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

2026-01-19

AI Summary

This work addresses the challenge of high energy consumption in GPU kernels, which developers often struggle to optimize efficiently due to limited knowledge of underlying hardware. We propose a novel method based on static analysis of PTX code that accurately predicts kernel energy consumption and recommends Pareto-optimal thread block configurations balancing both energy efficiency and execution time—without requiring runtime execution. By integrating static analysis with interpretable optimization guidance, our approach substantially reduces the configuration search space. Experimental results across diverse GPUs and kernels demonstrate an 83% accuracy in configuration recommendation and a 93.4% reduction in search space. Notably, on multi-head attention kernels, our method achieves up to 79% energy savings and a 106% improvement in throughput.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• GPU energy optimization
• AI applications
• power efficiency
• kernel configuration
• sustainable computing

Innovation

• static analysis
• energy optimization
• GPU kernels
• Pareto-optimal configuration
• PTX code