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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.01958 |
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| _version_ | 1866912410755923968 |
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| author | Bendavid, Josh D'Alfonso, Mariarosaria Eysermans, Jan Freer, Chad Goncharov, Maxim Heine, Matthew Lavezzo, Luca Moore, Marianne Paus, Christoph Shen, Xuejian Walter, David Wang, Zhangqier |
| author_facet | Bendavid, Josh D'Alfonso, Mariarosaria Eysermans, Jan Freer, Chad Goncharov, Maxim Heine, Matthew Lavezzo, Luca Moore, Marianne Paus, Christoph Shen, Xuejian Walter, David Wang, Zhangqier |
| contents | The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated during the High-Luminosity phase of the Large Hadron Collider. The key features that make this facility so powerful include highly optimized data access with a minimum of 100Gbps networking per server, a large managed NVMe storage system, and a substantial spinning-disk Ceph file system. The platform integrates a diverse set of high multicore CPU machines for tasks benefiting from the multithreading and GPU resources for example for neural network training. SubMIT also provides and supports a flexible environment for users to manage their own software needs for example by using containers. This article describes the facility, its users, and a few complementary, generic and real-life analyses that are used to benchmark its various capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SubMIT: A Physics Analysis Facility at MIT Bendavid, Josh D'Alfonso, Mariarosaria Eysermans, Jan Freer, Chad Goncharov, Maxim Heine, Matthew Lavezzo, Luca Moore, Marianne Paus, Christoph Shen, Xuejian Walter, David Wang, Zhangqier Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated during the High-Luminosity phase of the Large Hadron Collider. The key features that make this facility so powerful include highly optimized data access with a minimum of 100Gbps networking per server, a large managed NVMe storage system, and a substantial spinning-disk Ceph file system. The platform integrates a diverse set of high multicore CPU machines for tasks benefiting from the multithreading and GPU resources for example for neural network training. SubMIT also provides and supports a flexible environment for users to manage their own software needs for example by using containers. This article describes the facility, its users, and a few complementary, generic and real-life analyses that are used to benchmark its various capabilities. |
| title | SubMIT: A Physics Analysis Facility at MIT |
| topic | Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.01958 |