By a News Reporter-Staff News Editor at Computer Weekly News -- Researchers detail new data in Computer Research. According to news reporting originating from Hudson, Massachusetts, by VerticalNews correspondents, research stated, "As multicore processors are deployed in mainstream computing, the need for software tools to help parallelize programs is increasing dramatically. Data-dependence profiling is an important program analysis technique to exploit parallelism in serial programs."
Our news editors obtained a quote from the research from Intel, "More specifically, manual, semiautomatic, or automatic parallelization can use the outcomes of data-dependence profiling to guide where and how to parallelize in a program. However, state-of-the-art data-dependence profiling techniques consume extremely huge resources as they suffer from two major issues when profiling large and long-running applications: 1) runtime overhead and 2) memory overhead. Existing data-dependence profilers are either unable to profile large-scale applications with a typical resource budget or only report very limited information. In this paper, we propose an efficient approach to data-dependence profiling that can address both runtime and memory overhead in a single framework. Our technique, called SD3, reduces the runtime overhead by parallelizing the dependence profiling step itself. To reduce the memory overhead, we compress memory accesses that exhibit stride patterns and compute data dependences directly in a compressed format. We demonstrate that SD3 reduces the runtime overhead when profiling SPEC 2006 by a factor of 4.1x and 9.7x on eight cores and 32 cores, respectively. For the memory overhead, we successfully profile 22 SPEC 2006 benchmarks with the reference input, while the previous approaches fail even with the train input. In some cases, we observe more than a 20x improvement in memory consumption and a 16x speedup in profiling time when 32 cores are used."
According to the news editors, the research concluded: "We also demonstrate the usefulness of SD3 by showing manual parallelization followed by data dependence profiling results."
For more information on this research see: SD3: An Efficient Dynamic Data-Dependence Profiling Mechanism. IEEE Transactions on Computers, 2013;62(12):2516-2530. IEEE Transactions on Computers can be contacted at: Ieee Computer Soc, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Computers - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=12)
The news editors report that additional information may be obtained by contacting M. Kim, Intel Corp, Software & Serv Grp, Hudson, MA 01749, United States. Additional authors for this research include N.B. Lakshminarayana, H. Kim and C.K. Luk.
Keywords for this news article include: Hudson, Massachusetts, United States, Computer Research, North and Central America
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