UNR: Unified Notifiable RMA Library for HPC
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911988173504512 |
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| author | Feng, Guangnan Xie, Jiabin Dong, Dezun Lu, Yutong |
| author_facet | Feng, Guangnan Xie, Jiabin Dong, Dezun Lu, Yutong |
| contents | Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes for the remote process to detect the completion of RMA operations. Many previous works have proposed programming models and extensions to notify the communication peer, but they did not solve the multi-NIC aggregation, portability, hardware-software co-design, and usability problems. In this work, we proposed a Unified Notifiable RMA (UNR) library for HPC to address these challenges. In addition, we demonstrate the best practice of utilizing UNR within a real-world scientific application, PowerLLEL. We deployed UNR across four HPC systems, each with a different interconnect. The results show that PowerLLEL powered by UNR achieves up to a 36% acceleration on 1728 nodes of the Tianhe-Xingyi supercomputing system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UNR: Unified Notifiable RMA Library for HPC Feng, Guangnan Xie, Jiabin Dong, Dezun Lu, Yutong Distributed, Parallel, and Cluster Computing Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes for the remote process to detect the completion of RMA operations. Many previous works have proposed programming models and extensions to notify the communication peer, but they did not solve the multi-NIC aggregation, portability, hardware-software co-design, and usability problems. In this work, we proposed a Unified Notifiable RMA (UNR) library for HPC to address these challenges. In addition, we demonstrate the best practice of utilizing UNR within a real-world scientific application, PowerLLEL. We deployed UNR across four HPC systems, each with a different interconnect. The results show that PowerLLEL powered by UNR achieves up to a 36% acceleration on 1728 nodes of the Tianhe-Xingyi supercomputing system. |
| title | UNR: Unified Notifiable RMA Library for HPC |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2408.07428 |