ThreadFuser: A SIMT Analysis Framework for MIMD Programs

Abstract

The broad usage of accelerators, such as GPUs, faces two important challenges. Developing code for a new accelerator is expensive and unpredictable. Porting large parallel programs from Multiple Instruction Multiple Data (MIMD) CPUs to Single Instruction Multiple Thread (SIMT) GPUs involves significant effort that may or may not result in improved performance versus the CPU. This high activation energy to create new workloads introduces the second challenge: architects and systems researchers lack a diverse SIMT codebase to study new designs. To tackle these challenges, we introduce ThreadFuser, an analysis framework that efficiently and accurately predicts the performance of any pre-written MIMD program on SIMT hardware. ThreadFuser conducts thorough control and data flow analysis on dynamic CPU program traces, determining the impact of lock-step execution on CPU binaries. ThreadFuser efficiently delivers accurate reports on a MIMD program’s divergence and synchronization characteristics. Moreover, ThreadFuser seamlessly integrates with state-of-the-art GPU simulators to conduct detailed analyses and produce fine-grained performance measurements. We evaluate ThreadFuser on a diverse set of 36 CPU work- loads, demonstrating the potential and challenges of executing MIMD code on a SIMT machine. We demonstrate ThreadFuser’s potential to inform software development decisions and open new areas to explore in data-parallel hardware design.