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Heterogeneous computing come with tremendous potential and is a leading candidate for scientific applications that are becoming more and more complex. Accelerators such as GPUs whose computing momentum is growing faster than ever offer application performance when compute intensive portions of an application are offloaded to them. It is quite evident that future computing architectures are moving...
This paper proposes and evaluates a strategy to run Biological Sequence Comparison applications on hybrid platforms composed of GPUs and multicores with SIMD extensions. Our strategy provides multiple task allocation policies and the user can choose the one which is more appropriate to his/her problem. We also propose a workload adjustment mechanism that tackles situations that arise when slow nodes...
Graphics processing units (GPUs) have the potential to greatly accelerate many applications, yet programming models remain too low level. Many language-based solutions to date have addressed this problem by creating embedded domain-specific languages that compile to CUDA or OpenCL. These targets are meant for human programmers and thus are less than ideal compilation targets. LLVM recently gained...
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