系统建模和非线性系统级优化。 RecurDyn通过各种应用程序工具包提高了流程自动化的效率。用户可以使用捆绑的ProcessNet环境创建自己的自定义应用程序。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/RecurDyn.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"V9R3","env":{"SMP":"rdsolver <rmd-file> <rss-file>","STANDARD":"rdsolver <rmd-file> <rss-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77f544e840d137445803e","name":"RenderMan Pro Server","desc":"皮克斯的RenderMan Pro Server与皮克斯用于渲染自己的故事片动画的软件相同。这是一种高性能渲染器，用于处理可想象的最复杂的3D。\n","classifications":[],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/RenderMan%20Pro%20Server.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"V18.0","env":{"MPI":"netrender -progress -F $(RENDERMAN_HOSTS) <ribfiles>","SMP":"prman -progress -t:<smp-ranks> <ribfiles>","STANDARD":"prman -progress <ribfiles>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"61b19e781a50dd0001f99a4c","name":"Revit","desc":"","classifications":["HeavyIndustry"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=revit.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2020","env":{},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":1639030392475,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed77fc24e840d4518c94451","name":"Ricardo VECTIS","desc":"VECTIS是一个三维流体动力学程序，专门针对车辆和发动机行业中的流体流动仿真而开发。 VECTIS是围绕全自动网格生成器开发的，可为CFD分析提供快速的周转时间。 VECTIS独有的全自动网格生成器相对于大多数其他商用CFD工具具有明显的优势。这样可以确保CFD是现代工程开发计划不可或缺的一部分。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Ricardo%20VECTIS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2016.2","env":{"MPI":"phase2 <mesh-file>; phase4 -np <domains>; phase5 -np <domains> -rdmu -hf $HOME/mpd.hosts <input-file>","SMP":"phase2 <mesh-file>; phase4 -np <domains>; phase5 -np <domains> -rdmu <input-file>","STANDARD":"phase2 <mesh-file>; phase4; phase5 <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed7800b4e840d5448bad6d7","name":"Ricardo WAVE","desc":"WAVE是Ricardo Software提供的一维引擎和气体动力学模拟软件包。 WAVE使车辆性能仿真几乎可以基于任何进气，燃烧和排气系统配置进行。还包括一个传动系统模型，可以进行完整的车辆仿真。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Ricardo%20VECTIS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2016.2","env":{"STANDARD":"wave -b <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed780584e840d4240b13c69","name":"Ricardo WAVE RDM","desc":"WAVE RDM是Rescale上WAVE的与Ricardo RDM兼容的实现。运行RDM需要正确的RDM许可证，并且最适合用于大型设计的实验工作。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Ricardo%20VECTIS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2014.1","env":{"STANDARD":"NA"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed780b04e840d3e9c29d72b","name":"Rocky","desc":"Rocky DEM是功能强大的3D离散元素建模（DEM）程序，可快速准确地模拟输送机斜槽，轧机或其他物料处理设备中不同形状和大小的颗粒的颗粒流动行为。 Rocky DEM的多种功能使其与其他DEM代码区分开，包括其Multi-GPU处理功能，真正的非圆形颗粒形状，能够在不损失质量或体积的情况下模拟颗粒破裂的能力，由于磨损而导致的边界面减少的可视化以及更多。\n","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Rocky.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.3.2","env":{"SMP":"Rocky","STANDARD":"Rocky"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"616ce86cad54eb00010c0aa0","name":"RoseTTAFold","desc":"Rosettafold","classifications":["Medicine"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=456.png;base64,iVBORw0KGgoAAAANSUhEUgAAAFoAAABaCAYAAAA4qEECAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAfaSURBVHhe7ZdrbFTHFcf/M3d3jdfGhmJSEK1jvAYXsAptaIQIKYRECW3dJigYIzCqFKURpW3UqB+qqmopUlu1FYkUteEDgUQJOGG9tKoi1Fci5YWKCkSEACYxBkMIJJQEcIMf8e6d6X/mXgcwDz+o1C/nB+uZe2buzJkz55yZC0EQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBGEYqLgUBtL0fgnCniYYnEW2OhdLIxa1FaFML4bVo5GrfjKWXpehGXrJP4uhP7sesAv5+yOUep/SOmj9FKY+8zrWrjVRxxvgvtZxSKbcgv4MpW8CzErO8zIsdkDZ5VBmPUzwCOffSK0XUv5V9t0Mqz5iWz1Ov3cPXr2jgMb2r9E4vwR65yNXd8GP3Xh4LvttYi3Psf8AG/Zy7Hq/luyUrO8zkMb2JzjHalj7U+Sm/DqWRixt/yHfXUfzbUBLzepYel10XF6fbXN7uNidrFVS4X0wmcc4UQ9C8zz2r8xEnW6QpEly8XuAzBMw4S5KKmmwE2ippoGCHL3nM5Q1c2FPc/EnfTvUvzD92cep00EsWOBGoVr2Hm7EF6GTn48EJEgdYkOahhsD3fcntEx5FgF+y+fRcY+rYP/hBqOF+mLBRXTyRc7Zy/Yr267B0AztUWFcAXKsWy4SdgJSelosvUFKaBBGiBv7MpRFKtlCo3+CZPhCLLyIiyZtt6KVZvMoGs8G/O88PqLLuLaoPVEalfrC21CJdl+/GlZHUar0AH1IgRvghxuo67VJxOUIUNxNThaGo/xjY1uGxn+YohP0qAyMWo9tNfux4sMy9J37PlfWyRCfQvkmL1/y7iQE+YfZ9wMYm0Ey/xs0T33bj5UImDkuyUabP/cRGuwWNPcvzPlHZC/P1pqdjAaLho4JMPm/ss5Ug7vYZ0O0UTS0ZXkpm2d24xdMS05vBE7vdymtQTL4PZqrWqNOl9B4nOsr/I6KfUxLd3DcYdluGB49AGVm8+9p6MQuNL1ZwoVwUdoyn61j/RRHzuKBQ6NR6HyQC69CjilBM9UEehYeskmG8EZYMwa11Y9znPEo6F/RmLQwKVzFUS7z9AFHgjOyI9DjUJJ8ic8fM+rr0PRB2ssHsqKtDA3vVOGNN1L03E3sG6Il8yj35BTnztIJiuOeEWtsAja/kRs3A4nUIzyb/ha3DJnhGzqwKSw9dhs98+vQajWyVcfRV1LL1c6nIsyFROEw619AV2IhbXI7JdPQ0BIg3LeH4b8T59umc3ELGJb7sVbRaraDHnU77MFy//6gXENta8tx7Jg7APdSh0oUukujhk8pRr6vHnn9M+igCumxMyibx987vhU4ws2fgSCME37MwQ56s53D3wE8V3meZ1OB0ssjZBCGb2ijZjKE1nH3S1BI76BVOaFiPrSaU//H99HBeedm9Ow59OIuSuZBfekVBF9ehA/tCebP6Wwu4jsBlh+9mUYZwz5pFJVSdgMojPU3D4WX+MDDz8yJW2J4eBUKu6ECHuwu9fHmYtkbKrqdhOhknc+Fuf65H23cgZ9i03G2RwZ2aXoYDN/QFrsZOo/RiDdBd90WSysiBRiCDuN33FHO/gxN8IRW85iLcygLmK+ZKijgvyKEeYZ3YisX8SAS9px/a1CucptcyeuhNdOxrGMxx6WRraGO34xbY5h+itOnYbq2ozf/FsehHs5gsd7aRnpb5Tb+IkqXeG2V24iRMbIcHcBdv3i4qSb/rJxHWCqi4vFMlGuBHti9ryFQ32I704li7jPLqPAnvtXwipitPYTs5FeQzWzHM5O5ISMkryehVz2JstTfqcrz1IWRZGfiB/y4GEiurg8vTOOhFnuyiu2gErH+1Ksfa1ScKlhn6hkhIzN06dlT/NtKQ83DimMTqcAe/njnZLh6dJwb9T6kbq3F1syL6O6cxR4MaZSy31GWTvlbsGZNpIP72lrD9DNiUsU4z/y8YVI3TDVzLf5Ny92Mc7ocfZpOQEdwFC5c8pGmd/vC8o4dVSK9rdrrS4dLLYE9zRq9Xk1wjVHD8Bj6wixPXofmtWbD7DzffJoqVPBwWcgDbhfl+7n7PBSJUjV84SRMYjvy4UP+FN8+u5stvBHoN6myiwgeQOobOLjyXjS034LywEVHpI9CdGVUNiqvQKV8ETBv9mPMZObnKAW4G4pSr9NI47iddUh2cVyXe939OtkfbfxIKnPXwgNsiz+6TDXX6fLwXz6NSqWTMGVv0b7v8f1bUX+qmPO66OV46uJYgzA0Q9ef5I7b+33d4n7/HISv8YnhF/wEfck0wuC7nPguLDu8iAa/lwquwrbKs1TpDK9yj2Lpka+wfid06ue86nUiCL7D9zWNuZnyLQjVDt5ACj5CtPqenwtYjOUds+J6RPR8n68buwpL2saj8Qg/p5nj578cLfzbHdwgW8txNdP5j6GL3MdLBY1TwU1e4vs4miuov/vMxt1oPHo3y8UIEqu8fopO4DbH2DuAV3s40o/YN4N0z3Mc8wHW3cbR8M42gzOiMLgmy4+PRdg3DUnVji01DN0Yd7MwmIBE8QFsmehuIRH9/UclWpmfeVP5P9GvR8hzZNvUM7H0Sho7qujx5YzUNqTsRIxKn8FT491mCYIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCP9DgP8CwlqxiwAG25EAAAAASUVORK5CYII=","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"main","env":{},"loadCommand":"module add Anaconda3\nsource activate RoseTTAFold"}],"labels":[],"favoriteTime":0,"createTime":1634527340741,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"61ee4a4c2c3de40001facd97","name":"RStudio Server","desc":"RStudio是 R 的集成开发环境(IDE)，R是一种用于统计计算和图形的编程语言。它有两种格式：RStudio Desktop 是常规桌面应用程序，而 RStudio Server 在远程服务器上运行，并允许使用Web 浏览器访问 RStudio 。","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=RStudio.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.4.1717-foss-2021a-Java-11-R-4.1.0","env":{},"loadCommand":"module add RStudio-Server/1.4.1717-foss-2021a-Java-11-R-4.1.0\nrserver --server-daemonize=0 --www-port 7070 --rsession-which-r=$(which R)"}],"labels":[],"favoriteTime":0,"createTime":1643006540971,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["5ed77da74e840d446852a160"],"actived":true},{"id":"5ed7810d4e840d4268f968c5","name":"SAM Tools","desc":"SAM工具提供了各种实用程序，用于处理SAM格式的比对，包括按位置格式排序，合并，建立索引和生成比对。\n","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.9","env":{"STANDARD":"samtools <subcommand> [options]"},"loadCommand":"module add SAMtools/1.9-iccifort-2019.1.144-GCC-8.2.0-2.31.1"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"61b19eb21a50dd0001f99a4d","name":"Schrodinger","desc":"","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/png;name=SC.png;base64,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","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2021-2","env":{},"loadCommand":"module add Schrodinger/2021-2"}],"labels":[],"favoriteTime":0,"createTime":1639030450641,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed781704e840d31e80dec72","name":"Scikit Learn","desc":"scikit-learn是基于Python的机器学习包。 OpenCV是用于计算机视觉的C和Python库的集合","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/OpenCV.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.21,OpenCV 3.1.0","env":{},"loadCommand":"module add scikit-learn/0.21.3-intel-2019b-Python-3.7.4"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed781ef4e840d4a68de9dbf","name":"Shipflow","desc":"船舶流体动力学CFD的世界标准。 SHIPFLOW由海军建筑师，物理学家和数值分析家开发，针对船舶流体动力学设计进行了优化。\n","classifications":["HeavyIndustry"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Shipflow.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"6.4.02","env":{"MPI":"shipflow <input-file>","SMP":"shipflow <input-file>","STANDARD":"shipflow <input-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed88c854e840d375894076f","name":"Simcenter STAR-CCM+","desc":"STAR-CCM +不仅提供CFD求解器，还提供了一整套应用程序，用于解决涉及复杂几何形状，多物理场，（流体或固体）流动，传热和应力的问题。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Simcenter%203D%20Cloud%20%20%20Flow.png","docUrl":null,"users":["hezhiqiang","zhangliguo","humanhorizons","puppetect","songci","libre","ray55595","raymond"],"status":null,"versionDetails":[{"additionalDesc":"","version":"14.02.010-R8","env":{},"loadCommand":"module add star-ccm+/14.02.010-R8"},{"additionalDesc":"","version":"15.02.007-R8","env":{},"loadCommand":"module add star-ccm+/15.02.007-R8"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"625634a49efb0b0001e7351d","name":"SOAPnuke","desc":"用于对 FASTQ 或 BAM/CRAM 文件进行集成质量控制和预处理的工具","classifications":["LifeSciences"],"owner":"hpcclient","system":"linux","picUrl":"data:image/jpeg;name=21273571.jpg;base64,/9j/2wCEAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDIBCQkJDAsMGA0NGDIhHCEyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMv/AABEIAMgAyAMBIgACEQEDEQH/xAGiAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgsQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+gEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoLEQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APYVFSAUgFSAUAAFPAoAp4FACAU8CgCnAUAIBTsUuKUCgBMUoANDKccE8c49adigBuDnOfwpSDg4xntml7/1oJC9v0oATFN5OCB35ycYpwyTyMD0pSuflyR7igBuKMUBCCxLE5OcdhS9snjjnPagBn4EUYp/UUmKAIyKQipMU0igCMimEVKRTSKAISKYRUxFMIoAgYU3FSkUmKAJFFPApAKeBQAoFPApAKeBQAAU4CgCnAUAAFKBSgUuOaAE9s4oAPTI49BShRnPfpSbTjGeemR2oAacupC/Q+tL90KMgH35z7UJzuXO4gjOcZqlqOpW+nxRPcBmaRwsUaqSxf09vqcCgTZe2njHA5JFJu2hmYHA74rI/t3/AEcTrayuw2bolB3jdjquMg8g49+av6fqNvqVv5tqxKjg7lIIPuKEJST2LPDD5T7ZFA3Z5xigjaCS3A5JPAowwwFPGMc9v88UFCcbuvJ7UYpTwc5GDS4oAYRTcVIRTSKAIyKaRUhFNIoAiIphFSkUwigCIim4qQim4oAkAp4FNAp464oAcBTwKQCnCgBQKcBSYpwoABRtOc7jjOcUdQRnB9u1IWJbaFJ/SgBcZcEZGB+dJtKjLS/KOucDimynDA7vunOB3pzbghCEbyM4Y0ACoOQBlc56985rmNeMf24N5pV4yib1dUaLc20vkjBHznPuBiuoKhn4YFl5wewP+f51yN/DcpqZMcbB5LjfM6sg2R7Rg/P6HBOM4K5waNehlUvYrre2arLJePfWzkpbSzLJkbgBhlYHnBJBBHPQjAq94XuIXMwiV0ABaQghwzO5fdkKN2d2eOB04xWZMbtLm1vTBdXNhHG9wJBcptDfMQeNpOQeAcqM59TV/S45r29hucq2bhpFQsHaGNgSVZl+X7w4HPBo54u2/wDwUZRl7yOpXJYdcg8/LgGnk5K5O3nIGevFRglc5Xaq5Le/+fzpWEgOSd8ZGCuOn+NB0jgvzAdRnIJNGAo5JIBzk0Y4wCwJPBP0pTuYHHy+/egBAPlA5/GkPAyaBlTlj144FOIoGMIppFPNN4PSgCMimEVJ169aaaAIiKbinkUmKAHDingAkU3nBwAT2zT1OSfb2oAcMYz2p6nIzTFIXgDAHHtTs56cj1HagB9JwGwMk/yH+RQ5AXJ6DkgdTSKW285+hHNADyPr9aYfu7mKn1YDoKcuFULzxwSeKRkXbuCkMBgY64pCFZdz4OcYx04NLJjbl8kH5RheRnionZ/N2hgOOWIIx/j0NOAEWBvdtxz1z2/lQAbkbIYEEjByMcf5NYmv2jS6cIlnEZn3QbiAfv8A1GT04wQeB1OBWtJcwRNueVDIvyDLAEnPT6njj3rm9V1W2aWJJn+yD7RH5Mk4VhJjrsHJXg4JIBBIp26EVGuWzMqzWe0hhsvtEcToGiLFQ4Mm7cuZMgiTGPlx0PTiup0OTdBJ5twkkok8ssoCjK9eO3XPJJ5rl9Rs7GythEuj3V5blnkligQMI2XHLLj7zHo2OlWvDurD7RemNprwPOHkjMaxvaAgfLjq5GCOO44rSVO0bpWX9f1qZxtA7Qkqdzth2BAxz37cfSkUMCGZyOenqPx/pVObV7Bo3Av7ZJUbaytKMg8nGM5H3W/75PpViKdLriORXQLyy4YN+IOKzaa3RvcnkcqB0OT2poVIh8vcjOW/DvTUIYMhXbIDnGTj25/CnCPGV4wRjJYkmkA44B9R/KkHJ7jPY0zAKKSwOMfMGOM/nT8Ajjgc/WgBGPO0DOetJn8D6UKu1SOc9ycZNIemMZI/SmMac56Uxs9hTm+bo30IFDUARmm05h7ZzTfLX+6PyoAcvU808DOOSKaKZPdJaqC6ysDn/Vxl+gz2+lAE/XJ6EdMjpT1zuIO3p+NYeo6rG9o0lrdm2lhJf99A+GABBG3gnkjp04qfT9Ta4Xy72MxXEIAdsYR3PGV7/h79+KNiOdXsaWxuGYng5wTx+fpTxjAbcTt4yeMdqpzaxY28CzvOJF3Km6EF+WIA4GeMkfTNRPrFqqu0atIghEylXXEi8/d+b2/UUWY7o0CnmIQFHzY4fHPv9cf0pX3Fs9CR36cf5NZketLlX8jZE44O4nJILAcD+7g56c4zxzmahrV65u4wnkzQxCeOKBhJK6AjcGG0gcnAAyTzRbWwnNG7czwwBAqn5MKqIuecdwO3TnoK5rVPtF3NCJzP5czMhhUAx7RzlgQOQVGGH9cmBL6C7tXksTDGok8yDfIEWZ2wACABg7964OT8o7GmLI2oW5iv7O2e73MqxqrOd2BnDEAAAe/PGMYAp2slJuxlKV1uVf3Ud1a6VaHSlIkK3K+UwkVRkttIOQMBdpHTGMg1o6jNZ2CLLe3KacmAFdpd0kbsdzKoAIzhsEjORjrWVplz4ftNeubS1ito7sZKNAmAVKqUCH+LAXPbvkAZrdvSyxfbmtLQPJta5glwSyDIwM4G7nGc4Pr2rWq0+VNO33fd5CeqIdMiFpql/I9qxlu5E2mGPbvjC/JubOMjOOwOferCC3/tSW0FzDcOjZZZZDvGTlFxjBHDYz0IHHemtc6W1s92t/ALdJfN84sGQCM7WJbOep/lVa2ZVvk+zwvMjSSyRXBnV1iIBCKqqMsCGzjnGe+M1lrUldr8f66Bq3sT6zdWOmeHbiWazUWiIkWzyww3bsAYJ55bH581R0qPTrn7JqOn3ERnMKusayY2RSEOV2bgFJJHqSAcelR+I9XgbytNu45VkkKbioJFvk797MeMYB/hJ+UnjFSrM2mabDY2gdElIFvIY8rKrdMH5fm6njPHr1FyfLHXd/1/n1CUrPU6q31RI4t12yQkEBnZhtJxnrk7fxq6CFIKYZCuBt6VxupE395Hb6fc3EZkuI3ndI2+4oDctnoRkAjIyefdGvNUtI4fIilXUpZJJpIUkDggEYjbLELuBBB7Y4GTipcerdv6/r06mntLbnbMCCfmznpx0puWP3cE9fT/APVWCuszXKljJHEY/lkU4bDBirYPPRhz+HSiHXLgxWxmMcUkjOHj2jdkFgAo38nO0d+/rUtND50b4IOOPpSMcd+B3NYGm+ImvIneaykhOSyQsVDKvy8yA8I2SeM9ASM1DqetmRZBbskLwqPN3rukIbgbCD/eyOnbt1oaauhuasdAHSRmQOhKj5gp5H+FLz3OTXMaTMbSw8i0NpbkNgNcs2+TptyCAQcEA+h6VtWpuHdjNdW08YAwsSEYPJyTuPbFOw4yuWiAelJSnikpFBzkADPByc4qlqIvxPC9veWttbAESGVCzFsjGOQOmR+NXhjd2zin7Vb7wB+tF7CaucvqTXU3mCHURcMkq/uxbAomME5Ixjgj5iTjn6VmWlrqdzLZQrAEjgVlkWPOGcA4cbsfLwQM98c12Elj9odQZdtugx5KjAJB7n0x2q1FBDAgiijEcYO4beBknn9f51TloZuF2c++n3TyI9wtxhE+VlIVgWzuBIP3fu9D1UVmjTSljJAZryNRaGJpZJdzA/dX5COc8nd0+lduRhgOSccegp/Bc5UZxjJ70ou1rj9mjjY5I5C1tcssyMJDJES3yrkJxj73zDbg/n62tE0eNIUVbOeyVF+QMFyhJLHoTzk9PYEV0kYYLtEeztyc/l7d6ImkLOGQbAfkKnoBxj65zRFtR5f6uKNOxmHTJbmOAXBQFW3blG4g+xwO3f8AL1D4NLtrBTJGoWd1EbSdCwGQo98dv51orx8pky3bJxk0gz53LZ7gZ4x/nFSVyo4G5t7ootnBbu80UkQaYPGnmsu0lj8wPXPyqcHf6k1v3CWcebrz3QrtjceaQOCDkL2P4c9ORVbWYo4NdtcziGaRJFtyz5YtgfdyMcYzz6+1VruUW12Le1s7mJr2RhJchwUEKY3OWBOAcnb05J6d6T87/wBbGO1+pVtyLjUku471rfTUdIjA1uAEPIeM56q5K5YA8r2rTtZbSaJLx3eONWyFZ3AU4A5GSDgcgYI+hrP1YzSS2+mRWZgkPmfZzF5ewqHz94j+IqmeDjcDyRU9xpk1w0bF5NkM4uLa3imC73VCGQngAZGRgjgnpVt3au7dv6W6e1+4Xd7EC/bbLU7v9xHIs1wsiAyDoAOdoUsGwvr3B78dXaadEtnbwTBpJIYgqSuSZM7QuWbJJJx1rnnt7F/F9ratcl5mR3aMKpIG4EfMMHbxIuDnOG+tdch2sxGDnjvy3OfpWOj2f/AKppalKLTIYiqJI0fy7RzznHUA/TOOnU45NVpNNdbREt9kkqAElyw38EdcnrzjnjI9q2NgXDcl/f8APFIquoTftySc+2fQ1Rpyo5iazv2VY57KaU+U0bCOQGPcF3buTuIY/KM9D+dSf2JcKkYtrdYikkhIcId5b5g5OCRz1wOc+ldIVZsjA2kYORgj3oK5JGcEDoe3+f6ULR3sL2avcxn0d2aN3kU7AMqUBLAAjBIxxgn05Y9uKo3/AIamMy3dvcH7RDEViZYwCCTzxwOQAPw9zXUY9MYNNxkZDDHbAoY3BM5PTtO8uOWG1hsSzOshgnhKsoG0DJxnoOMjgYHvW5aWkcbmY2UMEu0LmPqRx1wO2AO9W3gheaOZo0MsedrYGRkYPNLnOfam23uCjYYeR0pKVsEUlIsUdaeKjFPGM0APXGe/HFL8x6Nwec+lIKVSSMkYOeme1ADhu7kAY596ACvGV56f5/OgckcdKXaQmByeoJoAAoJbqAw6mkxszt5UcnnJpRhFJJIHvSfKU3nByAD3yKBAFDBWI57BQPy+lMUEvwWAHJIxgnHfFPKEAtHx3G7PX6U3IDum3LN82AuM88c/gPypAZGp2EE93a3GDvglWRXEQJGTg8sOPwIP5c0Lqedbm3jm3t5aGW4C5UxxgBsgdzuXH5d61NVLwtBdoJ3hicu0cAyZMqFHtjv68emay7tBFdL9uIcITiXyyqMOB85HHRsYyB1ODjilv8jGS1M/TbWb7DJDcT+RNLIBGSwJdTuwANmc5ypB5AB56Vb0u6Szt1imkMckZS227mkG4cbug2oQBzwMjA9KfDLPFMLW5khvJpJZZbGQRrhVGCvTgkAjn2/GjKHUxKhEl4qiN8RAoSCW3sB1G4fLyOcetTGLTIjGzNDTrWQ6nDeTo73KwiNyCCinnJGec9O3ftWxCSykgZ+brnGcd/p/nvWTDHNHqIMlwqxIz4UEnzlYA888EMfetdSwYhUAA49se36UX0NobCmQ7sFCeSOo6fSmQh8JuYkHPJPX/IqUhMAEDGOppFA3sV7nJBHt1Hr2pligEDbgkjkEnv6UEEnPBB4IPpSnKjjmmgMBg+vXrQMQnBC9KQKFAAAH0GKdj159DTSOc0AN+VhkYIPIqNRhcbQoHAqQnFNNADDSUpptAAKkFRA08GgCQGnKMD8fWmA08UAOHAOBTlztGcZ74popwoACBjpn60EgDeVPT0yaX6UFsDP49KABifLJHXtkUwL8g2MQMAAg549u1P2gnNIFGeVOf73r70AY+oQtNcI0kY2xTDY6sAwXG4+p5Ix+vvWfLHNNqErPvhiK+UI1U7Arcl9xCngBgQOntxjQXS7OG+vrlII1uJsF5JBuH69sg8cetVorOGVY7i4gU3EatDKyIcD5eSvfHJwPQ/SktGmzFrW7G2sFpH9nXyTcCKN91zKcnBAB6j5mYA5I6Y9DVe4tXjtEktDIzqGm8pSpBkGAqcDd3AzgnA/O416PtCFxscKXhMmM4BAOTyADxjHXPbrQ6Q3QMlwm+4t2LeX5ZOOoznbknHtjOPY02ulhNJqxFYQJ/a0s3kxRcthCrcyDGWAbHHbI/oa6GERunmIxIYAghsjHbB71hWdpMYpre6KSC4B2IUwQp7nJJBG7GAccZ4zW/DGsUYjUKEVQoVVwBj0oe5cESZBA4NJkE0nIBJPvQCD0/lQaBgYGOg6c0nagHjrQaAENNNKaaaAGmmmlJphNADTSUE02gABqQGoQakBoAlBpwNRg04GgCQGnCmA04GgB+aOcHaee2abml3YI96AHDvwMUg43LuJJ/wDHaOMc0AAZ9zmgCje6d9oLzxuVufJMKPtDFQeTjIxzgZzkcVQiuYJowbuVAHl82MTArtUNwCQSByPXn07Vu9CMZ9KyNV0GPUZUuIZTbXMY2iRVBDLwcHvx2wRg81V7rlexElpoSxxxqxlWKNZWAi/dvlcAnHXAB+Y9uc1BIq3JWNi8JVg/kMVGQrkn3O4An8O2TXLT3Gp6PqMrvFaR6XuMkgaY5KIp5QLgA5AyGOCSOea6XQda0/VFeWG7MjqQGGSEGem3IGQRjn3qddGRGV3qbMSHy0VwqY/hTgVLk46UDqf8aCaDYO3NIOABjAHQUDBGR35pOgwKAFzTTRmkJoACaYTSk0wmgAJphNKTTCaAGk0maRjSZoAQGng1CpqQGgCUGng1EDTwaAJQacDUQNOBoAkBp2ajBpc0ASZoPPWm5pc0AKzqilmIAHUmsZrmXVmlSISpaIxUumAXIxxzzj/Poafr88sViqwJLJI0gwkRCkgc/ePAHHfHpnNVZWsre0EU9vdSLGmGkVevGSeMZOR0xn2ppGcnrYuIrsfKNhM8TKQWlMaoB0xtBz+n+FZ9/wCG1mbzxMFU7i1s2NjMVAUgnO0jGRtxyc81FP8A2c4lLW2pTkFdrJIyhiQeV5AHB5PHbqRT0vtFhKIlvMTKcIHbliDgj52zxgk/ShX2WxOj3LWhaoXRbOZsPEBDh2JcSBQWU5HPBByCc85PFbua43UNSt5ru2uLQqpDhfLkj372XnCEN8rAFhn+fQ9hmkVTd9Bc02jNITQaCk00mkJppNACk0wmgmmk0ABNMJoJphNACE03NIxpuaAEU08GoFapAaAJwaeDUANSA0ASg08GoQaeDQBKDS5qMGlBoAkBpc1HmlzQBR1yx/tHS5IRFDKwIYJMm5Tj269M8jn69KoWepNcRm2F39nliJWMlVfzlAHzDHbJAx1/PNb2axNY0N787oLjyg6GOVcH5kPJ2kEFWyBg5wPSgiSe6HvLch2/4m4iGOk9ttyxPAycDHGMAZ9881Z87UFjLTQ200fJyjHp64I9Mfr+PMNrGo6ZcyJfxpb2qBmLzXQbaCwVBgjG0k8ZIbgjkjNbGl3Vjr1v5trco1vGxV44GIG7gnn/AA+tJERlfQkh83Vr5bgLLDbR8ffxvx04HuTzxxjrW5mo1Cou1QAPYUuaZrFWHE0hNNzSE0DHE00mkJppNACk0wmkJppNAATTCaCaYTQAjGm7qazU3dQA1WqRWqsrVKrUATg1IGquGqQNQBMGp4NQg04NQBMDTgahDU4GgCUGlzUW6lzQBLms+bUDLdNZWrqJh95zyE6frg9P/wBYbq94LLS55icYU9Bk4AycDucA1i297pEVg73MRDZDM46u20Y2kHLEDA49KaREpa2Na5sw6rDPb/aVIwzOgkVj6spPsOlYkuhotpKLKa4kcK0scODGyux3ZViMp3AHAGfXmrL3mmrMipql9al2MYjLMQzHsNwPI/2ff0qeO8jEyxLq8pHIG6InODtILYxnd/Wi9lYh2Zf0rVk1BZAGUlZGVSCcsFODkY4III79uea0c1ys9xHH4hsXiljLTfMI5Y2EuDgMVyc44UkY45P06bNIuDvoPJpCaYWpCaCxxNNJppamlqAHFqYTSFqYWoAUmoy1Baoy1AAxpuaYzU3dQAxWqVWqEdaevWgCYNUgaoVqQUAShqcGqMdKcKAJQ1KGqMU6gCQNS7qZS0AUdbtZb7Spordgs+MxvnBU+oPY4zz/AD6VUtL26ZvIWGNZI32q8wxvXb94YPqQOx9q2ax1/wCQmn0P/oSU0ruxElrcl330Co87abAAuSAjMVOB05Ge4/L6VH5tzPFGI2tNoA3LJZyBS3fBJ6fh2o13/VD/AHTVuP8A480/3f6Ur9BdbGfZCa81MyqqiyU7lZPlVz1BAz0yc5749DW9urI8Pf8AIHt/+uaf+gCtWgqC0FLU0tQabQUKWppakNIelACFqYWpT0phoAQtUbNStUbUANZqTJoPWkoA/9k=","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.1.7","env":{},"loadCommand":"module add Anaconda3\nsource activate soapnuke"}],"labels":[],"favoriteTime":0,"createTime":1649816740640,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed782634e840d3c2c9ab498","name":"Stanford University Unstructured (SU²)","desc":"斯坦福大学非结构化（SU²）套件是用C ++编写的软件工具的开源集合，用于执行偏微分方程（PDE）分析和解决PDE约束的优化问题。\n","classifications":["Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Stanford%20University%20%20Unstructured%20%28SU2%29.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"7.0.0(Intel MPI)","env":{"MPI":"parallel_computation.py -n $RESCALE_CORES_PER_SLOT -f <config-file>","SMP":"parallel_computation.py -n $RESCALE_CORES_PER_SLOT -f <config-file>","STANDARD":"SU2_CFD <config-file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed85ef24e840d2a0ceacb04","name":"STAR","desc":"与参考剪接的转录物比对是用于基因组测序的工具。\n","classifications":["AcademicResearch","LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2.7.3a","env":{"STANDARD":"STAR"},"loadCommand":"module add STAR/2.7.3a-GCC-8.3.0"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ecb40fb3778cd2c545dad1b","name":"TensorFlow","desc":"TensorFlow是一个开源软件库，可使用Google的数据流图进行数值计算。","classifications":["ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/TensorFlow.png","docUrl":"2029/tensorflow","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.15(Anaconda3-2020.02)","env":{"STANDARD":"# This is a conda environment with tensorflow installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate tensorflow-1.15 \n"},{"additionalDesc":"","version":"1.15-gpu-cuda10.0-cudnn7.6.5 (Anaconda3-2020.02)","env":{"STANDARD":"# This is a conda environment with tensorflow-gpu installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate tensorflow-gpu-1.15 \n"},{"additionalDesc":"","version":"2.2(Anaconda3-2020.02)","env":{"STANDARD":"# This is a conda environment with tensorflow installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate tensorflow-2.2 \n"},{"additionalDesc":"","version":"2.2-gpu-cuda10.1-cudnn7.6.5 (Anaconda3-2020.02)","env":{"STANDARD":"# This is a conda environment with tensorflow-gpu installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate tensorflow-gpu-2.2 \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":["60b992a9be44e382f82f0ce6"],"actived":true},{"id":"600fcceb372f3082a77843d2","name":"TeraChem","desc":"TeraChem是第一个完全从头开始编写的计算化学 软件程序，可以从诸如图形处理单元（GPU）等新型流处理器中受益。","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/TeraChem.png","docUrl":"2029/terachem","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.9","env":{"STANDARD":""},"loadCommand":"source /public/software/.local/easybuild/software/terachem/terachem1.9/TeraChem/SetTCVars.sh;"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5edb42943778cd355499690f","name":"Theano","desc":"Theano是一个Python库，可让您定义，优化和有效评估涉及多维数组的数学表达式。它建立在NumPy之上。","classifications":["MachineLearning","ArtificialIntelligence"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/theano.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.0.4-Python3.7","env":{"STANDARD":"# This is a conda environment with tensorflow-gpu installed. \n# You can conda-install or pip-install more packages."},"loadCommand":"module add Anaconda3/2020.02 \n source activate\n conda activate theano-1.0 \n"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed860a24e840d4e387157ac","name":"tNavigator","desc":"Rock Flow Dynamics的tNavigator专为在工程师的笔记本电脑，服务器和HPC集群上运行动态油藏模拟而设计。它是专为在多核共享和分布式内存计算系统上运行并行加速算法而设计的。\n","classifications":["PetroleumExploration"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/tNavigator.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"18.2","env":{"MPI":"mpirun -np $RESCALE_NODES_PER_SLOT tnavigator","SMP":"mpirun -np $RESCALE_NODES_PER_SLOT tnavigator","STANDARD":"tnavigator-serial"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed864b94e840d472c7ab0a6","name":"VCFtools","desc":"VCFtools是用于处理VCF文件的工具的集合。\n","classifications":["LifeSciences","AcademicResearch"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"0.1.16","env":{"STANDARD":"vcftools"},"loadCommand":"module add VCFtools/0.1.16-iccifort-2019.5.281"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed865114e840d4d346fc9fe","name":"VI-Grade CarRealTime","desc":"VI-CarRealTime提供了实时的车辆仿真环境，车辆动力学和控制工程师可以使用相同的简化车辆模型来优化车辆和控制系统的性能。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"19.0(Windows)","env":{"SMP":"vicrt.exe -f <input-file> [options]","STANDARD":"vicrt.exe -f <input-file> [options]"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed866264e840d5274e47eb5","name":"Virtual Performance Solution","desc":"ESI Group的虚拟性能解决方案可对尚未实际制造的产品进行机械测试。它使工程师可以根据几何定义和材料规格在早期对设计进行交叉检查。因此，可以节省时间，资源和原型制作过程中所花费的费用。\n","classifications":["AutoMotive"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/VA%20One.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2019.02","env":{"MPI":"pamcrash -fp <precision> -np $RESCALE_CORES_PER_SLOT -nt 1 <input-filename>","SMP":"pamcrash -fp <precision> -ft <smp-ranks> <input-filename>","STANDARD":"pamcrash -fp <precision> <input-filename>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"600e9e2e5e50baf5cb25e64b","name":"VirtualFlow","desc":"VirtualFlow是由哈佛大学医学院牵头研发的全新开源药物发现平台，旨在利用高性能计算能力并行筛选潜在的有机化合物结构，以寻找有希望的新药物分子。","classifications":["LifeSciences"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/VirtualFlow.png","docUrl":"2029/virtualflow","users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"VirtualFlow","env":{"STANDARD":""},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ed867254e840d4d94e7bea9","name":"VMoveCAE","desc":"VCollab的VMoveCAE是用于CAE输入和解决方案文件的CAX生成器。它使用户能够读取各种格式的CAE文件，根据需要过滤数据，从CAE解决方案创建附加功能，以及将这些结果转换为VCollab紧凑的CAX文件格式。\n","classifications":["CAE"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/VMoveCAE.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"2018R1-6","env":{"STANDARD":"vmovecae [option=value] <model-filename> <results_filename> <CAX_filename>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed868be4e840d4e40dc3949","name":"VR&D GENESIS","desc":"GENESIS是一个完全集成的有限元分析和设计优化软件包。分析基于有限元方法，用于静态，正常模式，直接和模态频率分析，随机响应分析，热传递和系统屈曲计算。\n设计基于高级近似概念方法，可高效，可靠地找到最佳设计。","classifications":["AutoMotive","Aerospace"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/VR%26D%20GENESIS.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"1.3.1","env":{"SMP":"genesis <input_file> -threads=<smp-ranks>","STANDARD":"genesis <input_file>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ed869214e840d2f48b07a63","name":"Weather Research and Forecasting Model (WRF)","desc":"气象研究和预报（WRF）模型是下一代中尺度数值天气预报系统，旨在满足大气研究和运营预报需求。它具有两个动态核心，一个数据同化系统和一个允许并行计算和系统扩展的软件体系结构。该模型可用于范围从米到数千公里的各种气象应用。\n","classifications":["Meteorological"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Weather%20Research%20and%20%20%20Forecasting%20Model%20%20%20%28WRF%29.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.7.1","env":{},"loadCommand":"WRF/3.7.1-intel-2019b-dmpar"},{"additionalDesc":"","version":"4.1.3","env":{},"loadCommand":"module add WRF/4.1.3-intel-2019b-dmpar"},{"additionalDesc":"","version":"4.3","env":{},"loadCommand":"module add WRF/4.3-foss-2021a-dmpar"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ecb68593778cd50185ba57f","name":"运行MPICH软件","desc":"请到输入文件的选项中上传您的软件，使用命令运行您的软件。","classifications":["CustomSoftware"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bring%20Your%20Own%20MPICH%20%20%20Software.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"3.3.1/UCX 1.4.0","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.3.1","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.3/UCX 1.4.0","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.3","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.2.1","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.2","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.1.4","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.1","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.0.4","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""},{"additionalDesc":"","version":"3.0","env":{"MPI":"mpirun <your-mpi-program>"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":false},{"id":"5ecb695d3778cd2a7ce3b4bd","name":"运行OpenMPI软件","desc":"请到输入文件的选项中上传您的软件，使用命令运行您的软件。","classifications":["CustomSoftware"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bring%20Your%20Own%20%20%20OpenMPI%20Software.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"4.0.3","env":{"MPI":"srun -n $SLURM_NTASKS  <your-mpi-program>"},"loadCommand":"module add foss/2020a"},{"additionalDesc":"","version":"3.1.4","env":{"MPI":"srun -n $SLURM_NTASKS  <your-mpi-program>"},"loadCommand":"module add foss/2019b"},{"additionalDesc":"","version":"3.1.4-CUDA10.1","env":{"MPI":"srun -n $SLURM_NTASKS <your-mpi-program>"},"loadCommand":"module add fosscuda/2019b"},{"additionalDesc":"","version":"3.1.1-CUDA9.2","env":{"MPI":"srun -n $SLURM_NTASKS  <your-mpi-program>"},"loadCommand":"module add fosscuda/2018b"},{"additionalDesc":"","version":"3.1.1","env":{"MPI":"srun -n $SLURM_NTASKS  <your-mpi-program>"},"loadCommand":"module add foss/2018b"},{"additionalDesc":"","version":"1.10.3","env":{"MPI":"srun -n $SLURM_NTASKS  <your-mpi-program>"},"loadCommand":"module add foss/2016b"}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true},{"id":"5ecb61733778cd50f8e3162d","name":"运行自定义软件","desc":"请到输入文件的选项中上传您的软件，使用命令运行您的软件。","classifications":["CustomSoftware"],"owner":"hpcclient","system":null,"picUrl":"https://bkunyun-hpc-pic.oss-cn-shenzhen.aliyuncs.com/softwarepic/Bamtools.png","docUrl":null,"users":[],"status":null,"versionDetails":[{"additionalDesc":"","version":"CPU","env":{"STANDARD":"e.g. python myapp.py"},"loadCommand":""},{"additionalDesc":"","version":"CPU-多节点","env":{"STANDARD":"e.g. python myapp.py"},"loadCommand":""},{"additionalDesc":"","version":"GPU","env":{"STANDARD":"e.g. python myapp.py"},"loadCommand":""},{"additionalDesc":"","version":"GPU-多节点","env":{"STANDARD":"e.g. python myapp.py"},"loadCommand":""}],"labels":[],"favoriteTime":0,"createTime":0,"zoneId":null,"saleStatus":"ONSELL","softwares":[],"partnerDto":null,"productRateDto":null,"links":[],"actived":true}]