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FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels

Saurabhsingh Rajput, Alexander Brandt, Vadim Elisseev, Tushar Sharma

TL;DR

FlipFlop presents a static PTX-level energy optimization framework for CUDA kernels that predicts Pareto-optimal thread-block configurations and power caps without runtime execution. By calibrating a hybrid performance-power model to hardware and extracting static kernel features, it achieves energy savings while preserving throughput, demonstrated on multi-head attention and other CUDA kernels across GPUs. The approach yields up to 79% energy reduction per token with no more than ~5% throughput loss, and providesExplainable guidance that lowers developer effort by dramatically reducing the configuration search space (up to 93.4% reduction). Case studies and thorough validation show robust energy-performance predictions, portable calibration, and meaningful production impact for energy-aware AI systems that scale across architectures.

Abstract

Artificial Intelligence (AI) applications, such as Large Language Models, are primarily driven and executed by Graphics Processing Units (GPUs). These GPU programs (kernels) consume substantial amounts of energy, yet software developers often lack the hardware expertise and ad hoc knowledge required to optimize for power efficiency. We propose FlipFlop, a framework using static code analysis to predict energy consumption and recommend Pareto-optimal thread block configurations considering both power consumption and execution time. Our framework requires no runtime execution and analyzes PTX code, a low-level instruction set for CUDA-enabled GPUs. It is validated across a diverse set of GPUs and kernels, including multi-head attention, convolution, and matrix multiplication. FlipFlop achieves 83% accuracy in identifying locally optimal energy-efficient configurations, while also minimizing developer effort by reducing the optimization search space by 93.4%. For multi-head attention kernels, it yields up to 79% energy savings and 106% throughput gains relative to NVIDIA's occupancy heuristic. By integrating static analysis with real-time monitoring and providing explainable optimization guidance, FlipFlop empowers developers to create sustainable, high-performance GPU software which minimizes environmental and computational costs.

FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels

TL;DR

FlipFlop presents a static PTX-level energy optimization framework for CUDA kernels that predicts Pareto-optimal thread-block configurations and power caps without runtime execution. By calibrating a hybrid performance-power model to hardware and extracting static kernel features, it achieves energy savings while preserving throughput, demonstrated on multi-head attention and other CUDA kernels across GPUs. The approach yields up to 79% energy reduction per token with no more than ~5% throughput loss, and providesExplainable guidance that lowers developer effort by dramatically reducing the configuration search space (up to 93.4% reduction). Case studies and thorough validation show robust energy-performance predictions, portable calibration, and meaningful production impact for energy-aware AI systems that scale across architectures.

Abstract

Artificial Intelligence (AI) applications, such as Large Language Models, are primarily driven and executed by Graphics Processing Units (GPUs). These GPU programs (kernels) consume substantial amounts of energy, yet software developers often lack the hardware expertise and ad hoc knowledge required to optimize for power efficiency. We propose FlipFlop, a framework using static code analysis to predict energy consumption and recommend Pareto-optimal thread block configurations considering both power consumption and execution time. Our framework requires no runtime execution and analyzes PTX code, a low-level instruction set for CUDA-enabled GPUs. It is validated across a diverse set of GPUs and kernels, including multi-head attention, convolution, and matrix multiplication. FlipFlop achieves 83% accuracy in identifying locally optimal energy-efficient configurations, while also minimizing developer effort by reducing the optimization search space by 93.4%. For multi-head attention kernels, it yields up to 79% energy savings and 106% throughput gains relative to NVIDIA's occupancy heuristic. By integrating static analysis with real-time monitoring and providing explainable optimization guidance, FlipFlop empowers developers to create sustainable, high-performance GPU software which minimizes environmental and computational costs.
Paper Structure (40 sections, 5 figures, 3 tables, 3 algorithms)

This paper contains 40 sections, 5 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background & Related Work
  3. Transformer Architectures & LLM Computation
  4. GPU Architecture & CUDA Programming Model
  5. Performance & Power Modeling
  6. GPU Kernel Optimization
  7. GPU Execution Challenges
  8. Comparison with existing work
  9. Overview
  10. Research Questions
  11. Experimental Setup
  12. RQ1: Tuning Thread Blocks and Power Limits
  13. Methods---RQ1
  14. Data collection and metrics.
  15. Results---RQ1
  16. ...and 25 more sections

Figures (5)

  • Figure 1: Energy per token vs. block dimensions. Each line represents a sequence length. The horizontal line shows the consumed energy of occupancy-based configs
  • Figure 2: FlipFlop Architecture
  • Figure 3: Power scaling vs. active SMs on RTX 5000 Ada.
  • Figure 4: Predicted energy per token as a function of block config. Each line corresponds to a different seq. length
  • Figure 5: Trends between Actual and Predicted Energy