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*.
- Lei Jiang, Langshi Chen, Judy Qiu,Performance Characterization of Multi-threaded Graph Processing Applications on Many-Integrated-Core Architecture . IEEE International Symposium on Performance Analysis of Systems and Software, April 2-4, 2018.
- Judy Qiu, Harp-DAAL for High Performance Big Data Computing , Intel Magazine, March 17, 2018.
- Bo Peng et. al, HarpLDA+: Optimizing Latent Dirichlet Allocation for Parallel Efficiency, the IEEE Big Data 2017 conference, December 11-14, 2017.
- B. Peng, B. Zhang, L. Chen, M. Avram, R. Henschel, C. Stewart, S. Zhu, E. Mccallum, L. Smith, T. Zahniser, O. Jon, J. Qiu. HarpLDA+: Optimizing Latent Dirichlet Allocation for Parallel Efficiency, in the Proceedings of the 10th IEEE International Conference on Cloud Computing (IEEE Cloud 2017), June 25-30, 2017 (IEEE Big Data 2017).
- Tutorial on Harp-DAAL: Welcome to HPCDC Tutorial at SC 2017 A high Performance Machine Learning Framework for HPC-Cloud at Intel® HPC Developer Conference (HPCDC) 2017 held in Sheraton Denver Downtown Hotel, Denver, Colorado, November 11-12, 2017.
- L. Chen, B. Peng, B. Zhang, T. Liu, Y. Zou, L. Jiang, R. Henschel, C. Stewart, Z. Zhang, E. Mccallum, T. Zahniser, O. Jon, J. Qiu. Benchmarking Harp-DAAL: High Performance Hadoop on Intel® Xeon Phi™ Clusters IEEE Cloud Computing, 2017.
- B. Zhang, B. Peng, J. Qiu, Parallelizing Big Data Machine Learning Applications with Model Rotation, book chapter in New Frontiers in High Performance Computing, ISO Press,2017.
- K. ZheN, M. Birla, D. Crandall, B. Zhang, J. Qiu, 3/15/2017. A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA, Indiana University.
- E. Gámiz, A. Bazavovb, C. Bernardc, C. DeTard, D. Due, A.X. El-Khadraf, E.D. Freelandg, Steven Gottliebh, U.M. Helleri, J. Komijanij, A.S. Kronfeldjk, J. Laihoe, P.B. Mackenziek, E.T.Neill, T. Primerm, J.N. Simonek, R. Sugarn, D. Toussaintm, R.S. Van de Waterk, and R. Zhou, 11/20/2016. Kaon semileptonic decays with Nf = 2+1+1 HISQ fermions and physical light-quark masses, Cornell University Library.
- R. Li, C. DeTar, D. Doerfler, S. Gottlieb, A. Jha, D. Kalamkar, D. Toussaint, 11/3/2016. MILC staggered conjugate gradient performance on Intel® Xeon Phi™ , Cornell University Library.
- C. DeTar, D. Doerfler, S. Gottlieb, A. Jha, B. Joo, D. Kalamkar, R. Li, D. Toussaint, 9/21/2016. MILC Staggered Conjugate Gradient Performance on Intel Xeon Phi, IXPUG.
- Jha, V. Morozov, J. Deslippe, 9/19/2016. Vectorization Strategies for Intel's 2nd Generation Intel Xeon Phi Architecture Codenamed Knights Landing, Argonne National Labs.
- DeTar, D. Doerfler, S. Gottlieb, A. Jha, B. Joo, D. Kalamkar, R. Li, D.Toussaint, 9/19/2016. MILC Staggered Conjugate Gradient Intel Xeon Phi, Argonne National Labs.
- Zhang, B.Peng, J. Qiu. High Performance LDA through Collective Model Data Communication Optimization , Indiana University, 9/1/2016.
- B. Zhang, B. Peng, J. Qiu. Model-Centric Computation Abstractions in Machine Learning Applications (BeyondMR of VLDB, 2016), San Francisco, CA, June 26 - July 01, 2016.
- B. Zhang, B. Peng, J. Qiu. High Performance LDA through Collective Model Communication Optimization , Proceedings of International Conference on Computational Science (ICCS2016) conference, June 6-8, 2016, San Diego, California.
- B. Zhang. A Collective Communication Layer for the Software Stack of Big Data Analytics, Doctoral Symposium. Proceedings of IEEE International Conference on Cloud Engineering (IC2E2016) Conference, April 4-8, 2016, Berlin, Germany.
- B. Zhang, B. Peng, J. Qiu. 2016, Parallelizing Big Data Machine Learning Algorithms with Model Rotation, Semantic Scholar, March 2016.
- B. Zhang, B. Peng, J. Qiu, Parallel LDA Through Synchronized Communication Optimizations, Indiana University, 1/20/2016.
- B. Zhang et. al, Harp: Collective Communication on Apache Hadoop* Proceedings of IEEE on Cloud Engineering (IC2E 2015), Tempe, Arizona, March 9-12, 2015.