Cao H.

Michael Haidl

Michel Steuwer

Sergei Gorlatch

Stefan Breuer

Yee K.

 The implementation of stencil computations on modern, massively parallel systems with GPUs and other accelerators currently relies on manually-tuned coding using low-level approaches like OpenCL and CUDA. This makes development of stencil applications a complex, time-consuming, and error-prone task. We describe how stencil computations can be programmed in our SkelCL approach that combines high-level programming abstractions with competitive performance on multi-GPU systems. SkelCL extends the OpenCL standard by three high-level features: 1) pre-implemented parallel patterns (a.k.a. skeletons); 2) container data types for vectors and matrices; 3) automatic data (re)distribution mechanism. We introduce two new SkelCL skeletons which specifically target stencil computations – MapOverlap and Stencil – and we describe their use for particular application examples, discuss their efficient parallel implementation, and report experimental results on systems with multiple GPUs. Our evaluation of three real-world applications shows that stencil code written with SkelCL is considerably shorter and offers competitive performance to hand-tuned OpenCL code. </jats:p

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Parallel Processing Letters

High-Level Programming of Stencil Computations on Multi-GPU Systems Using the SkelCL Library

http://eprints.gla.ac.uk/148974/7/148974.pdf

High-Level Programming of Stencil Computations on Multi-GPU Systems Using the SkelCL Library

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