MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures
Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. To address the challenges above, we designed and implemented MICSVM, a highly efficient parallel SVM for x86 based multi-core and many core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi coprocessor (MIC).
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- Conference: IEEE 28th International Parallel and Distributed Processing Symposium (IPDPS 2014), May 19-23, 2014, Phoenix, Arizona, 809-818
- IEEE Computer Society, Los Alamitos, CA, United States(US).
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- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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- United States
- 42 ENGINEERING; Parallel Architectures; Modeling techniques; Machine Learning Mathod; Runtime System