*Result*: Productivity and Performance of Global-View Programming with XcalableMP PGAS Language.

*XcalableMP (XMP) is a PGAS parallel language with a directive-based extension of C and Fortran. While it supports gcoarrayh as a local-view programming model, an XMP global-view programming model is useful when parallelizing data-parallel programs by adding directives with minimum code modification. This paper considers the productivity and performance of the XMP global-view programming model. In the global-view programming model, a programmer describes data distributions and work-mapping to map the computations to nodes, where the computed data are located. Global-view communication directives are used to move a part of the distributed data globally and to maintain consistency in the shadow area. Rich sets of XMP global-view programming model can reduce the cost for parallelization significantly, and optimization of gprivatizationh is not necessary. For productivity and performance study, the Omni XMP compiler and the Berkeley Unified Parallel C compiler are used. Experimental results show that XMP can implement the benchmarks with a smaller programming cost than UPC. Furthermore, XMP has higher access performance for global data, which has an affinity with own process than UPC. In addition, the XMP co array function can effectively tune the application's performance. [ABSTRACT FROM PUBLISHER]

Copyright of 2012 12th IEEE/ACM International Symposium on Cluster, Cloud & Grid Computing (CCGRID 2012) is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)*