Accelerating MPI All-to-All Communication with Online Compression on Modern GPU Clusters-Reference-Cited by-同舟云学术

Accelerating MPI All-to-All Communication with Online Compression on Modern GPU Clusters

Published:2022 Issue: Volume: Page:3-25
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Zhou Qinghua,Kousha Pouya,Anthony Quentin,Shafie Khorassani Kawthar,Shafi Aamir,Subramoni Hari,Panda Dhabaleswar K.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-07312-0_1

Reference32 articles.

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2. Chu, C.H., Kousha, P., Awan, A.A., Khorassani, K.S., Subramoni, H., Panda, D.K.: NV-group: link-efficient reduction for distributed deep learning on modern dense GPU systems. In: Proceedings of the 34th ACM International Conference on Supercomputing (2020)

3. Di, S., Cappello, F.: Fast error-bounded lossy HPC data compression with SZ. In: International Parallel and Distributed Processing Symposium (IPDPS) (2016)

4. Filgueira, R., Singh, D., Calderón, A., Carretero, J.: CoMPI: enhancing MPI based applications performance and scalability using run-time compression. In: European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, pp. 207–218 (2009)

5. IBM: IBM Spectrum MPI: accelerating high-performance application parallelization (2018). https://www.ibm.com/us-en/marketplace/spectrum-mpi. Accessed 13 May 2022

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