DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression (1905.05957v3)

Abstract: A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nodes on a network. Communication is a major bottleneck in such applications, and in recent years, compressed stochastic gradient methods such as QSGD (quantized SGD) and sparse SGD have been proposed to reduce communication. It was also shown that error compensation can be combined with compression to achieve better convergence in a scheme that each node compresses its local stochastic gradient and broadcast the result to all other nodes over the network in a single pass. However, such a single pass broadcast approach is not realistic in many practical implementations. For example, under the popular parameter server model for distributed learning, the worker nodes need to send the compressed local gradients to the parameter server, which performs the aggregation. The parameter server has to compress the aggregated stochastic gradient again before sending it back to the worker nodes. In this work, we provide a detailed analysis on this two-pass communication model and its asynchronous parallel variant, with error-compensated compression both on the worker nodes and on the parameter server. We show that the error-compensated stochastic gradient algorithm admits three very nice properties: 1) it is compatible with an \emph{arbitrary} compression technique; 2) it admits an improved convergence rate than the non error-compensated stochastic gradient methods such as QSGD and sparse SGD; 3) it admits linear speedup with respect to the number of workers. The empirical study is also conducted to validate our theoretical results.

Summary of "DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression"

The paper "DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression" tackles a significant challenge in distributed stochastic gradient descent (SGD) for large-scale machine learning: the communication bottleneck. This communication bottleneck, exacerbated in distributed systems with multiple nodes, is addressed through innovative compression strategies that aim to reduce the volume of data transmitted without significantly impacting the convergence rate.

Problem Statement and Methodology

The core problem addressed in this paper is the inefficiency in communication costs associated with distributed SGD when deployed across multiple worker nodes and a parameter server. Conventional one-pass compressed stochastic gradient algorithms show reduced performance due to the inherent information loss during compression. The authors propose a two-pass error-compensated compression strategy, dubbed DoubleSqueeze, which introduces compression not only at the worker nodes but also at the parameter server.

In this approach, each worker node compresses its local stochastic gradients and communicates these compressed gradients to the parameter server. The parameter server, in turn, compresses the aggregated gradients before disseminating them back to the worker nodes. This double-pass model is reinforced by error compensation mechanisms on both sides to mitigate the information loss during compression.

Key Results and Theoretical Contributions

The DoubleSqueeze algorithm presents three notable features:

1. Compatibility with Various Compression Schemes: The model allows for flexible implementation with different compression techniques, whether biased or unbiased.
2. Improved Convergence Rate: When analyzed theoretically, DoubleSqueeze achieves better convergence rates compared to traditional non-error-compensated methods like QSGD and sparse SGD, especially under high compression ratios.
3. Linear Speedup: The methodology supports linear speedup in relation to the number of workers, making it more scalable and efficient for real-world applications.

Two significant claims of the paper are the attainment of a convergence rate equivalent to that of non-compressed algorithms and reduced convergence time when operating under restricted bandwidth environments. These claims are empirically validated using the ResNet-18 model on the CIFAR-10 dataset, showing that DoubleSqueeze converges comparably to traditional SGD without compression yet operates with much lower time costs per iteration under low bandwidth conditions.

Practical Implications and Future Directions

Practically, the implications of DoubleSqueeze are significant for distributed learning systems where bandwidth is a critical constraint. By incorporating error-compensated compression at both the worker and server levels, the approach promises reduced communication costs while maintaining efficiency in model training.

Theoretically, the paper opens avenues for further exploration in parallel SDA models that integrate advanced data compression techniques with error compensation. Future research could extend this work to incorporate adaptive compression strategies based on network conditions or model complexity, potentially increasing the efficiency of distributed machine learning frameworks further. Additionally, exploring the application of DoubleSqueeze on diverse datasets and models could provide deeper insights into its adaptability and generalization capabilities.

Overall, the DoubleSqueeze algorithm represents a promising advancement in the field of distributed machine learning, particularly in terms of optimizing communication costs while retaining robust performance metrics.