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

TL;DR

FlipFlop is a static analysis framework that predicts energy consumption and recommends optimal GPU kernel configurations to improve energy efficiency and performance without runtime overhead.

Contribution

It introduces a static code analysis approach for energy optimization of GPU kernels, reducing search space and providing explainable, high-accuracy recommendations.

Findings

Achieves 83% accuracy in energy-efficient configuration prediction.

Reduces optimization search space by 93.4%.

Up to 79% energy savings and 106% throughput gains for multi-head attention kernels.

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.