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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/2511.02257 |
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| _version_ | 1866918184757493760 |
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| author | Selvitopi, Oguz Ozturk, Emin Chen, Jie Sadayappan, Ponnuswamy Edwards, Robert G. Buluç, Aydın |
| author_facet | Selvitopi, Oguz Ozturk, Emin Chen, Jie Sadayappan, Ponnuswamy Edwards, Robert G. Buluç, Aydın |
| contents | Computation of correlation functions is a key operation in Lattice quantum chromodynamics (LQCD) simulations to extract nuclear physics observables. These functions involve many binary batch tensor contractions, each tensor possibly occupying hundreds of MBs of memory. Performing these contractions on GPU accelerators poses the challenge of scheduling them as to optimize tensor reuse and reduce data traffic. In this work we propose two fast novel scheduling algorithms that reorder contractions to increase temporal locality via input/intermediate tensor reuse. Our schedulers take advantage of application-specific features, such as contractions being binary and locality within contraction trees, to optimize the objective of minimizing peak memory. We integrate them into the LQCD analysis software suite Redstar and improve time-to-solution. Our schedulers attain upto 2.1x improvement in peak memory, which is reflected by a reduction of upto 4.2x in evictions, upto 1.8x in data traffic, resulting in upto 1.9x faster correlation function computation time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast Algorithms for Scheduling Many-body Correlation Functions on Accelerators Selvitopi, Oguz Ozturk, Emin Chen, Jie Sadayappan, Ponnuswamy Edwards, Robert G. Buluç, Aydın Distributed, Parallel, and Cluster Computing Computation of correlation functions is a key operation in Lattice quantum chromodynamics (LQCD) simulations to extract nuclear physics observables. These functions involve many binary batch tensor contractions, each tensor possibly occupying hundreds of MBs of memory. Performing these contractions on GPU accelerators poses the challenge of scheduling them as to optimize tensor reuse and reduce data traffic. In this work we propose two fast novel scheduling algorithms that reorder contractions to increase temporal locality via input/intermediate tensor reuse. Our schedulers take advantage of application-specific features, such as contractions being binary and locality within contraction trees, to optimize the objective of minimizing peak memory. We integrate them into the LQCD analysis software suite Redstar and improve time-to-solution. Our schedulers attain upto 2.1x improvement in peak memory, which is reflected by a reduction of upto 4.2x in evictions, upto 1.8x in data traffic, resulting in upto 1.9x faster correlation function computation time. |
| title | Fast Algorithms for Scheduling Many-body Correlation Functions on Accelerators |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.02257 |