Accelerating Model Training in Multi-cluster Environments with Consumer-grade GPUs

Author:

Lim Hwijoon1ORCID,Ye Juncheol2ORCID,Abdu Jyothi Sangeetha3ORCID,Han Dongsu1ORCID

Affiliation:

1. KAIST, Daejeon, Korea, South ? Republic of Korea

2. KAIST, Daejeon, Republic of Korea

3. UC Irvine, VMware Research, Irvine, California, United States of America

Funder

National Research Foundation of Korea

Institute of Information & Communications Technology Planning & Evaluation (IITP)

Publisher

ACM

Reference56 articles.

1. DC2: Delay-aware Compression Control for Distributed Machine Learning

2. Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, and Dimitris Papailiopoulos. 2022. On the utility of gradient compression in distributed training systems. Proceedings of Machine Learning and Systems 4 (2022).

3. Alham Fikri Aji and Kenneth Heafield. 2017. Sparse communication for distributed gradient descent. arXiv preprint arXiv:1704.05021 (2017).

4. Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31 (2018).

5. The ZeroMQ authors. 2023. ZeroMQ. https://zeromq.org/.

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