gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters
Author:
Affiliation:
1. University of California, Riverside, USA
2. Argonne National Laboratory, USA
3. Stevens Institute of Technology, USA
4. University of Iowa, USA
5. Florida State University, USA
6. University of California, Merced, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3650200.3656636
Reference31 articles.
1. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: A system for large-scale machine learning. In 12th { USENIX} symposium on operating systems design and implementation ({ OSDI} 16). 265–283.
2. Ahmed M. Abdelmoniem Ahmed Elzanaty Mohamed-Slim Alouini and Marco Canini. 2021. An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems. arxiv:2101.10761 [cs.LG]
3. Optimization of MPI collective communication on BlueGene/L systems
4. S-Caffe
5. Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation
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