Titlebook: Languages and Compilers for Parallel Computing; 21th International W José Nelson Amaral Conference proceedings 2008 Springer-Verlag Berlin

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Compiler-Driven Dependence Profiling to Guide Program Parallelization,llelization of legacy codes and effective exploitation of all available hardware resources. Thread-level speculation (TLS) has been proposed as a technique to parallelize the execution of serial codes or serial sections of parallel codes. One of the key aspects of TLS is task selection for speculati

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gluepy: A Simple Distributed Python Programming Framework for Complex Grid Environments,espect to connection management, accommodate dynamic processes joining/leaving at runtime, and provide simple means to tolerate communication/node failures. All of the above must be presented in a simple and flexible programming model. This paper designs and implements such a framework by minimally

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CUDA-Lite: Reducing GPU Programming Complexity,UDA, as one such tool. We leverage programmer knowledge via annotations to perform transformations and show preliminary results that indicate auto-generated code can have performance comparable to hand coding.

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MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs,ation of this framework and demonstrate performance approaching that achievable from manually parallelized and optimized C code. With these results, we argue that CUDA can be an effective data-parallel programming model for more than just GPU architectures.

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