Scientific Computing with Multicore and Accelerators

Jakub Kurzak is a research director in the Innovative Computing Laboratory in the Department of Electrical Engineering and Computer Science at the University of Tennessee. Dr. Kurzak is a program committee member for several international conferences and a reviewer for a number of top-ranking journals. His research focuses on utilizing multicore systems and accelerators for scientific computing.David A. Bader is a professor in the School of Computational Science and Engineering, College of Computing, and executive director for High Performance Computing at the Georgia Institute of Technology. He is a lead scientist in the DARPA Ubiquitous High Performance Computing (UHPC) program, an associate editor for several high-impact journals, and editor of the book Petascale Computing: Algorithms and Applications (CRC Press, 2008). An IEEE Fellow and member of the ACM, Dr. Bader has been an NSF CAREER Award recipient and has received awards from IBM, NVIDIA, Intel, Sun Microsystems, and Microsoft Research. His main areas of research are in parallel algorithms, combinatorial optimization, and computational biology and genomics.Jack Dongarra is a University Distinguished Professor of Electrical Engineering and Computer Science at the University of Tennessee, where he is the director of the Innovative Computing Laboratory and the director of the Center for Information Technology Research. He also is a member of the Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory, a Turing Fellow at the University of Manchester, and an adjunct professor in the Department of Computer Science at Rice University. A Fellow of the AAAS, ACM, IEEE, and SIAM, Dr. Dongarra has received numerous awards, including the first SIAM Special Interest Group on Supercomputing award for Career Achievement, the first IEEE Medal of Excellence in Scalable Computing, and the IEEE Sidney Fernbach Award. His research encompasses numerical algorithms in linear algebra, parallel computing, the use of advanced computer architectures, programming methodology, and tools for parallel computers.

• Dense Linear Algebra. Sparse Linear Algebra. Multigrid Methods. Fast Fourier Transforms. Combinatorial Algorithms. Stencil Algorithms. Bioinformatics. Molecular Modeling. Complementary Topics. Index.

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