Scout: a data-parallel programming language for graphics processors

Authors:
Patrick McCormick;Jeff Inman;James Ahrens;Jamaludin Mohd-Yusof;Greg Roth;Sharen Cummins
Affiliations:
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, United States;Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, United States;Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, United States;Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, United States;Computer Science Department, The University of Utah, United States;Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, United States
Venue:
Parallel Computing
Year:
2007

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Abstract

Commodity graphics hardware has seen incredible growth in terms of performance, programmability, and arithmetic precision. Even though these trends have been primarily driven by the entertainment industry, the price-to-performance ratio of graphics processors (GPUs) has attracted the attention of many within the high-performance computing community. While the performance of the GPU is well suited for computational science, the programming interface, and several hardware limitations, have prevented their wide adoption. In this paper we present Scout, a data-parallel programming language for graphics processors that hides the nuances of both the underlying hardware and supporting graphics software layers. In addition to general-purpose programming constructs, the language provides extensions for scientific visualization operations that support the exploration of existing or computed data sets.