An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems

• URL: http://arxiv.org/abs/2101.10761v1
• Date: Tue, 26 Jan 2021 13:06:00 GMT
• Title: An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
• Authors: Ahmed M. Abdelmoniem and Ahmed Elzanaty and Mohamed-Slim Alouini and Marco Canini
• Abstract summary: Sparsity-Inducing Distribution-based Compression (SIDCo) is a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) Our evaluation shows SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
• Score: 77.88178159830905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent many-fold increase in the size of deep neural networks makes efficient distributed training challenging. Many proposals exploit the compressibility of the gradients and propose lossy compression techniques to speed up the communication stage of distributed training. Nevertheless, compression comes at the cost of reduced model quality and extra computation overhead. In this work, we design an efficient compressor with minimal overhead. Noting the sparsity of the gradients, we propose to model the gradients as random variables distributed according to some sparsity-inducing distributions (SIDs). We empirically validate our assumption by studying the statistical characteristics of the evolution of gradient vectors over the training process. We then propose Sparsity-Inducing Distribution-based Compression (SIDCo), a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) while being faster by imposing lower compression overhead. Our extensive evaluation of popular machine learning benchmarks involving both recurrent neural network (RNN) and convolution neural network (CNN) models shows that SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.

Related papers

• Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance [55.01966743652196]
  We propose a novel algorithm for distributed gradient descent (SGD) with compressed gradient communication in the parameter-server framework. Our gradient compression technique, named flattened one-bit gradient descent (FO-SGD), relies on two simple algorithmic ideas.
  arXiv  Detail & Related papers  (2024-05-17T21:17:27Z)
• Accelerating Distributed Deep Learning using Lossless Homomorphic Compression [17.654138014999326]
  We introduce a novel compression algorithm that effectively merges worker-level compression with in-network aggregation. We show up to a 6.33$times$ improvement in aggregation throughput and a 3.74$times$ increase in per-iteration training speed.
  arXiv  Detail & Related papers  (2024-02-12T09:57:47Z)
• L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep Learning [24.712888488317816]
  We provide a framework for adapting the degree of compression across the model's layers dynamically during training. Our framework, called L-GreCo, is based on an adaptive algorithm, which automatically picks the optimal compression parameters for model layers.
  arXiv  Detail & Related papers  (2022-10-31T14:37:41Z)
• Quantization for Distributed Optimization [0.0]
  We present a set of all-reduce gradient compatible compression schemes which significantly reduce the communication overhead while maintaining the performance of vanilla SGD. Our compression methods perform better than the in-built methods currently offered by the deep learning frameworks.
  arXiv  Detail & Related papers  (2021-09-26T05:16:12Z)
• Compressed Communication for Distributed Training: Adaptive Methods and System [13.244482588437972]
  Communication overhead severely hinders the scalability of distributed machine learning systems. Recently, there has been a growing interest in using gradient compression to reduce the communication overhead. In this paper, we first introduce a novel adaptive gradient method with gradient compression.
  arXiv  Detail & Related papers  (2021-05-17T13:41:47Z)
• ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training [74.43625662170284]
  Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. We propose a new compression technique that leverages similarity in the gradient distribution amongst learners to provide significantly improved scalability. We experimentally demonstrate that ScaleCom has small overheads, directly reduces gradient traffic and provides high compression rates (65-400X) and excellent scalability (up to 64 learners and 8-12X larger batch sizes over standard training) without significant accuracy loss.
  arXiv  Detail & Related papers  (2021-04-21T02:22:10Z)
• On the Utility of Gradient Compression in Distributed Training Systems [9.017890174185872]
  We evaluate the efficacy of gradient compression methods and compare their scalability with optimized implementations of synchronous data-parallel SGD. Surprisingly, we observe that due to computation overheads introduced by gradient compression, the net speedup over vanilla data-parallel training is marginal, if not negative.
  arXiv  Detail & Related papers  (2021-02-28T15:58:45Z)
• Sparse Communication for Training Deep Networks [56.441077560085475]
  Synchronous gradient descent (SGD) is the most common method used for distributed training of deep learning models. In this algorithm, each worker shares its local gradients with others and updates the parameters using the average gradients of all workers. We study several compression schemes and identify how three key parameters affect the performance.
  arXiv  Detail & Related papers  (2020-09-19T17:28:11Z)
• PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning [62.440827696638664]
  We introduce a simple algorithm that directly compresses the model differences between neighboring workers. Inspired by the PowerSGD for centralized deep learning, this algorithm uses power steps to maximize the information transferred per bit.
  arXiv  Detail & Related papers  (2020-08-04T09:14:52Z)
• Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle [117.95823660228537]
  We propose a novel structured sparsification method for efficient network compression. The proposed method automatically induces structured sparsity on the convolutional weights. We also address the problem of inter-group communication with a learnable channel shuffle mechanism.
  arXiv  Detail & Related papers  (2020-02-19T12:03:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.