Judy Qiu is an associate professor of Intelligent Systems Engineering at Indiana University. Her area of research is in data-intensive computing at the intersection of Cloud and high performance computing (HPC) multicore technologies. This includes a specialization on programming models that support iterative computation, ranging from storage to analysis which can scalably execute data intensive applications. Her research has been funded by NSF, NIH, Intel, Microsoft*, Google*, and Indiana University. She is the recipient of a NSF CAREER Award, Indiana University Trustees Award for Teaching Excellence, and Indiana University Outstanding Junior Faculty Award.
The Indiana University Intel® Parallel Processing Center (Intel® PCC) is a multi-component interdisciplinary center. The focus of the center is grand challenges in high performance simulation and data analytics with innovative applications, and software development using the Intel® architecture. Issues of programming productivity and performance portability will be studied.
High Performance Data analysis plays an important role in both data-driven scientific discovery and commercial services with research on novel parallel systems supporting data analytics and machine learning. The earlier research has introduced a novel HPC-Cloud convergence framework named Harp-DAAL and demonstrated that the combination of Big Data and HPC techniques can simultaneously achieve productivity and performance. Harp is a communication library (as a plugin to Apache Hadoop*) that orchestrates efficient node synchronization with a MapCollective computing model optimized for big data. Harp uses Intel® Data Analytics Accelerator Library (Intel® DAAL), for its highly optimized kernels on Intel® Xeon and Intel® Xeon Phi™. This way the high-level API of big data tools can be combined with intra-node fine-grained parallelism that is optimized for HPC platforms. Harp-DAAL shows how simulations and big data can use common programming environments with a runtime based on a rich set of collectives and libraries.
The project will select a subset of machine learning and data analysis algorithms from state-of-the-art libraries and tools and compare them on different platforms (including Xeon, Xeon Phi and other accelerators). We will benchmark the Harp-DAAL machine learning library and compare it with other Machine Learning and Deep Learning frameworks. We will produce a high-quality Machine Learning library with optimal performance, which compares with routines from Apache Mahout, MLlib, TensorFlow* and other libraries such as those in R and Python*.
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