KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads

TL;DR

KPerfIR introduces a compiler-centric framework for GPU profiling tailored to AI workloads, enabling customizable, accurate, and low-overhead performance analysis integrated into compiler workflows.

Contribution

It presents a novel multilevel infrastructure that integrates profiling into the compiler, enhancing performance analysis for modern GPU-based AI workloads.

Findings

Low profiling overhead of 8.2%

High measurement accuracy with 2% relative error

Provides actionable insights into GPU intra-kernel optimizations

Abstract

In this work, we propose KPerfIR, a novel multilevel compiler-centric infrastructure to enable the development of customizable, extendable, and portable profiling tools tailored for modern artificial intelligence (AI) workloads on modern GPUs. Our approach integrates profiling capabilities directly into the compiler workflow, allowing profiling functionalities to be implemented as compiler passes, offering a programmable and reusable framework for performance analysis. This design bridges the gap between compilers and profilers, enabling fine-grained insights into complex optimization challenges such as overlapping the execution of fine-grained function units on GPUs. KPerfIR is integrated into the Triton infrastructure to highlight the power of a compiler-centric approach to advance performance analysis and optimization in the ever-evolving landscape of AI compilers. Our evaluation shows that our tool incurs low overhead (8.2%), provides accurate measurements (2% relative error), and delivers actionable insights into complicated GPU intra-kernel optimizations.