Topics in Parallel and Distributed Computing

Sushil K. Prasad (BTech'85 IIT Kharagpur, MS'86 Washington State, Pullman; PhD'90 Central Florida, Orlando - all in Computer Science/Engineering) is a Professor of Computer Science at Georgia State University and Director of Distributed and Mobile Systems (DiMoS) Lab. He has carried out theoretical as well as experimental research in parallel and distributed computing, resulting in 140+ refereed publications, several patent applications, and about $3M in external research funds as principal investigator and over $6M overall (NSF/NIH/GRA/Industry).Sushil has been honored as an ACM Distinguished Scientist in Fall 2013 for his research on parallel data structures and applications. He was the elected chair of IEEE Technical Committee on Parallel Processing for two terms (2007-11), and received its highest honors in 2012 - IEEE TCPP Outstanding Service Award. Currently, he is leading the NSF-supported IEEE-TCPP curriculum initiative on parallel and distributed computing with a vision to ensure that all computer science and engineering graduates are well-prepared in parallelism through their core courses in this era of multi- and many-cores desktops and handhelds. His current research interests are in Parallel Data Structures and Algorithms, and Computation over Geo-Spatiotemporal Datasets over Cloud, GPU and Multicore Platforms. His homepage is www.cs.gsu.edu/prasad. Anshul Gupta is a Principal Research Staff Member in Mathematical Sciences department at IBM T.J. Watson Research Center. His research interests include sparse matrix computations and their applications in optimization and computational sciences, parallel algorithms, and graph/combinatorial algorithms for scientific computing. He has coauthored several journal articles and conference papers on these topics and a textbook titled "Introduction to Parallel Computing." He is the primary author of Watson Sparse Matrix Package (WSMP), one of the most robust and scalable parallel direct solvers for large sparse systems of linear equations. Arnold L. Rosenberg is a Research Professor in the Computer Science Department at Northeastern University; he also holds the rank of Distinguished University Professor Emeritus in the Computer Science Department at the University of Massachusetts Amherst. Prior to joining UMass, Rosenberg was a Professor of Computer Science at Duke University from1981 to 1986, and a Research Sta_ Member at the IBM Watson Research Center from 1965 to 1981. He has held visiting positions at Yale University and the University of Toronto. He was a Lady Davis Visiting Professor at the Technion (Israel Institute of Technology) in 1994, and a Fulbright Senior Research Scholar at the University of Paris-South in 2000. Rosenberg's research focuses on developing algorithmic models and techniques to exploit the new modalities of "collaborative computing" (wherein multiple computers cooperate to solve a computational problem) that result from emerging computing technologies. Rosenberg is the author or coauthor of more than 170 technical papers on these and other topics in theoretical computer science and discrete mathematics. He is the coauthor of the research book "Graph Separators, with Applications" and the author of the textbook "The Pillars of Computation Theory: State, Encoding, Nondeterminism"; additionally, he has served as coeditor of several books. Dr. Rosenberg is a Fellow of the ACM, a Fellow of the IEEE, and a Golden Core member of the IEEE Computer Society. Rosenberg received an A.B. in mathematics at Harvard College and an A.M. and Ph.D. in applied mathematics at Harvard University. More details are available at http://www.cs.umass.edu/~rsnbrg/. Alan Sussman is a Professor in the Department of Computer Science and Institute for Advanced Computer Studies at the University of Maryland. Working with students and other researchers at Maryland and other institutions he has published over 100 conference and journal papers and received several best paper awards in various topics related to software tools for high performance parallel and distributed computing, and has contributed chapters to 6 books. His research interests include peer-to-peer distributed systems, software engineering for high performance computing, and large scale data intensive computing. He is an associate editor for the Journal of Parallel and Distributed Computing, a subject area editor for the Parallel Computing journal, and an associate editor for IEEE Transactions on Services Computing. Software tools he has built have been widely distributed and used in many computational science applications, in areas such as earth science, space science, and medical informatics. He received his Ph.D. in computer science from Carnegie Mellon University. Charles Weems is co-director of the Architecture and Language Implementation lab at the University of Massachusetts. His current research interests include architectures for media and embedded applications, GPU computing, and high precision arithmetic. Previously he led development of two generations of a heterogeneous parallel processor for machine vision, called the Image Understanding Architecture, and co-directed initial work on the Scale compiler that was eventually used for the TRIPS architecture. He is the author of numerous articles, has served on many program committees, chaired the 1997 IEEE CAMP Workshop, the 1999 IEEE Frontiers Symposium, co-chaired IEEE IPDPS in 1999, 2000, and 2013, was general vice-chair for IPDPS from 2001 through 2005, and co-chairs the LSPP workshop. He has co-authored twenty-six introductory CS texts, and co-edited the book Associative Processing and Processors. He is a member of ACM, Senior Member of IEEE, a member of the Executive Committee of the IEEE TC on Parallel Processing, has been an editor for IEEE TPDS, Elsevier JPDC, and is an editor with Parallel Computing.

"Providing practical assistance for adding parallel programming at an early stage to undergraduate students in computer science is the aim of this book." --Computing Reviews

• Parallelism in PythonHands-On Parallelism Using ScratchNetworks and MPI for Cluster ComputingPDC Concepts in Digital LogicModules for threadsParallelism and Concurrency for Data Structures