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To improve productivity for developing parallel applications on high performance computing systems, the XcalableMP PGAS language has been proposed. XcalableMP supports both a typical parallelization under the ''global-view memory model'' which uses directives and a flexible parallelization under the ''local-view memory model'' which uses coarray features. The goal of the present paper is to clarify XcalableMP's productivity and performance. To do so, we implement and evaluate the high performance computing challenge benchmark, namely, EP STREAM Triad, High Performance Linpack, Global fast Fourier transform, and RandomAccess on the K computer using up to 16,384 compute nodes and a generic cluster system using up to 128 compute nodes. We found that we could more easily implement the benchmarks using XcalableMP rather than using MPI. Moreover, most of the performance results using XcalableMP were almost the same as those using MPI.

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SUMMARYThis paper presents a set of extensions on High Performance Fortran (HPF) to make it more usable for parallelizing real-world production codes. HPF has been effective for programs that a compiler can automatically optimize efficiently. However, once the compiler cannot, there have been no ways for the users to explicitly parallelize or optimize their programs. In order to resolve the situation, we have developed a set of HPF extensions (HPF/JA) to give the users more control over sophisticated parallelization and communication optimizations. They include parallelization of loops with complicated reductions, asynchronous communication, user-controllable shadow, and communication pattern reuse for irregular remote data accesses. Preliminary experiments have proved that the extensions are effective at increasing HPF's usability.

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This paper proposes twisted

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