Scalable Algorithms

Novel scalable scientific algorithms are needed to enable key science applications and to exploit the computational power of largescale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and no synchronization points. Authored by two of the leading experts in this area, this book focuses on the latest advances in scalable algorithms for large scale systems.

Vassil Alexandrov is an ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre and the leader of Extreme Computing Research Group. He holds an MSc degree in Applied Mathematics from Moscow State University, Russia (1984) and a PhD degree in Parallel Computing from Bulgarian Academy of Sciences (1995). He has held previous positions at the University of Liverpool, UK (Departments of Statistics and Computational Mathematics and Computer Science, 1994-1999), the University of Reading, UK (School of Systems Engineering, 1999-2010, as a Professor of Computational Science leading the Computational Science research group until September 2010, and as the Director of the Centre for Advanced Computing and Emerging Technologies until July 2010).

He is a member of the Editorial Board of the Journal of Computational Science, Guest Editor of Mathematics and Computers in Simulation, Guest Editor of special issue on Scalable Algorithms for Large Scale Problems of the Journal of Computational Science. He is one of the founding fathers of the International Academy of Information Technology and Quantitative Management. His expertise and research interests are in the area of Computational Science encompassing Parallel and High Performance Computing, Scalable Algorithms for advanced Computer Architectures, Monte Carlo methods and algorithms. In particular, scalable Monte Carlo algorithms are developed for Linear Algebra, Computational Finance, Environmental Models, Computational Biology etc. In addition the research focuses on scalable and fault-tolerant algorithms for petascale architectures and the exascale architecture challenge. He currently leads the Extreme Computing research group at BSC focusing on solving problems with uncertainty on large scale computing systems applying the techniques and methods mentioned above. He has published over 100 papers in renowned refereed journals and international conferences and workshops in the area of his research expertise.

Jack Dongarra received a Bachelor of Science in Mathematics from Chicago State University in 1972 and a Master of Science in Computer Science from the Illinois Institute of Technology in 1973. He received his Ph.D. in Applied Mathematics from the University of New Mexico in 1980. He worked at the Argonne National Laboratory until 1989, becoming a senior scientist. He now holds an appointment as University Distinguished Professor of Computer Science in the Computer Science Department at the University of Tennessee and holds the title of Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL), Turing Fellow at Manchester University, and an Adjunct Professor in the Computer Science Department at Rice University. He is the director of the Innovative Computing Laboratory at the University of Tennessee. He is also the director of the Center for Information Technology Research at the University of Tennessee which coordinates and facilitates IT research efforts at the University.

He specializes in numerical algorithms in linear algebra, parallel computing, the use of advanced-computer architectures, programming methodology, and tools for parallel computers. His research includes the development, testing and documentation of