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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Extending a Highly Parallel Data Mining Algorithm to the Intel ® Many Integrated Core Architecture
verfasst von : Alexander Heinecke, Michael Klemm, Dirk Pflüger, Arndt Bode, Hans-Joachim Bungartz
Erschienen in: Euro-Par 2011: Parallel Processing Workshops
Verlag: Springer Berlin Heidelberg
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Extracting knowledge from vast datasets is a major challenge in data-driven applications, such as classification and regression, which are mostly compute bound. In this paper, we extend our SG
+ +
algorithm to the Intel
®
Many Integrated Core Architecture (Intel
®
MIC Architecture). The ease of porting an application to Intel MIC Architecture is shown: porting existing SSE code is very easy and straightforward. We evaluate the current prototype pre-release coprocessor board codenamed Intel
®
“Knights Ferry”. We utilize the pragma-based offloading programming model offered by the Intel
®
Composer XE for Intel MIC Architecture, generating both the host and the coprocessor code. We compare the achieved performance with an NVIDIA C2050 accelerator and show that the pre-release Knights Ferry coprocessor delivers better performance than the C2050 and exceeds the C2050 when comparing the productivity aspect of implementing algorithms for the coprocessors.