TBPoint: Reducing Simulation Time for Large-Scale GPGPU Kernels

Abstract

Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way to take samples. For the evaluated 12 kernels, the geometric means of sampling errors of TBPoint, Ideal-Simpoint, and random sampling are 0.47%, 1.74%, and 7.95%, respectively, while the geometric means of the total sample size of TBPoint, Ideal-Simpoint, and random sampling are 2.6%, 5.4%, and 10%, respectively. TBPoint narrows the speed gap between hardware and GPGPU simulators, enabling more and more large-scale GPGPU kernels to be analyzed using detailed timing simulations.