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

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. Ske1CL 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 Ske1CL 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 Ske1CL is considerably shorter and offers competitive performance to hand-tuned OpenCL code.