Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference

TL;DR

This paper presents a comprehensive performance modeling framework for distributed large language model training and inference, analyzing hardware, parallelization strategies, and technology scaling impacts to guide future system design.

Contribution

It introduces a general analytical framework that accurately predicts performance considering compute, memory, network, and parallelization, validated with industry data and applied to technology scaling analysis.

Findings

Performance bottlenecks evolve with technology scaling.

Memory footprint and activation re-computation significantly impact training speed.

DRAM scaling influences inference latency.

Abstract

Aligning future system design with the ever-increasing compute needs of large language models (LLMs) is undoubtedly an important problem in today's world. Here, we propose a general performance modeling methodology and workload analysis of distributed LLM training and inference through an analytical framework that accurately considers compute, memory sub-system, network, and various parallelization strategies (model parallel, data parallel, pipeline parallel, and sequence parallel). We validate our performance predictions with published data from literature and relevant industry vendors (e.g., NVIDIA). For distributed training, we investigate the memory footprint of LLMs for different activation re-computation methods, dissect the key factors behind the massive performance gain from A100 to B200 ($\sim$ 35x speed-up closely following NVIDIA's scaling trend), and further run a design space exploration at different technology nodes (12 nm to 1 nm) to study the impact of logic, memory, and network scaling on the performance. For inference, we analyze the compute versus memory boundedness of different operations at a matrix-multiply level for different GPU systems and further explore the impact of DRAM memory technology scaling on inference latency. Utilizing our modeling framework, we reveal the evolution of performance bottlenecks for both LLM training and inference with technology scaling, thus, providing insights to design future systems for LLM training and inference.