Analyzing CUDA workloads using a detailed GPU simulator

Modern Graphic Processing Units (GPUs) provide sufficiently flexible programming models that understanding their performance can provide insight in designing tomorrow’s manycore processors, whether those are GPUs or otherwise. The combination of multiple, multithreaded, SIMD cores makes studying these GPUs useful in understanding tradeoffs among memory, data, and thread level parallelism. While modern GPUs offer orders of magnitude more raw computing power than contemporary CPUs, many important applications, even those with abundant data level parallelism, do not achieve peak performance. This paper characterizes several non-graphics applications written in NVIDIA’s CUDA programming model by running them on a novel detailed microarchitecture performance simulator that runs NVIDIA’s parallel thread execution (PTX) virtual instruction set. For this study, we selected twelve non-trivial CUDA applications demonstrating varying levels of performance improvement on GPU hardware (ver...

• Type: Conference
• Year: 2009
• Where: ISPASS
• Authors: Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt
• Keywords: GPUs, ISPASS 2009, Performance, Performance Simulator, Software Engineering