LLload: An Easy-to-Use HPC Utilization Tool
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910904794218496 |
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| author | Byun, Chansup Reuther, Albert Mullen, Julie Anderson, LaToya Arcand, William Bergeron, Bill Bestor, David Bonn, Alexander Burrill, Daniel Gadepally, Vijay Houle, Michael Hubbell, Matthew Jananthan, Hayden Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Morales, Guillermo Prout, Andrew Rosa, Antonio Yee, Charles Kepner, Jeremy |
| author_facet | Byun, Chansup Reuther, Albert Mullen, Julie Anderson, LaToya Arcand, William Bergeron, Bill Bestor, David Bonn, Alexander Burrill, Daniel Gadepally, Vijay Houle, Michael Hubbell, Matthew Jananthan, Hayden Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Morales, Guillermo Prout, Andrew Rosa, Antonio Yee, Charles Kepner, Jeremy |
| contents | The increasing use and cost of high performance computing (HPC) requires new easy-to-use tools to enable HPC users and HPC systems engineers to transparently understand the utilization of resources. The MIT Lincoln Laboratory Supercomputing Center (LLSC) has developed a simple command, LLload, to monitor and characterize HPC workloads. LLload plays an important role in identifying opportunities for better utilization of compute resources. LLload can be used to monitor jobs both programmatically and interactively. LLload can characterize users' jobs using various LLload options to achieve better efficiency. This information can be used to inform the user to optimize HPC workloads and improve both CPU and GPU utilization. This includes improvements using judicious oversubscription of the computing resources. Preliminary results suggest significant improvement in GPU utilization and overall throughput performance with GPU overloading in some cases. By enabling users to observe and fix incorrect job submission and/or inappropriate execution setups, LLload can increase the resource usage and improve the overall throughput performance. LLload is a light-weight, easy-to-use tool for both HPC users and HPC systems engineers to monitor HPC workloads to improve system utilization and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLload: An Easy-to-Use HPC Utilization Tool Byun, Chansup Reuther, Albert Mullen, Julie Anderson, LaToya Arcand, William Bergeron, Bill Bestor, David Bonn, Alexander Burrill, Daniel Gadepally, Vijay Houle, Michael Hubbell, Matthew Jananthan, Hayden Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Morales, Guillermo Prout, Andrew Rosa, Antonio Yee, Charles Kepner, Jeremy Performance The increasing use and cost of high performance computing (HPC) requires new easy-to-use tools to enable HPC users and HPC systems engineers to transparently understand the utilization of resources. The MIT Lincoln Laboratory Supercomputing Center (LLSC) has developed a simple command, LLload, to monitor and characterize HPC workloads. LLload plays an important role in identifying opportunities for better utilization of compute resources. LLload can be used to monitor jobs both programmatically and interactively. LLload can characterize users' jobs using various LLload options to achieve better efficiency. This information can be used to inform the user to optimize HPC workloads and improve both CPU and GPU utilization. This includes improvements using judicious oversubscription of the computing resources. Preliminary results suggest significant improvement in GPU utilization and overall throughput performance with GPU overloading in some cases. By enabling users to observe and fix incorrect job submission and/or inappropriate execution setups, LLload can increase the resource usage and improve the overall throughput performance. LLload is a light-weight, easy-to-use tool for both HPC users and HPC systems engineers to monitor HPC workloads to improve system utilization and efficiency. |
| title | LLload: An Easy-to-Use HPC Utilization Tool |
| topic | Performance |
| url | https://arxiv.org/abs/2410.21036 |