Lingyuan Wang

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Graphics processing units (GPUs) have been accepted as a powerful and viable coprocessor solution in high-performance computing domain. In order to maximize the benefit of GPUs for a multicore platform, a mechanism is needed for CPU threads in a parallel application to share this computing resource for efficient execution. NVIDIA's Fermi architecture(More)
Scalable systems employing a mix of GPUs with CPUs are becoming increasingly prevalent in high-performance computing. The presence of such accelerators introduces significant challenges and complexities to both language developers and end users. This paper provides a close study of efficient coordination mechanisms to handle parallel requests from multiple(More)
Rapid advances in the performance and programmability of graphics accelerators have made GPU computing a compelling solution for a wide variety of application domains. However, the increased complexity as a result of architectural heterogeneity and imbalances in hardware resources poses significant programming challenges in harnessing the performance(More)
Double precision floating-point performance is critical for hardware acceleration technologies to be adopted by domain scientists. In this work we use the Hessenberg reduction to demonstrate the potential of FPGAs and GPUs for obtaining satisfactory double precision floating-point performance. Currently a Xeon (Nehalem) 2.26 GHz CPU can outperform Xilinx(More)
High-Performance Computing (HPC) systems are increasingly moving towards an architecture that is deeply hierarchical. However, the execution model with single-level parallelism embodied in legacy parallel programming models falls short in exploiting the multi-level parallelism opportunities in both hardware architectures and applications. This makes the use(More)
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