Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929551866593280 |
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| author | Tallent, Nathan Strube, Jan Guo, Luanzheng Lee, Hyungro Firoz, Jesun Ghosh, Sayan Fang, Bo Bel, Oceane Spurgeon, Steven Akers, Sarah Doty, Christina Cromwell, Erol |
| author_facet | Tallent, Nathan Strube, Jan Guo, Luanzheng Lee, Hyungro Firoz, Jesun Ghosh, Sayan Fang, Bo Bel, Oceane Spurgeon, Steven Akers, Sarah Doty, Christina Cromwell, Erol |
| contents | Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information sources, and potentially cloud. Unfortunately, the obvious methods for constructing continuum platforms, orchestrating workflow tasks, and curating datasets over time fail to achieve scientific requirements for performance, energy, security, and reliability. Furthermore, achieving the best use of continuum resources depends upon the efficient composition and execution of workflow tasks, i.e., combinations of numerical solvers, data analytics, and machine learning. Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets. This report describes the results and successes of CHESS from the perspective of open science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16093 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security Tallent, Nathan Strube, Jan Guo, Luanzheng Lee, Hyungro Firoz, Jesun Ghosh, Sayan Fang, Bo Bel, Oceane Spurgeon, Steven Akers, Sarah Doty, Christina Cromwell, Erol Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition Performance Systems and Control C.2.4; C.4; D.1.3; J.2; K.6.4 Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information sources, and potentially cloud. Unfortunately, the obvious methods for constructing continuum platforms, orchestrating workflow tasks, and curating datasets over time fail to achieve scientific requirements for performance, energy, security, and reliability. Furthermore, achieving the best use of continuum resources depends upon the efficient composition and execution of workflow tasks, i.e., combinations of numerical solvers, data analytics, and machine learning. Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets. This report describes the results and successes of CHESS from the perspective of open science. |
| title | Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security |
| topic | Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition Performance Systems and Control C.2.4; C.4; D.1.3; J.2; K.6.4 |
| url | https://arxiv.org/abs/2410.16093 |