Titlebook: Languages and Compilers for Parallel Computing; 23rd International W Keith Cooper,John Mellor-Crummey,Vivek Sarkar Conference proceedings 2

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Optimizing and Auto-tuning Belief Propagation on the GPU,ray that is accessed using a variable index within a loop. However, accesses from local memory take longer than accesses from registers and shared memory, so it is desirable to minimize the use of local memory. This paper contains an analysis of strategies used to reduce the use of local memory in a

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Parallelizing Compiler Framework and API for Power Reduction and Software Productivity of Real-Time, heterogeneous multicores force programmers very difficult programming. The long application program development period lowers product competitiveness. In order to overcome such a situation, this paper proposes a compilation framework which bridges a gap between programmers and heterogeneous multic

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Optimizing the Exploitation of Multicore Processors and GPUs with OpenMP and OpenCL,ate the proposal on three different architectures, SMP, Cell/B.E. and GPUs, showing the wide usefulness of the approach. The evaluation is done with four different benchmarks, Matrix Multiply, BlackScholes, Perlin Noise, and Julia Set. We compare the results obtained with the execution of the same b

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CnC-CUDA: Declarative Programming for GPUs,frequencies. Instead, future computer systems are expected to be built using homogeneous and heterogeneous many-core processors with 10’s to 100’s of cores per chip, and complex hardware designs to address the challenges of concurrency, energy efficiency and resiliency. Unlike previous generations o

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Parallel Graph Partitioning on Multicore Architectures,ectures. It is used to distribute graphs across memory and to improve spatial locality. There are several parallel implementations of graph partitioning for distributed-memory architectures..In this paper, we present a parallel graph partitioner that implements a variation of the Metis partitioner f

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