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The introduction of General-Purpose computation on GPUs (GPGPUs) has changed the landscape for the future of parallel computing. At the core of this phenomenon are massively multithreaded, data-parallel architectures possessing impressive acceleration ratings, offering low-cost supercomputing together with attractive power budgets. Even given the numerous benefits provided by GPGPUs, there remain...
Graphic Processing Units (GPUs) have evolved to provide a massive computational power. In contrast to Central Processing Units, GPUs are so-called many-core processors with hundreds of cores capable of running thousands of threads in parallel. This parallel processing power can accelerate the simulation of communication systems. In this work, we utilize NVIDIA's Compute Unified Device Architecture...
There is building interest in using FPGAs as accelerators for high-performance computing, but existing systems for programming them are so far inadequate. In this paper we propose a soft processor programming model and architecture inspired by graphics processing units (GPUs) that are well-matched to the strengths of FPGAs, namely highly-parallel and pipelinable computation. In particular, our soft...
With fast development of transistor technology, Graphic Processing Unit(GPU) is increasingly used in the non-graphics applications, and major GPU hardware vendors have introduced software stacks for their own GPUs, such as Brook+ for AMD GPU. Compared with the traditional parallel systems, heterogeneous systems integerating stream-based multi-threaded GPUs provide higher parallel computing capabilities...
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