Breaking the computation and communication abstraction barrier in distributed machine learning workloads-Reference-Cited by-同舟云学术

Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Published:2022-02-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
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Author:

Jangda Abhinav¹,Huang Jun²,Liu Guodong³,Sabet Amir Hossein Nodehi⁴,Maleki Saeed⁵,Miao Youshan⁶^ORCID,Musuvathi Madanlal⁵,Mytkowicz Todd⁵,Saarikivi Olli⁵

Affiliation:

1. University of Massachusetts at Amherst, USA

2. Ohio State University, USA

3. Chinese Academy of Sciences, China

4. University of California at Riverside, USA

5. Microsoft Research, USA

6. Microsoft Research, China

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503222.3507778

Reference53 articles.

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3. Accessed: 2022-01-12. cuDNN. https://docs.nvidia.com/cuda/cudnn/index.html Accessed: 2022-01-12. cuDNN. https://docs.nvidia.com/cuda/cudnn/index.html

4. Accessed: 2022-01-12. CUTLASS. https://github.com/NVIDIA/cutlass Accessed: 2022-01-12. CUTLASS. https://github.com/NVIDIA/cutlass

5. Accessed: 2022-01-12. GPUDirect RDMA. https://docs.nvidia.com/cuda/gpudirect-rdma/index.html Accessed: 2022-01-12. GPUDirect RDMA. https://docs.nvidia.com/cuda/gpudirect-rdma/index.html

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