Titlebook: Languages and Compilers for Parallel Computing; 22nd International W Guang R. Gao,Lori L. Pollock,Xiaoming Li Conference proceedings 2010 S

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MIMD Interpretation on a GPU,ts shared-memory communication, recursion, etc. Through a variety of careful design choices and optimizations, reasonable efficiency is obtained on NVIDIA CUDA GPUs. The discussion covers both the methods used and the motivation in terms of the relevant aspects of GPU architecture.

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A Communication Framework for Fault-Tolerant Parallel Execution,ble performance and availability. A communicating parallel program must employ checkpoint-restart and/or process redundancy to make continuous forward progress in such an unreliable environment. A communication model based on one-sided Put/Get calls, pioneered by the Linda system, is a good match as

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The STAPL pList,ent access when used in a parallel program. The Standard Template Adaptive Parallel Library (.) is a parallel programming library that extends C++ with support for parallelism. . provides a collection of distributed data structures (.) and parallel algorithms (.) and a generic methodology for extend

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Hardware Support for OpenMP Collective Operations,of-the-art on-chip networks and block-based cache coherence protocols used in shared memory Chip MultiProcessors (CMPs) are inefficient for implementing these collective operations. The performance of CMPs can be seriously degraded by the multitude of memory requests and coherence messages required

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MIMD Interpretation on a GPU, clusters, and massively parallel systems. It would be highly desirable for GPUs (Graphics Processing Units) also to be able to leverage algorithms and programming tools designed for MIMD targets. Unfortunately, most GPU hardware implements a very restrictive multi-threaded SIMD-based execution mode

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