Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression

Centrala begrepp

The author introduces Tensor Homomorphic Compression (THC) as a novel bi-directional compression framework to address communication overhead in distributed deep learning. THC enables direct aggregation of compressed values, eliminating computational overhead and improving training efficiency.

Sammanfattning

The content discusses the introduction of Tensor Homomorphic Compression (THC) to accelerate distributed deep learning by reducing communication overhead. THC allows for faster training with target accuracy and is compatible with in-network aggregation for further acceleration. The article details the implementation, benefits, optimizations, and evaluation of THC in various scenarios.

Key points:

• Introduction of THC to address communication overhead in distributed deep learning.
• Benefits of THC include faster training with target accuracy and compatibility with in-network aggregation.
• Implementation details for workers and parameter servers.
• Optimizations using Randomized Hadamard Transform (RHT) and optimal lookup table construction.
• Evaluation results showing improved time-to-accuracy and throughput compared to state-of-the-art systems.

Statistik

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.

Citat

"We introduce Tensor Homomorphic Compression (THC), a novel bi-directional compression framework that enables the direct aggregation of compressed values."
"Our evaluation shows that training representative vision and language models with THC reaches target accuracy by 1.40× to 1.47× faster using INA."

Viktiga insikter från

THC

by Minghao Li (... på arxiv.org 03-07-2024

https://arxiv.org/pdf/2302.08545.pdf

Djupare frågor

How does THC compare to other compression schemes like DGC, TopK, and TernGrad

THC outperforms other compression schemes like DGC, TopK, and TernGrad in several aspects. Firstly, THC introduces a novel bi-directional compression framework that enables direct aggregation of compressed values without the need for decompression at the parameter server (PS). This eliminates computational overheads and ensures high accuracy during training. In contrast, DGC and TopK are sparsification algorithms that only communicate the top k% of coordinates by magnitude, leading to potential loss of information and reduced accuracy. TernGrad is a quantization algorithm that converts each coordinate into a value x ∈ {−1,0,1}, which may result in larger quantization errors compared to THC.

What are the implications of implementing THC on programmable switches for hardware acceleration

Implementing THC on programmable switches offers significant opportunities for hardware acceleration. By offloading the PS completely to programmable switches, THC simplifies the PS operations as it removes compression and decompression tasks. Since THC compresses floating-point gradients into integer table indices suitable for switches' processing capabilities, it aligns well with programmable switch architectures. The use of programmable switches allows for efficient lookup table operations and value aggregation without additional conversions from floating points to integers at workers.

How does THC handle packet loss and stragglers during distributed training

THC is designed to handle packet loss and stragglers effectively during distributed training sessions. In cases where there is data loss between workers and the PS or when stragglers delay communication processes, THC can tolerate such issues by ignoring outliers caused by these problems. Workers can fill in missing data with zeros if they do not receive corresponding aggregation results within a specified time threshold due to packet loss or delays from stragglers. Additionally, implementing partial aggregation strategies where the PS broadcasts partial results once it hears from the majority of workers can help mitigate impacts on model accuracy caused by packet losses or straggler issues.