Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors

TL;DR

This paper systematically evaluates the performance of Transformer-based large language models on Habana GAUDI processors, providing insights and optimization strategies for efficient deployment on specialized AI hardware.

Contribution

It offers a comprehensive performance comparison, optimization strategies, and end-to-end evaluation of Transformers on GAUDI, addressing a research gap in hardware acceleration.

Findings

GAUDI's MME and TPC have distinct strengths and weaknesses

Optimization strategies improve Transformer performance on GAUDI

End-to-end LLM performance on GAUDI reveals key bottlenecks

Abstract

Transformer models have achieved remarkable success in various machine learning tasks but suffer from high computational complexity and resource requirements. The quadratic complexity of the self-attention mechanism further exacerbates these challenges when dealing with long sequences and large datasets. Specialized AI hardware accelerators, such as the Habana GAUDI architecture, offer a promising solution to tackle these issues. GAUDI features a Matrix Multiplication Engine (MME) and a cluster of fully programmable Tensor Processing Cores (TPC). This paper explores the untapped potential of using GAUDI processors to accelerate Transformer-based models, addressing key challenges in the process. Firstly, we provide a comprehensive performance comparison between the MME and TPC components, illuminating their relative strengths and weaknesses. Secondly, we explore strategies to optimize MME and TPC utilization, offering practical insights to enhance computational efficiency. Thirdly, we evaluate the performance of Transformers on GAUDI, particularly in handling long sequences and uncovering performance bottlenecks. Lastly, we evaluate the end-to-end performance of two Transformer-based large language models (LLM) on GAUDI. The contributions of this work encompass practical insights for practitioners and researchers alike. We delve into GAUDI's capabilities for Transformers through systematic profiling, analysis, and optimization exploration. Our study bridges a research gap and offers a roadmap for optimizing Transformer-based model training on the GAUDI architecture.