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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.Electronic version of an article published as Parallel

Writing and optimizing programs for high performance on systems with GPUs remains a challenging task even for expert programmers. One promising optimization technique is to evaluate parts of the program upfront on the CPU and embed the computed results in the GPU code allowing for more aggressive compiler optimizations. This technique is known as multi-stage programming and has proven to allow for significant performance benefits. Unfortunately, to achieve such optimizations in current GPU programming models like OpenCL, programmers are forced to manipulate the GPU source code as plain strings, which is error-prone and type-unsafe.In this paper we describe PACXX -a GPU programming approach using modern C++ standards, with the convenient features like type deduction, lambda expressions, and algorithms from the standard template library (STL). Using PACXX, a GPU program is written as a single C++ program, rather than two distinct host and kernel programs. We extend PACXX with an easy-to-use and typesafe API for multi-stage programming avoiding the pitfalls of string manipulation. Using just-in-time compilation techniques, PACXX generates efficient GPU code at runtime.Our evaluation shows that using PACXX allows for writing multi-stage code easier and safer than currently possible. Using two detailed application studies we show that multi-stage programming can significantly outperform equivalent non-staged programs. Furthermore, we show that PACXX generates code with performance comparable to industrial-strength OpenCL compilers.

Simulations on three-dimensional random lasers were performed by finite-difference time-domain integration of Maxwell's equations combined with rate-equations providing gain. We investigated the frequency-dependent emission polarization of random lasers in the far-field of the sample and characterized the influence of anisotropic pumping in orthogonal polarizations. Under weak scattering, the polarization states of random lasing modes were random for isotropic pumping and linear under anisotropic pumping. These findings are in accordance with recent experimental observations. A crossover was observed towards very strong scattering, in which the scattering destroys the pump-induced polarization-anisotropy of the random lasing modes and randomizes (scrambles) the mode-polarization.