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Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation
This paper describes the new hardware-based streaming-aggregation capability added to Mellanox’s Scalable Hierarchical Aggregation and Reduction Protocol in its HDR InfiniBand switches. For large messages, this capability is designed to achieve reduction bandwidths similar to those of point-to-point...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295336/ http://dx.doi.org/10.1007/978-3-030-50743-5_3 |
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author | Graham, Richard L. Levi, Lion Burredy, Devendar Bloch, Gil Shainer, Gilad Cho, David Elias, George Klein, Daniel Ladd, Joshua Maor, Ophir Marelli, Ami Petrov, Valentin Romlet, Evyatar Qin, Yong Zemah, Ido |
author_facet | Graham, Richard L. Levi, Lion Burredy, Devendar Bloch, Gil Shainer, Gilad Cho, David Elias, George Klein, Daniel Ladd, Joshua Maor, Ophir Marelli, Ami Petrov, Valentin Romlet, Evyatar Qin, Yong Zemah, Ido |
author_sort | Graham, Richard L. |
collection | PubMed |
description | This paper describes the new hardware-based streaming-aggregation capability added to Mellanox’s Scalable Hierarchical Aggregation and Reduction Protocol in its HDR InfiniBand switches. For large messages, this capability is designed to achieve reduction bandwidths similar to those of point-to-point messages of the same size, and complements the latency-optimized low-latency aggregation reduction capabilities, aimed at small data reductions. MPI_Allreduce() bandwidth measured on an HDR InfiniBand based system achieves about 95% of network bandwidth. For medium and large data reduction this also improves the reduction bandwidth by a factor of 2–5 relative to host-based (e.g., software-based) reduction algorithms. Using this capability also increased DL-Poly and PyTorch application performance by as much as 4% and 18%, respectively. This paper describes SHARP Streaming-Aggregation hardware architecture and a set of synthetic and application benchmarks used to study this new reduction capability, and the range of data sizes for which Streaming-Aggregation performs better than the low-latency aggregation algorithm. |
format | Online Article Text |
id | pubmed-7295336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72953362020-06-16 Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation Graham, Richard L. Levi, Lion Burredy, Devendar Bloch, Gil Shainer, Gilad Cho, David Elias, George Klein, Daniel Ladd, Joshua Maor, Ophir Marelli, Ami Petrov, Valentin Romlet, Evyatar Qin, Yong Zemah, Ido High Performance Computing Article This paper describes the new hardware-based streaming-aggregation capability added to Mellanox’s Scalable Hierarchical Aggregation and Reduction Protocol in its HDR InfiniBand switches. For large messages, this capability is designed to achieve reduction bandwidths similar to those of point-to-point messages of the same size, and complements the latency-optimized low-latency aggregation reduction capabilities, aimed at small data reductions. MPI_Allreduce() bandwidth measured on an HDR InfiniBand based system achieves about 95% of network bandwidth. For medium and large data reduction this also improves the reduction bandwidth by a factor of 2–5 relative to host-based (e.g., software-based) reduction algorithms. Using this capability also increased DL-Poly and PyTorch application performance by as much as 4% and 18%, respectively. This paper describes SHARP Streaming-Aggregation hardware architecture and a set of synthetic and application benchmarks used to study this new reduction capability, and the range of data sizes for which Streaming-Aggregation performs better than the low-latency aggregation algorithm. 2020-05-22 /pmc/articles/PMC7295336/ http://dx.doi.org/10.1007/978-3-030-50743-5_3 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
spellingShingle | Article Graham, Richard L. Levi, Lion Burredy, Devendar Bloch, Gil Shainer, Gilad Cho, David Elias, George Klein, Daniel Ladd, Joshua Maor, Ophir Marelli, Ami Petrov, Valentin Romlet, Evyatar Qin, Yong Zemah, Ido Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title | Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title_full | Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title_fullStr | Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title_full_unstemmed | Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title_short | Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) Streaming-Aggregation Hardware Design and Evaluation |
title_sort | scalable hierarchical aggregation and reduction protocol (sharp)(tm) streaming-aggregation hardware design and evaluation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295336/ http://dx.doi.org/10.1007/978-3-030-50743-5_3 |
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