Center for Hybrid Multicore Productivity Research (CHMPR)
Distinguished Computational Science Lecture Series

Computational Science at the
Argonne Leadership Computing Facility

Paul Messina
Director of Science Argonne National Laboratory
http://www.alcf.anl.gov

3:00 p.m. Thursday, 1 November 2012, ITE 456, UMBC

The goal of the Argonne Leadership Computing Facility (ALCF) is to extend the frontiers of science by solving problems that require innovative approaches and the largest-scale computing systems. ALCF’s current production computer has over 150,000 cores, and the system currently being readied for production – Mira, an IBM Blue Gene/Q system — has nearly one million cores.  How does one program such systems?  Are current software tools such as MPI and OpenMP available for such systems. Are scientific and engineering applications able to scale to such levels of parallelism?   Is resilience a new concern for 1,000,000 production codes on Mira This talk will address these questions and describe a sampling of projects that are using ALCF systems in their research.  Finally, the ways to gain access to ALCF resources will be presented.

Paul Messina is Director of Science at the ALCF. Dr. Messina guides the ALCF science teams using the IBM Blue Gene systems. In 2002-2004, he served as Distinguished Senior Computer Scientist at Argonne and as Adviser to the Director General at CERN (European Organization for Nuclear Research). Previously at Caltech, Dr. Messina served as Director of the Center for Advanced Computing Research, as Assistant Vice President for Scientific Computing, and as Faculty Associate for Scientific Computing. He led the Computational and Computer Science component of Caltech’s research project funded by the Academic Strategic Alliances Program of the Accelerated Strategic Computing Initiative. He also acted as Co-principal Investigator for the National Virtual Observatory and TeraGrid. At Argonne, he held a number of positions from 1973-1987 and was the founding Director of the Mathematics and Computer Science Division.

CSEE Colloquium

Energy Conservation in Biometric Algorithms

LCDR Robert Schultz
United States Naval Academy

1:00pm Friday, 19 October 2012, ITE 227

Whether using iris recognition to gain access to a secure facility or face recognition to unlock a cell phone, biometric signal processing is rapidly becoming a part of everyday life. Many algorithms are being implemented on portable devices that have a limited battery life. This talk will present some work, conducted at the USNA Center for Biometric Signal Processing, which indicates that significant energy savings can be obtained by using C versus Java and Integers versus software Floats in applications written for the Android operating system. A comparison of the effect of using Integers versus Floats on a modern iris recognition algorithm will also be presented.

LCDR Robert Schultz is a submarine officer that has been assigned as a Junior Permanent Military Professor of Electrical Engineering at the United States Naval Academy. His research interests include hyperspectral and biometric image processing. As a member of the USNA Center for Biometric Signal Processing, he has recently been working to identify more energy efficient methods for biometric algorithm implementation.

UMBC Information Systems Department
Fall 2012 Distinguished Lecture Series

The 'Learning Health System'
as the Consummate Informatics Challenge

Dr. Charles P. Friedman
Professor of Information and Public Health
Director of the Michigan Health Informatics Program
University of Michigan

11:00am 19 October 2012, ITE456, UMBC

It is widely recognized that the nation requires a Learning Health System (LHS) to provide higher quality, safer, and more affordable health care. An LHS is one that can routinely and securely aggregate data from disparate sources, convert the data to knowledge, and disseminate that knowledge, in actionable forms, to everyone who can benefit from it. Achieving a Learning Health System at national scale requires solution of a wide array of technology and policy problems and, as such, is the consummate challenge in health informatics. This presentation will describe the LHS, why it is vital to our future, the specific problems that must be addressed, and a pathway through which the nation might achieve an LHS.

Charles Friedman directs the Health Informatics program at the University of Michigan. Prior to joining the university in 2011, he was chief scientific officer of the Office of National Coordinator for Health Information Technology in the U.S. Department of Health and Human Services. From 2007-2009 he served as the nation’s deputy national coordinator for health IT. He has also held federal positions as associate director for research informatics and information technology at the National Heart Lung and Blood Institute at the National Institutes of Health and senior scholar at the National Library of Medicine. He led the creation of informatics programs during his professorships in medicine, information science, and biomedical engineering at the University of Pittsburgh and the University of North Carolina-Chapel Hill. He is the author of a well-known health informatics textbook and serves as associate editor of the Journal of the American Medical Informatics Association.

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CSEE Colloquium

Experiences Teaching Thousands Online

Professor Michael L. Littman
Computer Science, Brown University

1:00pm Friday, 26 October 2012, ITE 227, UMBC

Last Fall, a pair of well-respected computer scientists at Stanford offered their AI class for free to people everywhere via the Internet. Over 160,000 students signed up, spurring a worldwide conversation on the impact of online teaching on higher education and sending universities throughout the US scrambling to announce initiatives in this space. I had the good fortune to teach a class for one of the startups and will share my experiences.

Michael L. Littman is a professor of computer scientist at Brown University. He works mainly in reinforcement learning, but has done work in machine learning, game theory, computer networking, partially observable Markov decision process solving, computer solving of analogy problems and other areas. He has held faculty positions in the computer science departments at Duke University and Rutgers University, where he chaired the department from 2009 to 2012.  In the summer of 2012 he taught a massive open online course (MOOC) on graph algorithms.

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UMBC CSEE Colloquium

Real-time Causal Anomaly Detection for Hyperspectral Imagery

Yu-Lei Wang
Information and Communication Engineering College
Harbin Engineering University, China

1:00pm Friday, 12 October 2012, ITE 227, UMBC

Due to availability of very high spectral resolution, a hyperspectral imaging sensor is capable of uncovering many subtle signal sources which cannot be visually inspected or known by prior knowledge. Such signal sources generally appear as anomalies in the data. As a result, anomaly detection has received considerable interest in hyperspectral imaging. In anomaly detection real time causal processing is particularly important and crucial. This is because many anomalies, such as moving targets, may not stay long enough and the duration of their presence is very short. Most importantly, they may show up suddenly and instantly, then disappear quickly afterwards. Therefore, for an algorithm to be able to detect these targets in a timely fashion, the process must be real time. In addition, the data that can be used should be only those which have been visited and processed. So, the data processing must be also causal as well. Such causality is a very important pre-requisite to real time processing. Our work is believed to be the first work devoted to exploring this concept into anomaly detection. Specifically, it further derives a causal innovations information update equation for implementing real time causal anomaly detection. This concept which makes use of only innovations information provided by the pixel currently being processed without re-processing previous pixels is similar to those derived in Kalman filtering.

Yu-Lei Wang received her BS degree in Electrical Engineering from Harbin Engineering University, China in 2009 and is currently a Ph.D. student in the same university. Since December 2011 Ms. Wang has been working in the Remote Sensing Signal and Image Processing Laboratory at UMBC on hyperspectral anomaly detection under a China State Scholarship awarded by China Scholarship Council for a two-year visit to UMBC. Ms. Wang's research interest includes remote sensing image processing and vital sign signal processing.

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MS Thesis Defense

Using Mobile Data Collectors to Federate Clusters
of Disjoint Sensor Network Segments

Bhuvana Kalyanasundaram

11:00am Tuesday 2 October 2012, ITE 346

Wireless Sensor Networks (WSN) operating unattended in harsh environments have the higher probability of suffering from large scale damage, where many nodes fail simultaneously and the network gets partitioned into several disjoint segments. Restoring connectivity of structurally damaged WSN’s segments may be very urgent considering that they are employed to assist in risky missions. A similar scenario is when multiple standalone networks are to be federated to serve an emerging event such as an earthquake and conduct search-and-rescue. To deal with these scenarios, Mobile Data Mules (MDMs) are employed to establish intermittent links by moving around and carrying data from one segment to another. To limit data delivery latency and minimize the motion overhead, the travel path of the MDM should be shortened. We present a novel algorithm that groups the segments into k overlapping clusters based on the inter-segment proximity. Each cluster is assigned a distinct MDM to tour its segments. A segment that belongs to two clusters serves as a gateway that enables data transfer across clusters. Our algorithm minimizes the tour length for each MDM and sets the speed of the individual MDMs to rendezvous at the gateway nodes so that buffering space and time for inter-cluster traffic are minimized.

Committee: Drs. Mohamed Younis (chair), Charles Nicholas and Chintan Patel

Introduction to OpenACC

Mark Ebersole, NVIDIA

1:00pm Thursday, 27 September 2012, ENG 005a

Modern GPUs have grown past their graphics heritage and evolved into the world's most successful parallel computing architecture. The introduction of this talk will briefly cover where the GPU came from and how it turned into this processing powerhouse. We will then look into how to access this power by using the relatively new standard called OpenACC. This method is a balance between the maximum flexibility you get by writing your own kernels and the ease of use you get using existing libraries. We will then end the lecture looking at the existing GPU Computing ecosystem that works well with OpenACC.

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

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CSEE Colloquium

Volume Calculation of Magnetic Resonance
Tissues via Image Classification

Shih-Yu Chen
Remote Sensing Signal & Image Processing Laboratory
UMBC Computer Science and Electrical Engineering

1:00pm Friday 5 October 2012, ITE 227

Magnetic resonance (MR) tissue volume calculation is very important in medical diagnosis. A general approach is to first perform image classification of desired tissue substances slice by slice and then calculate tissue volumes via classified data samples in each slice. Two issues are generally involved; (1) selection of training samples which are slice-dependent, i.e., each slice requires its own specific training samples and (2) classification which must be carried out slice by slice individually because training samples obtained from one slice are not necessarily applicable to another. We develop a volume sphering analysis (VSA) approach which can process all MR image slices as one single image cube to calculate tissue volumes via image classification using only one set of training samples that is obtained from a single image slice. The proposed VSA using one set of training samples not only performs comparably to that using training samples specifically selected for individual image slices, but also saves significant amounts of selecting training samples and computing time.

Shih-Yu Chen received the BS degree in Electrical Engineering from Da-Yeh University in 2005, and the MS EE degree from National Chung Hsing University in 2010. He is currently a PhD (EE) student at UMBC. Mr. Chen's research interest includes medical image, remote sensing image and vital sign signal processing.

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