A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing

Abstract

The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators, built around GPUs, many-core chips, FPGAs or DSPs, are used to offload compute-intensive tasks. The advent of this type of systems has brought about a wide and diverse ecosystem of development platforms, optimization tools and performance analysis frameworks. This is a review of the state-of-the-art in performance tools for heterogeneous computing, focusing on the most popular families of accelerators: GPUs and Intel's Xeon Phi. We describe current heterogeneous systems and the development frameworks and tools that can be used for developing for them. The core of this survey is a review of the performance models and tools, including simulators, proposed in the literature for these platforms.