一句话：提出 FlipFlop，用 PTX 静态分析预测 GPU 内核能耗并推荐帕累托最优线程块配置。
AI 应用大量依赖 GPU，但其内核能耗巨大，开发者通常缺乏硬件经验来优化功耗。FlipFlop 是一个无需运行时的静态分析框架：它分析 CUDA 的 PTX 代码，预测能耗，并推荐同时考虑功耗与执行时间的帕累托最优线程块配置，还结合实时监控提供可解释的优化指导。作者在多种 GPU 和内核（多头注意力、卷积、矩阵乘法）上验证该框架。结果显示，识别局部最优能效配置的准确率达 83%，并将优化搜索空间缩减 93.4%；对多头注意力内核，相比 NVIDIA 的 occupancy 启发式，最多可节能 79%、吞吐提升 106%。
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.
@inproceedings{abs-2601-13345,
title = {FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels},
author = {Saurabhsingh Rajput and Alexander Brandt and Vadim Elisseev and Tushar Sharma},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3787793}
}