# Sparsified SGD with Memory

@article{Stich2018SparsifiedSW, title={Sparsified SGD with Memory}, author={Sebastian U. Stich and Jean-Baptiste Cordonnier and Martin Jaggi}, journal={ArXiv}, year={2018}, volume={abs/1809.07599} }

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. Various recent works proposed to use quantization or sparsification techniques to reduce the amount of data that needs to be communicated, for instance by only sending the most significant entries of the stochastic gradient (top-k…

## 411 Citations

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## References

SHOWING 1-10 OF 46 REFERENCES

### Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

- Computer Science
- 2018

A sparsification scheme for SGD where only a small constant number of coordinates are applied at each iteration, which outperforms QSGD in progress per number of bits sent and opens the path to using lock-free asynchronous parallelization on dense problems.

### The Convergence of Sparsified Gradient Methods

- Computer ScienceNeurIPS
- 2018

It is proved that, under analytic assumptions, sparsifying gradients by magnitude with local error correction provides convergence guarantees, for both convex and non-convex smooth objectives, for data-parallel SGD.

### Local SGD Converges Fast and Communicates Little

- Computer ScienceICLR
- 2019

It is proved concise convergence rates for local SGD on convex problems and show that it converges at the same rate as mini-batch SGD in terms of number of evaluated gradients, that is, the scheme achieves linear speedup in the number of workers andmini-batch size.

### QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

- Computer ScienceNIPS
- 2017

Quantized SGD is proposed, a family of compression schemes for gradient updates which provides convergence guarantees and leads to significant reductions in end-to-end training time, and can be extended to stochastic variance-reduced techniques.

### Scalable distributed DNN training using commodity GPU cloud computing

- Computer ScienceINTERSPEECH
- 2015

It is shown empirically that the method can reduce the amount of communication by three orders of magnitude while training a typical DNN for acoustic modelling, and enables efficient scaling to more parallel GPU nodes than any other method that is aware of.

### Scaling SGD Batch Size to 32K for ImageNet Training

- Computer ScienceArXiv
- 2017

Layer-wise Adaptive Rate Scaling (LARS) is proposed, a method to enable large-batch training to general networks or datasets, and it can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet.

### meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

- Computer ScienceICML
- 2017

Surprisingly, experimental results demonstrate that the authors can update only 1-4% of the weights at each back propagation pass, and the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given.

### Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization

- Computer ScienceICML
- 2018

This paper proposes the error compensated quantized stochastic gradient descent algorithm to improve the training efficiency, and presents theoretical analysis on the convergence behaviour, and demonstrates its advantage over competitors.

### Gradient Sparsification for Communication-Efficient Distributed Optimization

- Computer ScienceNeurIPS
- 2018

This paper proposes a convex optimization formulation to minimize the coding length of stochastic gradients and experiments on regularized logistic regression, support vector machines, and convolutional neural networks validate the proposed approaches.

### TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

- Computer ScienceNIPS
- 2017

This work mathematically proves the convergence of TernGrad under the assumption of a bound on gradients, and proposes layer-wise ternarizing and gradient clipping to improve its convergence.