Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis

TL;DR

This paper introduces a hardware-aware runtime mapping technique for open-source GPGPU platforms that optimizes performance by analyzing micro-architecture parameters, surpassing traditional hardware-agnostic methods.

Contribution

It presents a novel micro-architecture parameter analysis approach for runtime kernel mapping on open-source GPGPUs, improving performance and resource utilization.

Findings

Significant performance improvements on Vortex GPGPU

Effective optimization across various GPU configurations

Enhanced hardware resource utilization

Abstract

GPGPU execution analysis has always been tied to closed-source, proprietary benchmarking tools that provide high-level, non-exhaustive, and/or statistical information, preventing a thorough understanding of bottlenecks and optimization possibilities. Open-source hardware platforms offer opportunities to overcome such limits and co-optimize the full {hardware-mapping-algorithm} compute stack. Yet, so far, this has remained under-explored. In this work, we exploit micro-architecture parameter analysis to develop a hardware-aware, runtime mapping technique for OpenCL kernels on the open Vortex RISC-V GPGPU. Our method is based on trace observations and targets optimal hardware resource utilization to achieve superior performance and flexibility compared to hardware-agnostic mapping approaches. The technique was validated on different architectural GPU configurations across several OpenCL kernels. Overall, our approach significantly enhances the performance of the open-source Vortex GPGPU, contributing to unlocking its potential and usability.