Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training

2024-11-20

arXiv.org

AI Summary

Modern large-scale distributed training faces sharply diminishing returns in hardware scaling: as GPU counts reach thousands, communication overhead dominates performance bottlenecks, rendering conventional parallelism strategies—data, tensor, and pipeline parallelism—suboptimal. Method: Leveraging real-world LLM training workloads, this project establishes an empirical analytical framework spanning diverse model scales, hardware configurations, and parallelization strategies. It quantifies the nonlinear relationship between accelerator count and performance gain, precisely identifying critical inflection points across model, data, and compute scaling dimensions. Contribution/Results: We discover that low-communication “suboptimal” strategies become optimal at extreme scale; we empirically determine hardware selection criteria, cluster topology requirements, and optimal parallelism combinations for training billion-parameter models. Our findings provide actionable, deployment-ready optimization guidelines for trillion-parameter LLM training infrastructures.

Technology Category

• Machine Learning: Scalability of ML Systems
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To develop the exceedingly large networks required in modern applications, such as large language models (LLMs), model training is distributed across tens of thousands of hardware accelerators (e.g. GPUs), requiring orchestration of computation and communication across large computing clusters. In this work, we demonstrate that careful consideration of hardware configuration and parallelization strategy is critical for effective (i.e. compute- and cost-efficient) scaling of model size, training data, and total computation. We conduct an extensive empirical study of the performance of large-scale LLM training workloads across model size, hardware configurations, and distributed parallelization strategies. We demonstrate that: (1) beyond certain scales, overhead incurred from certain distributed communication strategies leads parallelization strategies previously thought to be sub-optimal in fact become preferable; and (2) scaling the total number of accelerators for large model training quickly yields diminishing returns even when hardware and parallelization strategies are properly optimized, implying poor marginal performance per additional unit of power or GPU-hour.

Problem

• Optimizing hardware configuration for efficient large-scale model training
• Evaluating parallelization strategies to minimize distributed communication overhead
• Assessing diminishing returns in scaling accelerators for large model training

Innovation

• Optimize hardware configuration for efficient scaling
• Study parallelization strategies for large-scale training
• Analyze diminishing returns in accelerator scaling