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首页> 外文会议>IEEE Region 10 Colloquium >Functional Parallelism with Shared Memory and Distributed Memory Approaches
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Functional Parallelism with Shared Memory and Distributed Memory Approaches

机译：具有共享内存和分布式存储器方法的功能并行性

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摘要

The recent enhancements in processor architechtures have given rise to multi-threaded, multi-core and multi-processor based clusters of high performance computing. To exploit the variety of parallelism available in these current and future computer systems, programmers must use appropriate parallel programming approaches. Though conventional programming models exist for parallel programming neither of them have sufficiently addressed the emerging processor technologies. The paper evaluates how functional programming can be used with distributed memory and shared memory languages to exploit the scalability, heterogeneity and flexibility of clusters in solving the recursive Strassen's matrix multiplication problem. The results show that the functional language Erlang is more efficient than virtual shared memory approach and can be made more scalable than distributed memory programming approaches when incorporated with OpenMP.
机译：最近处理器architechtures的增强功能对基于高性能计算的多线程，多核和多处理器的集群产生了上升。为了利用这些当前和未来的计算机系统中可用的各种并行性，程序员必须使用适当的并行编程方法。尽管存在并行编程的传统编程模型，但它们都没有充分解决新兴处理器技术。本文评估了功能性编程如何与分布式内存和共享内存语言一起使用，以利用群集解决递归划分的矩阵乘法问题的群集的可扩展性，异质性和灵活性。结果表明，功能语言Erlang比虚拟共享内存方法更有效，并且当与OpenMP结合时，可以比分布式内存编程方法更加可扩展。

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