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c 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of(More)
c 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of(More)
Application development for modern high-performance systems with Graphics Processing Units (GPUs) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex , lengthy and error-prone programs. In this paper, we present SkelCL – a high-level programming approach for systems with multiple GPUs and its implementation as a(More)
c 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of(More)
Computers have become increasingly complex with the emergence of heterogeneous hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous computational power at the cost of increased programming effort resulting in a tension between performance and code portability. Typically, code is either tuned in a low-level imperative(More)
Heterogeneous computing has now become mainstream with virtually every desktop machines featuring accelerators such as Graphics Processing Units (GPUs). While heterogeneity offers the promise of high-performance and high-efficiency, it comes at the cost of huge programming difficulties. Languages and interfaces for programming such system tend to be(More)
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, which makes it a complex , time-consuming, and error-prone task. We describe how stencil computations can be programmed in our SkelCL approach that(More)
SUMMARY Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics(More)
Application development for modern high-performance systems with graphics processing units (GPUs) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. We present SkelCL—a high-level programming approach for systems with multiple GPUs and its implementation as a library on top of(More)
Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism and provide their efficient implementations, allowing the application developer to focus on the structure of algorithms, rather than on implementation details. This becomes especially important for modern parallel systems with multiple graphics processing(More)