Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression

• URL: http://arxiv.org/abs/2407.01378v1
• Date: Mon, 1 Jul 2024 15:32:28 GMT
• Title: Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression
• Authors: Wenchen Han, Shay Vargaftik, Michael Mitzenmacher, Brad Karp, Ran Ben Basat,
• Abstract summary: gradient compression is a promising solution to gradient aggregation bottlenecks. However, gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. In this work, we identify several common issues in previous gradient compression systems and evaluation methods. We propose several general design and evaluation techniques to address these issues and provide guidelines for future work.
• Score: 13.255861297820326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bottlenecks is gradient compression, directly reducing communicated gradient data volume. However, in practice, many gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. In this work, we identify several common issues in previous gradient compression systems and evaluation methods. These issues include excessive computational overheads; incompatibility with all-reduce; and inappropriate evaluation metrics, such as not using an end-to-end metric or using a 32-bit baseline instead of a 16-bit baseline. We propose several general design and evaluation techniques to address these issues and provide guidelines for future work. Our preliminary evaluation shows that our techniques enhance the system's performance and provide a clearer understanding of the end-to-end utility of gradient compression methods.

Related papers

• Unrolled Compressed Blind-Deconvolution [77.88847247301682]
  sparse multichannel blind deconvolution (S-MBD) arises frequently in many engineering applications such as radar/sonar/ultrasound imaging. We propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
  arXiv  Detail & Related papers  (2022-09-28T15:16:58Z)
• Communication-Efficient Federated Learning via Quantized Compressed Sensing [82.10695943017907]
  The presented framework consists of gradient compression for wireless devices and gradient reconstruction for a parameter server. Thanks to gradient sparsification and quantization, our strategy can achieve a higher compression ratio than one-bit gradient compression. We demonstrate that the framework achieves almost identical performance with the case that performs no compression.
  arXiv  Detail & Related papers  (2021-11-30T02:13:54Z)
• Wyner-Ziv Gradient Compression for Federated Learning [4.619828919345114]
  Gradient compression is an effective method to reduce communication load by transmitting compressed gradients. This paper proposes a practical gradient compression scheme for federated learning, which uses historical gradients to compress gradients. We also implement our gradient quantization method on the real dataset, and the performance of our method is better than the previous schemes.
  arXiv  Detail & Related papers  (2021-11-16T07:55:43Z)
• 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)
• CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation [3.0786359925181315]
  Communication overhead is the key challenge for distributed computation training. gradient compression technique can greatly alleviate the impact of communication overhead. However, gradient compression brings in extra cost, which will delay the next training iteration.
  arXiv  Detail & Related papers  (2021-06-21T01:15:12Z)
• 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)
• Analyzing and Mitigating JPEG Compression Defects in Deep Learning [69.04777875711646]
  We present a unified study of the effects of JPEG compression on a range of common tasks and datasets. We show that there is a significant penalty on common performance metrics for high compression.
  arXiv  Detail & Related papers  (2020-11-17T20:32:57Z)
• 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)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

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.