THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression. (arXiv:2302.08545v1 [cs.LG])

Deep neural networks (DNNs) are the de-facto standard for essential use
cases, such as image classification, computer vision, and natural language
processing. As DNNs and datasets get larger, they require distributed training
on increasingly larger clusters. A main bottleneck is then the resulting
communication overhead where workers exchange model updates (i.e., gradients)
on a per-round basis. To address this bottleneck and accelerate training, a
widely-deployed approach is compression. However, previous deployments often
apply bi-directional compression schemes by simply using a uni-directional
gradient compression scheme in each direction. This results in significant
computational overheads at the parameter server and increased compression
error, leading to longer training and lower accuracy.

We introduce Tensor Homomorphic Compression (THC), a novel bi-directional
compression framework that enables the direct aggregation of compressed values
while optimizing the bandwidth to accuracy tradeoff, thus eliminating the
aforementioned overheads. Moreover, THC is compatible with in-network
aggregation (INA), which allows for further acceleration. Evaluation over a
testbed shows that THC improves time-to-accuracy in comparison to alternatives
by up to 1.32x with a software PS and up to 1.51x using INA. Finally, we
demonstrate that THC is scalable and tolerant for acceptable packet-loss rates.



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