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Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Prior work focuses a lot on GPUs but little is known about the performance impact on (Intel Xeon) Phi. In this work, we apply multiple streams into six real-world applications on Phi. We then systematically evaluate the performance benefits of using multiple streams...
In light of its powerful computing capacity and high energy efficiency, GPU (graphics processing unit) has become a focus in the research field of HPC (High Performance Computing). CPU-GPU heterogeneous parallel systems have become a new development trend of super-computer. However, the inherent unreliability of the GPU hardware deteriorates the reliability of super-computer. We have researched on...
In recent years, heterogeneous parallel system have become a focus research area in high performance computing field. Generally, in a heterogeneous parallel system, CPU provides the basic computing environment and special purpose accelerator (GPU in this paper) provides high computing performance. However, the overall performance of the system is prone to be limited by the data communication between...
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...
Graphic Processing Unit (GPU), with many light-weight data-parallel cores, can provide substantial parallel computing power to accelerate several general purpose applications. Both the AMD and NVIDIA corps provide their specific high performance GPUs and software platforms. As the floating-point computing capacity increases continually, the problem of ``memory-wall'' becomes more serious, especially...
Graphic Processing Unit (GPU), with many light-weight data-parallel cores, can provide substantial parallel computational power to accelerate general purpose applications. But the powerful computing capacity could not be fully utilized for memory-intensive applications, which are limited by off-chip memory bandwidth and latency. Stencil computation has abundant parallelism and low computational intensity...
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