Patterns and skeletons for parallel and distributed computing
Patterns and skeletons for parallel and distributed computing
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
SkePU: a multi-backend skeleton programming library for multi-GPU systems
Proceedings of the fourth international workshop on High-level parallel programming and applications
SkelCL - A Portable Skeleton Library for High-Level GPU Programming
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
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Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches -- CUDA and OpenCL -- are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for single- and multi-GPU systems. In this paper, we focus on applications that work on two-dimensional data. We extend SkelCL by the matrix data type and the MapOverlap skeleton which specifies computations that depend on neighboring elements in a matrix. The abstract data types and a high-level data (re)distribution mechanism of SkelCL shield the programmer from the low-level data transfers between the system's main memory and multiple GPUs. We demonstrate how the extended SkelCL is used to implement real-world image processing applications on two-dimensional data. We show that both from a productivity and a performance point of view it is beneficial to use the high-level abstractions of SkelCL.