Massive amounts of data have led to the training of large-scale machine
learning models on a single worker inefficient. Distributed machine learning
methods such as Parallel-SGD have received significant interest as a solution
to tackle this problem. However, the performance of distributed systems does
not scale linearly with the number of workers due to the high network
communication cost for synchronizing gradients and parameters. Researchers have
proposed techniques such as quantization and sparsification to alleviate this
problem by compressing the gradients. Most of the compression schemes result in
compressed gradients that cannot be directly aggregated with efficient
protocols such as all-reduce. In this paper, we present a set of all-reduce
compatible gradient compression schemes which significantly reduce the
communication overhead while maintaining the performance of vanilla SGD. We
present the results of our experiments with the CIFAR10 dataset and
observations derived during the process. Our compression methods perform better
than the in-built methods currently offered by the deep learning frameworks.
Code is available at the repository:
https://github.com/vineeths96/Gradient-Compression.

本文提出了一组全局归约兼容的梯度压缩方案，通过压缩梯度实现降低通信开销的目的，并取得比当前深度学习框架提供的方法更好的表现效果。

分布式优化的无偏单尺度和多尺度量化器

Unbiased Single-scale and Multi-scale Quantizers for Distributed  Optimization

We propose a family of adaptive integer compression operators for distributed
Stochastic Gradient Descent (SGD) that do not communicate a single float. This
is achieved by multiplying floating-point vectors with a number known to every
device and then rounding to integers. In contrast to the prior work on integer
compression for SwitchML by Sapio et al. (2021), our IntSGD method is provably
convergent and computationally cheaper as it estimates the scaling of vectors
adaptively. Our theory shows that the iteration complexity of IntSGD matches
that of SGD up to constant factors for both convex and non-convex, smooth and
non-smooth functions, with and without overparameterization. Moreover, our
algorithm can also be tailored for the popular all-reduce primitive and shows
promising empirical performance.

本文提出一组适用于分布式随机梯度下降的自适应整数压缩算子，可通过适应性地估计向量的缩放来达到与 SGD 相同的迭代复杂度，并且也可用于 all-reduce，并在实验中取得了良好的效果。

IntSGD: 随机梯度的自适应无浮点压缩

IntSGD: Adaptive Floatless Compression of Stochastic Gradients

We study gradient compression methods to alleviate the communication
bottleneck in data-parallel distributed optimization. Despite the significant
attention received, current compression schemes either do not scale well or
fail to achieve the target test accuracy. We propose a new low-rank gradient
compressor based on power iteration that can i) compress gradients rapidly, ii)
efficiently aggregate the compressed gradients using all-reduce, and iii)
achieve test performance on par with SGD. The proposed algorithm is the only
method evaluated that achieves consistent wall-clock speedups when benchmarked
against regular SGD with an optimized communication backend. We demonstrate
reduced training times for convolutional networks as well as LSTMs on common
datasets. Our code is available at this https URL

本文研究了梯度压缩方法以缓解数据并行分布式优化中的通信瓶颈，提出了一种基于幂迭代的新型低秩梯度压缩器，在快速压缩梯度、高效聚合压缩梯度并且在测试性能上与随机梯度下降（SGD）相当的情况下实现了持续的加速，该算法是唯一评估的方法，与具有优化的通信后端的常规 SGD 进行基准测试时实现了一致的挂钟加速，并且在常见数据集上展示了卷积网络以及 LSTM 的训练时间缩短。