GPU-FPtuner: Mixed-precision Auto-tuning for Floating-point Applications on GPU

Abstract

GPUs have been extensively used to accelerate scientific applications from a variety of domains: computational fluid dynamics, astronomy and astrophysics, climate modeling, numerical analysis, to name a few. Many of these applications rely on floating-point arithmetic, which is approximate in nature. High-precision libraries have been proposed to mitigate accuracy issues due to the use of floating-point arithmetic. However, these libraries offer increased accuracy at a significant performance cost. Previous work, primarily focusing on CPU code and on standard IEEE floating-point data types, has explored mixed precision as a compromise between performance and accuracy. In this work, we propose a mixed precision autotuner for GPU applications that rely on floating-point arithmetic. Our tool supports standard 32- and 64-bit floating-point arithmetic, as well as high precision through the QD library. Our autotuner relies on compiler analysis to reduce the size of the tuning space. In particular, our tuning strategy takes into account code patterns prone to error propagation and GPU-specific considerations to generate a tuning plan that balances performance and accuracy. Our autotuner pipeline, implemented using the ROSE compiler and Python scripts, is fully automated and the code is available in open source. Our experimental results collected on benchmark applications with various code complexities show performance-accuracy tradeoffs for these applications and the effectiveness of our tool in identifying representative tuning points.