CGX: Adaptive System Support For Communication-Efficient Deep ...

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2111.08617 (cs) [Submitted on 16 Nov 2021 (v1), last revised 29 Dec 2022 (this version, v5)] Title:CGX: Adaptive System Support for Communication-Efficient Deep Learning Authors:Ilia Markov, Hamidreza Ramezanikebrya, Dan Alistarh View a PDF of the paper titled CGX: Adaptive System Support for Communication-Efficient Deep Learning, by Ilia Markov and 2 other authors View PDF

Abstract:The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth overprovisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between "cloud-grade" servers with such support, relative to their popular "consumer-grade" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: \emph{At the system level}, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. \emph{At the application level}, it provides \emph{seamless, parameter-free} integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a \emph{layer-wise adaptive compression} technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.

Subjects:	Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
Cite as:	arXiv:2111.08617 [cs.DC]
(or arXiv:2111.08617v5 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2111.08617 Focus to learn more arXiv-issued DOI via DataCite
Journal reference:	Middleware 2022
Related DOI:	https://doi.org/10.1145/3528535.3565248 Focus to learn more DOI(s) linking to related resources

Submission history

From: Ilia Markov [view email] [v1] Tue, 16 Nov 2021 17:00:42 UTC (222 KB) [v2] Wed, 17 Nov 2021 14:00:02 UTC (222 KB) [v3] Fri, 4 Feb 2022 16:33:14 UTC (242 KB) [v4] Tue, 24 May 2022 21:15:50 UTC (268 KB) [v5] Thu, 29 Dec 2022 15:07:04 UTC (1,375 KB) Full-text links:

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