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Scientific Computing and Data<\/a>\u00a0\/\u00a0High Performance Computing<\/a> \/ About HPC<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section bb_built=”1″ _builder_version=”3.22″ prev_background_color=”#000000″][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.27.4″ header_font=”|700|||||||” header_text_color=”#221f72″ header_2_text_color=”#221f72″ header_2_font_size=”24px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ sticky_enabled=”0″ text_text_shadow_horizontal_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_vertical_length_tablet=”0px” 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header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ z_index_tablet=”0″ background_pattern_color=”rgba(0,0,0,0.2)” background_mask_color=”#ffffff”]<\/p>\n Scientific Computing and Data partners with scientists to accelerate scientific discovery. Led by Dean for Scientific Computing and Data<\/span> Patricia Kovatch<\/a>, the Scientific Computing team provides tools, resources, and assistance to researchers across institutions around the globe.<\/span><\/p>\n Supported by grant UL1TR004419 from the National Center for Advancing Translational Sciences, National Institutes of Health.<\/span><\/p>\n \u00a0<\/p>\n The Minerva supercomputer is maintained by High Performance Computing (HPC). Minerva was created in 2012 and has been upgraded several times (most recently in Nov. 2024 and Feb. 2026) and has over 20 petaflops of computing power. It consists of 25,584 Intel Platinum processors in different generations including 2.1 GHz, 2.3 GHz, 2.6 GHz, and 2.9 GHz computing cores (112 cores or 96 cores or 64 cores or 48 cores per node with two sockets in each node) with 1.5 terabytes (TB) or 2 terabytes (TB) of memory per node, 408 graphical processing units (GPUs), including 48 Nvidia B200s, 236 Nvidia H100s, 32 Nvidia L40S, 44 Nvidia A100s, 48 Nvidia V100s, 452 TB of total memory, and 32 petabytes of spinning storage accessed via IBM\u2019s Spectrum Scale\/General Parallel File System (GPFS). Minerva has contributed to over 2,100 peer-reviewed publications since 2012.<\/span> Click here<\/a> for a quickstart guide to access Minerva directly. See also:<\/span><\/p>\n For Minerva User Training Classes and Archives,<\/span> click here<\/a>.<\/p>\n \u00a0<\/p>\n Through Minerva, researchers may access volumes of data:<\/p>\n Data Ark (Data Commons)<\/strong><\/a><\/p>\n The Data Ark team downloads, organizes and performs quality assurance and quality control on research data used by Mount Sinai data scientists. The team also manages the data access process, answers questions on the data, and updates to the latest versions of the data sets. Click here<\/a> to learn more about the Data Ark and how to access it.<\/p>\n Mount Sinai Data Warehouse (MSDW)<\/strong><\/a><\/p>\n The Mount Sinai Data Warehouse (MSDW) collects clinical and operational data for use in clinical and translational research, as well as quality and improvement initiatives. Click here<\/a> to learn more about MSDW and how to use it.<\/p>\n \u00a0<\/p>\n Scientific Computing and Data supports many avenues for conducting research:<\/p>\n \u00a0<\/p>\n Still have questions about High Performance Computing? Contact us at hpchelp@hpc.mssm.edu<\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" About High Performance Computing | Scientific Computing and Data | Icahn School of Medicine at Mount Sinai<\/p>\n","protected":false},"author":600,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"Scientific Computing and Data Science partners with scientists to accelerate scientific discovery. Led by Dean for Scientific Computing Patricia Kovatch<\/a>, the Scientific Computing team provides tools, resources, and assistance to researchers across institutions around the globe.\r\n\r\n\u00a0\r\nAbout High Performance Computing<\/h1>\n
Minerva High Performance Computing Platform<\/h2>\n
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Data Resources<\/h2>\n
Research Services<\/h2>\n
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Questions?<\/h1>\n
Minerva High Performance Computing Platform<\/h2>\r\nThe primary asset for Scientific Computing is the supercomputer Minerva. The HPC resource, Minerva, was created in 2012 and has been upgraded several times, most recently in Nov. 2024, utilizes 24,912 Intel Platinum in different generations including 8568Y+ 2.3GHz, 8358 2.6 GHz, and 8268 2.9 GHz compute cores (96 cores or 64 cores or 48 cores per node with two sockets in each node) with 1.5TB of memory per node, 196 H100 GPUs, 32 L40S, 40 A100 GPUs, 48 V100 GPUs, 440 terabytes of total memory, 32 petabytes of spinning storage accessed via IBM\u2019s Spectrum Scale\/General Parallel File System (GPFS) for a total of > 2 petaflops of CPU compute power and ~ 8 petaflops for GPU compute power. Minerva has contributed to over 1,700 peer-reviewed publications since 2012. Minerva cluster design is driven by the research demand performed by Minerva users (i.e. the number of nodes, the amount of memory per node, and the amount of disk space for storage).\r\n\r\nThe primary asset for Scientific Computing is the supercomputer Minerva. The HPC resource, upgraded in 2020, utilizes 14,304 Intel Gold 8168 24C, 2.7 GHz compute cores (48 cores per node with two sockets in each node), 286 nodes with 192 GB of memory per node, 65.7 terabytes of total memory, 350 terabytes of solid-state storage and nearly 30 petabytes of spinning storage accessed via IBM\u2019s Spectrum Scale\/General Parallel File System (GPFS) for a total of 1.2 petaflops of compute power. Minerva has contributed to over 1,200 peer-reviewed publications in seven years. Click here<\/a> for a quickstart guide to access Minerva directly. See also:\r\n
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Data Resources<\/h3>\r\nThrough Minerva, researchers may access volumes of data:\r\n\r\nData Ark (Data Commons)<\/strong>\r\n\r\nThe Data Ark team downloads, organizes and performs quality assurance and quality control on research data used by Mount Sinai data scientists. The team also manages the data access process, answers questions on the data, and updates to the latest versions of the data sets. Click here<\/a> to learn more about the Data Ark and how to access it.\r\n\r\nMount Sinai Data Warehouse (MSDW)<\/strong>\r\n\r\nThe Mount Sinai Data Warehouse (MSDW) collects clinical, operational, and financial data for use in clinical and translational research, as well as quality and improvement initiatives. Click here<\/a> to learn more about MSDW and how to use it.\r\n\r\n\u00a0\r\n
Research Services<\/h3>\r\nScientific Computing and Data Science supports many avenues for conducting research:\r\n
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Questions?<\/h3>\r\nStill have questions about High Performance Computing? Contact us at hpchelp@hpc.mssm.edu<\/a>\r\n\r\n\u00a0\r\n\r\n\u00a0\r\n\r\n\u00a0","_et_gb_content_width":"","footnotes":""},"class_list":["post-2044","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/2044","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/users\/600"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/comments?post=2044"}],"version-history":[{"count":53,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/2044\/revisions"}],"predecessor-version":[{"id":13420,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/2044\/revisions\/13420"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/media?parent=2044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}