(re)distribution mechanism allows for scalability when using multi-GPU systems. We use a real-world example from the field of medical imaging to motivate the design of our programming model and we show how application development using SkelCL is simplified without sacrificing performance: we were able

no special support for systems consisting of multiple GPUs. The SkelCL library presented in this paper is built on top of the OpenCL standard and offers pre-implemented recurring computation and communication pat-terns (skeletons) which greatly simplify programming for multi-GPU systems. The library also

computations can be programmed in our SkelCL approach that combines high level of programming abstrac-tion 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. skele-tons); 2) container data types

high-level abstractions. The especially challenging systems with multiple GPU are not addressed at all by these low-level programming models. We propose SkelCL – a library providing so-called algorithmic skeletons that capture recurring patterns of parallel compu-tation and communication, together

-prone. Memory management is complex due to the separate memories of CPU and GPU and GPUs support only a subset of common CPU programming languages. Both, CUDA and OpenCL lack support for multi-GPU systems, such that additional challenges like communication and synchronization arise when using multiple GPUs. We

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

-consuming, and error-prone task. We describe how stencil computations can be programmed in our SkelCL approach that combines high-level programming ab-stractions with competitive performance on multi-GPU systems. SkelCL extends the OpenCL standard by three high-level features: 1) pre-implemented parallel patterns (a

Abstract. We present MGPU, a C++ programming library targeted at single-node multi-GPU systems. Such systems combine disproportionate floating point performance with high data locality and are thus well suited to implement real-time algorithms. We describe the library de-sign, programming interface

that do not exist in other languages such as CUDA. Understanding these implications allows us to provide a single library with decent performance on a variety of platforms. We choose triangular solver (TRSM) and matrix multiplication (GEMM) as representative level 3 BLAS routines to implement in OpenCL

of representation and retrieval of visualmedia were confined to specialized image databases (geographical, medical, pilot experimentsin computerized slide libraries), in the professional applications of the audiovisualindustries (production, broadcasting and archives), and in computerized training or education