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Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Xiaoteng, Liu, Pavly Halim

TL;DR

Analytical framework for predicting General Matrix Multiplication (GEMM) performance on modern GPUs, focusing on runtime, power consumption, and energy efficiency, and identifies critical performance patterns related to matrix dimensions, thread block configurations, and memory access patterns.

Abstract

Analytical framework for predicting General Matrix Multiplication (GEMM) performance on modern GPUs, focusing on runtime, power consumption, and energy efficiency. Our study employs two approaches: a custom-implemented tiled matrix multiplication kernel for fundamental analysis, and NVIDIA's CUTLASS library for comprehensive performance data collection across advanced configurations. Using the NVIDIA RTX 4070 as our experimental platform, we developed a Random Forest-based prediction model with multi-output regression capability. Through analysis of both naive tiled matrix multiplication with varying tile sizes (1 to 32) and 16,128 CUTLASS GEMM operations across diverse configurations, we identified critical performance patterns related to matrix dimensions, thread block configurations, and memory access patterns. Our framework achieved exceptional accuracy with an R^2 score of 0.98 for runtime prediction (mean error 15.57%) and 0.78 for power prediction (median error 5.42%). The system successfully predicts performance across matrix sizes, demonstrating robust scaling behavior. Our results show that optimal tile size selection can improve performance by up to 3.2x while reducing power consumption by 22% compared to baseline configurations. Analysis of shared memory utilization and SM occupancy reveals that tile sizes of 16x16 achieve the best balance between parallelism and resource usage. The implementation of our framework, including prediction models and analysis tools, is available as an open-source project at GPPerf [https://github.com/pavlyhalim/GPPerf].

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

TL;DR

Analytical framework for predicting General Matrix Multiplication (GEMM) performance on modern GPUs, focusing on runtime, power consumption, and energy efficiency, and identifies critical performance patterns related to matrix dimensions, thread block configurations, and memory access patterns.

Abstract

Analytical framework for predicting General Matrix Multiplication (GEMM) performance on modern GPUs, focusing on runtime, power consumption, and energy efficiency. Our study employs two approaches: a custom-implemented tiled matrix multiplication kernel for fundamental analysis, and NVIDIA's CUTLASS library for comprehensive performance data collection across advanced configurations. Using the NVIDIA RTX 4070 as our experimental platform, we developed a Random Forest-based prediction model with multi-output regression capability. Through analysis of both naive tiled matrix multiplication with varying tile sizes (1 to 32) and 16,128 CUTLASS GEMM operations across diverse configurations, we identified critical performance patterns related to matrix dimensions, thread block configurations, and memory access patterns. Our framework achieved exceptional accuracy with an R^2 score of 0.98 for runtime prediction (mean error 15.57%) and 0.78 for power prediction (median error 5.42%). The system successfully predicts performance across matrix sizes, demonstrating robust scaling behavior. Our results show that optimal tile size selection can improve performance by up to 3.2x while reducing power consumption by 22% compared to baseline configurations. Analysis of shared memory utilization and SM occupancy reveals that tile sizes of 16x16 achieve the best balance between parallelism and resource usage. The implementation of our framework, including prediction models and analysis tools, is available as an open-source project at GPPerf [https://github.com/pavlyhalim/GPPerf].

Paper Structure

This paper contains 47 sections, 12 equations, 9 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Significance
  3. Current Limitations
  4. Research Approach
  5. Literature Review
  6. Performance Prediction Approaches
  7. Analytical Modeling
  8. Machine Learning-Based Prediction
  9. Energy Optimization Strategies
  10. Dynamic Voltage and Frequency Scaling (DVFS)
  11. Hybrid Memory Systems
  12. Performance-Energy Trade-offs
  13. Limitations of Existing Approaches
  14. Research Gap and Motivation
  15. Proposed Idea
  16. ...and 32 more sections

Figures (9)

  • Figure 1: Roofline Performance Model for the Ada Architecture showing memory-bound and compute-bound regions. The ridge point at 59 FLOPs/Byte marks the transition between memory-limited and compute-limited performance.
  • Figure 2: The runtime of tiled matrix multiplication for different matrix sizes with tile=1
  • Figure 3: The runtime of tiled matrix multiplication for different matrix sizes with tile=4
  • Figure 4: The runtime of tiled matrix multiplication for different matrix sizes with tile=8,16, and 32.
  • Figure 5: The power usage of matrix multiplication for different matrix sizes with different tile sizes
  • ...and 4 more figures