A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

Abstract

This paper proposes a hybrid framework for fast and accurate performance estimation of OpenCL kernels running on GPUs. The kernel execution flow is statically analyzed and thereupon the execution trace is generated via a loop-based bidirectional branch search. Then the trace is dynamically simulated to perform a dummy execution of the kernel to obtain the estimated time. The framework does not rely on profiling or measurement results which are used in conventional performance estimation techniques. Moreover, the lightweight trace-based simulation consumes much less time than a fine-grained GPU simulator. Our framework can accurately grasp the variation trend of the execution time in the design space and robustly predict the performance of the kernels across two generations of recent Nvidia GPU architectures. Experiments on four Commercial Off-The-Shelf (COTS) GPUs show that our framework can predict the runtime performance with average Mean Absolute Percentage Error (MAPE) of 17.04% and time consumption of a few seconds. We also demonstrate the practicability of our framework with a realworld application.