MPI Progress For All
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916321740980224 |
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| author | Zhou, Hui Latham, Robert Raffenetti, Ken Guo, Yanfei Thakur, Rajeev |
| author_facet | Zhou, Hui Latham, Robert Raffenetti, Ken Guo, Yanfei Thakur, Rajeev |
| contents | The progression of communication in the Message Passing Interface (MPI) is not well defined, yet it is critical for application performance, particularly in achieving effective computation and communication overlap. The opaque nature of MPI progress poses significant challenges in advancing MPI within modern high-performance computing (HPC) practices. Firstly, the lack of clarity hinders the development of explicit guidelines for enhancing computation and communication overlap in applications. Secondly, it prevents MPI from seamlessly integrating with contemporary programming paradigms, such as task-based runtimes and event-driven programming. Thirdly, it limits the extension of MPI functionalities from the user space. In this paper, we examine the role of MPI progress by analyzing the implementation details of MPI messaging. We then generalize the asynchronous communication pattern and identify key factors influencing application performance. Based on this analysis, we propose a set of MPI extensions designed to enable users to explicitly construct and manage an efficient progress engine. We provide example codes to demonstrate the use of these proposed APIs in achieving improved performance, adapting MPI to task-based or event-driven programming styles, and constructing collective algorithms that rival the performance of native implementations. Our approach is compared to previous efforts in the field, highlighting its reduced complexity and increased effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MPI Progress For All Zhou, Hui Latham, Robert Raffenetti, Ken Guo, Yanfei Thakur, Rajeev Distributed, Parallel, and Cluster Computing The progression of communication in the Message Passing Interface (MPI) is not well defined, yet it is critical for application performance, particularly in achieving effective computation and communication overlap. The opaque nature of MPI progress poses significant challenges in advancing MPI within modern high-performance computing (HPC) practices. Firstly, the lack of clarity hinders the development of explicit guidelines for enhancing computation and communication overlap in applications. Secondly, it prevents MPI from seamlessly integrating with contemporary programming paradigms, such as task-based runtimes and event-driven programming. Thirdly, it limits the extension of MPI functionalities from the user space. In this paper, we examine the role of MPI progress by analyzing the implementation details of MPI messaging. We then generalize the asynchronous communication pattern and identify key factors influencing application performance. Based on this analysis, we propose a set of MPI extensions designed to enable users to explicitly construct and manage an efficient progress engine. We provide example codes to demonstrate the use of these proposed APIs in achieving improved performance, adapting MPI to task-based or event-driven programming styles, and constructing collective algorithms that rival the performance of native implementations. Our approach is compared to previous efforts in the field, highlighting its reduced complexity and increased effectiveness. |
| title | MPI Progress For All |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.13807 |