NDLI: Translating Chapel to Use FREERIDE: A Case Study in Using an HPC Language for Data-Intensive Computing

Content Provider	IEEE Xplore Digital Library
Author	Bin Ren Agrawal, G. Chamberlain, B. Deitz, S.
Copyright Year	2011
Abstract	In the last few years, the growing significance of data-intensive computing has been closely tied to the emergence and popularity of new programming paradigms for this class of applications, including Map-Reduce, and new high-level languages for data-intensive computing. The ultimate goal of these efforts in data-intensive computing has been to achieve parallelism with as little effort as possible, while supporting high efficiency and scalability. While these are also the goals that the parallel language/compiler community has tried meeting for the past several decades, the development of languages and programming systems for data-intensive computing has largely been in isolation to the developments in general parallel programming. Such independent developments in the two areas, i.e., data-intensive computing and high productivity languages lead to the following questions: I) Are HPC languages suitable for expressing data-intensive computations? and if so, II.a) What are the issues in using them for effective parallel programming? or, if not, II.b) What characteristics of data-intensive computations force the need for separate language support?. This paper takes a case study to address these questions. Particularly, we study the suitability of Chapel for expressing data-intensive computations. We also examine compilation techniques required for directly invoking a data-intensive middleware from Chapel's compilation system. The data-intensive middleware we use in this effort is FREERIDE that has been developed at Ohio State. We show how certain transformations can enable efficient invocation of the FREERIDE functions from the Chapel compiler. Our experiments show that after certain optimizations, the performance of the version of Chapel compiler that invokes FREERIDE functions is quite comparable to the performance of hand-written data-intensive applications.
Starting Page	1242
Ending Page	1249
File Size	707794
Page Count	8
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.266
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Productivity Runtime Program processors Arrays Data mining Middleware
Content Type	Text
Resource Type	Article

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