The Indiana University Intel Parallel Processing Center is a multi-component interdisciplinary center. The initial activities involve Center Director Judy Qiu, an Assistant Professor in the IU School of Informatics and Computing, and Distinguished Professor of Physics Steven Gottlieb. Qiu will be researching novel parallel systems supporting data analytics and Gottlieb will be adapting the physics simulation code of the MILC Collaboration to the Intel Xeon Phi processor. More generally, the focus of the Center will be grand challenges in high performance simulation and data analytics with innovative applications, and software development using the Intel architecture. Issues of programmer productivity and performance portability will be studied.
center administration
Prof. Steven Gottlieb, Physics Gottlieb is a founding member of the MILC Collaboration which studies Quantum Chromodynamics, one of nature's four fundamental forces. The open source MILC code is part of the SPEC benchmark and has been used as a performance benchmark for a number of supercomputer acquisitions. Gottlieb will be working on restructuring the MILC code to make optimal use of the SIMD vector units and many-core architecture of the Xeon Phi. These will be used in upcoming supercomputers at the National Energy Research Supercomputing Center (NERSC) and the Argonne Leadership Computing Center (ALCC). The MILC code currently is used for hundreds of millions of core-hours at NSF and DOE supercomputer centers.
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Prof. Judy Qiu, Informatics and Computing
Data analysis plays an important role in data-driven scientific discovery and commercial services. Prof. Qiu's earlier research has shown that previous complicated versions of MapReduce can be replaced by Harp (a Hadoop plug-in) that offers both data abstractions useful for high performance iterative computation and MPI-quality communication that can drive libraries like Mahout, MLlib, and DAAL on HPC and Cloud systems. We will select a subset of machine learning algorithms and implement them with optimal performance using Hadoop/Harp and Intel's library DAAL. The code will be tested on Intel’s Haswell and Xeon Phi architectures.
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