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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Heinecke, A., Klemm, M., Pflüger, D., Bode, A., & Bungartz, H. J. (2012). Extending a highly parallel data mining algorithm to the intel ® many integrated core architecture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7156 LNCS, pp. 375–384). Springer Verlag. https://doi.org/10.1007/978-3-642-29740-3_42
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