Modeling GPU-CPU workloads and systems

Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to optimize a high performance application for a very specific and well documented system, it may not perform as well or even function on a different system. Developers who have less experience with either the application domain or the system architecture may devote a significant effort to writing a program that merely functions correctly. We believe that a comprehensive analysis and modeling framework is necessary to ease application development and automate program optimization on heterogeneous platforms. This paper reports on an empirical evaluation of 25 CUDA applications on four GPUs and three CPUs, leveraging the Ocelot dynamic compiler infrastructure which can execute and instrument the same CUDA applications on either target. Using a combination of instrumentation and statistical analysis, we record 37...

• Type: Conference
• Year: 2010
• Where: ASPLOS
• Authors: Andrew Kerr, Gregory F. Diamos, Sudhakar Yalamanchili
• Keywords: ASPLOS 2010, Cuda Applications, CUDA Kernel Performance, Modeling Framework, Programming Languages