Big Data Analytics Performance for Large Out-of-Core Matrix Solvers on Advanced Hybrid Architectures.

摘要

This thesis examines the performance of large Out-Of-Core matrices to assess the optimal Big Data system performance of evolving computer architectures, based on the performance evaluation of a large Lower-Upper Matrix Decomposition (LUD) employing a highly tuned, I/O managed, slab based LUD software package developed by the Lockheed Martin Corporation. We present extensive benchmark studies conducted with this package on UMBC's Bluegrit and Bluewave clusters, and NASA-GFSC's Discover cluster systems.;Our results show speedup for a single node achieved by Phi Coprocessors relative to the host CPU SandyBridge processors is about a 1.5X improvement, which compares with the studies published by F.Masci (2013) where he obtains a 2-2.5x performance. The performances across 20 CPU nodes of SandyBridge obtains a uniform speedup of 0.5X over Westmere for problem sizes of 10K, 20K and 40K unknowns. With an Infiniband DDR, the performance of Nehalem processors is comparable to Westmere without the interconnect.