Optimizing UPC Programs for Multi-Core Systems | Zendy

Yili Zheng | Zendy

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Optimizing UPC Programs for Multi-Core Systems

Author(s) -

Yili Zheng

Publication year - 2010

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2010/646829

Subject(s) - computer science , partitioned global address space , scalability , parallel computing , locality , out of core algorithm , multi core processor , computer architecture , matrix multiplication , operating system , linguistics , physics , quantum mechanics , quantum , philosophy

The Partitioned Global Address Space (PGAS) model of Unified Parallel C (UPC) can help users express and manage application data locality on non-uniform memory access (NUMA) multi-core shared-memory systems to get good performance. First, we describe several UPC program optimization techniques that are important to achieving good performance on NUMA multi-core computers with examples and quantitative performance results. Second, we use two numerical computing kernels, parallel matrix–matrix multiplication and parallel 3-D FFT, to demonstrate the end-to-end development and optimization for UPC applications. Our results show that the optimized UPC programs achieve very good and scalable performance on current multi-core systems and can even outperform vendor-optimized libraries in some cases.

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