Tensor Homomorphic Compression (THC) for Accelerating Distributed Deep Learning

핵심 개념
THC introduces a novel bi-directional compression framework to accelerate distributed deep learning by eliminating computational overheads and improving accuracy.
Deep neural networks (DNNs) require distributed training on larger clusters as models and datasets grow. Compression schemes like THC reduce communication overhead and accelerate training. THC enables direct aggregation of compressed values, eliminating computational overhead. THC is compatible with in-network aggregation for further acceleration. Evaluation shows THC achieves target accuracy faster compared to state-of-the-art systems. THC simplifies the Parameter Server (PS) architecture and improves training throughput. THC can handle packet loss and stragglers, ensuring model accuracy and convergence. Implementation includes GPU-based compression, RHT pre-processing, and optimal lookup table construction. Evaluation on computer vision and language models demonstrates the effectiveness of THC.
"Our evaluation shows that training representative vision and language models with THC reaches target accuracy by 1.40× to 1.47× faster using INA and 1.28× to 1.33× faster using a software PS compared with state-of-the-art systems." "THC with the programmable switch also improves the training throughput by up to 54% over Horovod RDMA."
"THC introduces Tensor Homomorphic Compression, a novel bi-directional compression framework that enables the direct aggregation of compressed values." "THC achieves the target accuracy faster using INA and a software PS compared to state-of-the-art systems."

에서 추출된 핵심 인사이트

by Minghao Li (... 에서 03-07-2024

더 깊은 문의

How does THC handle packet loss and stragglers to ensure model accuracy and convergence

THC handles packet loss and stragglers by incorporating mechanisms to mitigate their impact on model accuracy and convergence. In the case of packet loss, THC can ignore the outliers caused by missing data, ensuring that the training process continues smoothly without being significantly affected by the lost information. Workers can fill in the missing data with zeros and proceed with the received aggregation results, minimizing the impact of packet loss on model training. Additionally, THC implements a synchronization scheme where workers coordinate their model parameters after each epoch to address severe packet loss scenarios. This scheme allows workers to align their model parameters and compensate for any discrepancies that may arise due to missing data.

What are the implications of THC's compatibility with in-network aggregation for further acceleration

The compatibility of THC with in-network aggregation presents an opportunity for further acceleration of the training process. By leveraging in-network aggregation, THC can offload the parameter server to programmable switches, enhancing hardware acceleration and optimizing the communication process. With THC's compression of floating-point gradients into integer table indices, it seamlessly integrates with programmable switches, eliminating the need for additional conversions and streamlining the communication flow. This compatibility with in-network aggregation allows THC to leverage the capabilities of programmable switches for efficient data processing and communication, ultimately leading to improved training performance and throughput.

How does THC's optimization of the lookup table contribute to minimizing quantization error and improving model accuracy

The optimization of the lookup table in THC plays a crucial role in minimizing quantization error and enhancing model accuracy. By constructing an optimal lookup table that minimizes the error in quantizing truncated normal random variables, THC ensures that the quantization process is optimized for accuracy. The lookup table is designed to reduce the error in quantizing values within a specified range, allowing for precise quantization of transformed coordinates. Through this optimization, THC effectively minimizes the quantization error of the transformed vectors, leading to improved model accuracy and convergence during the training process.