Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training

Divya Kiran Kadiyala, Saeed Rashidi, Taekyung Heo, Abhimanyu Rajeshkumar Bambhaniya, Tushar Krishna, Alexandros Daglis

TL;DR

COMET addresses the challenge of designing massive distributed DL training clusters by introducing a holistic methodology that jointly explores model parallelism, data parallelism, and cluster resources. The approach combines workload modeling, training-strategy configuration, and end-to-end training-time estimation using roofline- and memory-traffic-based analytics, augmented by ASTRA-sim for execution-time breakdowns and by considering memory-expansion options like CXL-attached memory. Its key contributions are a reusable workflow, a lightweight toolchain, and case studies on Transformer and DLRM that reveal how memory capacity and bandwidth, per-node compute, and network topology shape performance and efficiency; notably, memory expansion can yield substantial gains (up to 1.4× in the case studies) and overall cluster comparisons can show up to 7.7× differences. COMET enables rapid, design-time sensitivity analyses to guide system designers in provisioning resources and selecting parallelization strategies that maximize training throughput or cost efficiency for targeted workloads. This framework offers practical, actionable insights for evaluating current and emerging memory, compute, and interconnect technologies in distributed DL training contexts.

Abstract

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization--to amortize their steep cost--is a challenging task requiring careful balance of compute, memory, and network resources. Moreover, a plethora of each model's tuning knobs drastically affect the performance, with optimal values often depending on the underlying cluster's characteristics, which necessitates a complex cluster-workload co-design process. To facilitate the design space exploration of such massive DL training clusters, we introduce COMET, a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training. We develop a step-by-step process to establish a reusable and flexible methodology, and demonstrate its application with case studies of training large models on cluster configurations of variable compute, memory, and network resources. Our case studies demonstrate COMET's utility in identifying promising architectural optimization directions and guiding system designers in configuring key model and cluster parameters. To illustrate, cluster configuration comparisons identify performance differences of up to 7.7x and highlight performance optimization opportunities of up to 1.4x when employing memory expansion as an optimization technique.

COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training

TL;DR

COMET addresses the challenge of designing massive distributed DL training clusters by introducing a holistic methodology that jointly explores model parallelism, data parallelism, and cluster resources. The approach combines workload modeling, training-strategy configuration, and end-to-end training-time estimation using roofline- and memory-traffic-based analytics, augmented by ASTRA-sim for execution-time breakdowns and by considering memory-expansion options like CXL-attached memory. Its key contributions are a reusable workflow, a lightweight toolchain, and case studies on Transformer and DLRM that reveal how memory capacity and bandwidth, per-node compute, and network topology shape performance and efficiency; notably, memory expansion can yield substantial gains (up to 1.4× in the case studies) and overall cluster comparisons can show up to 7.7× differences. COMET enables rapid, design-time sensitivity analyses to guide system designers in provisioning resources and selecting parallelization strategies that maximize training throughput or cost efficiency for targeted workloads. This framework offers practical, actionable insights for evaluating current and emerging memory, compute, and interconnect technologies in distributed DL training contexts.

Abstract

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization--to amortize their steep cost--is a challenging task requiring careful balance of compute, memory, and network resources. Moreover, a plethora of each model's tuning knobs drastically affect the performance, with optimal values often depending on the underlying cluster's characteristics, which necessitates a complex cluster-workload co-design process. To facilitate the design space exploration of such massive DL training clusters, we introduce COMET, a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training. We develop a step-by-step process to establish a reusable and flexible methodology, and demonstrate its application with case studies of training large models on cluster configurations of variable compute, memory, and network resources. Our case studies demonstrate COMET's utility in identifying promising architectural optimization directions and guiding system designers in configuring key model and cluster parameters. To illustrate, cluster configuration comparisons identify performance differences of up to 7.7x and highlight performance optimization opportunities of up to 1.4x when employing memory expansion as an optimization technique.
Paper Structure (35 sections, 3 equations, 15 figures, 3 tables)

This paper contains 35 sections, 3 equations, 15 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Distributed DL Parallelization Strategies
  4. Challenges in Distributed DL Training
  5. Prior Work on Distributed DL Training
  6. The COMET Methodology
  7. Workload Modeling
  8. Training Strategy Configuration
  9. Distributed Training Time Estimation
  10. Compute delay estimation
  11. Memory traffic estimation
  12. Communication delay estimation
  13. Total training time estimation
  14. COMET Implementation
  15. DL Model Analysis
  16. ...and 20 more sections

Figures (15)

  • Figure 1: Tunable cluster component parameters in COMET and value ranges evaluated in this paper.
  • Figure 2: COMET methodology overview.
  • Figure 3: Variation of per-node memory capacity requirements as a function of MP and DP degrees in a fixed-size cluster.
  • Figure 4: Roofline model. Attainable performance shifts for the same OI, depending on available memory bandwidth.
  • Figure 5: COMET implementation and workflow.
  • ...and 10 more figures