Portable to Efficient: Auto-Tuning Hardware-Agnostic GPU Kernels in Julia

2026-08-21

AI Summary

本文通过将自动调优集成到Julia编写的硬件无关GPU内核中，解决了跨不同架构高效执行的难题，显著提升了性能。

Abstract

Traditionally, GPU kernels have been developed and optimized within vendor-specific programming models to achieve high performance, resulting in software that is difficult to optimize and adapt across increasingly heterogeneous computing systems. Hardware-agnostic programming models offer a more sustainable approach to GPU software development by improving portability and maintainability, but achieving efficient execution across diverse architectures remains challenging. We address this challenge by integrating auto-tuning into hardware-agnostic GPU kernels written in Julia. We rebuild the established Kernel Tuner auto-tuning framework with Julia support, enabling systematic exploration of kernel configurations for hardware-agnostic GPU kernels targeting NVIDIA, AMD, Intel, and Apple GPUs. We demonstrate this approach on hardware-agnostic singular value decomposition (SVD) as implemented in the NextLA.jl linear algebra library. The results show that auto-tuning is essential for creating resource-efficient hardware-agnostic GPU kernels across a variety of hardware. Optimal configurations improve kernel performance by a factor of 3x to 7x compared to median parameter configurations, demonstrating the substantial impact of tuning on efficient hardware utilization.

Problem

• hardware-agnostic
• GPU kernels
• auto-tuning
• portability
• heterogeneous computing systems

Innovation

• Auto-Tuning
• Hardware-Agnostic
• GPU Kernels
• Julia
• Efficient Execution