Multicore Computing

Every area of science and engineering today has to process voluminous data sets. Using exact, or even approximate, algorithms to solve intractable problems in critical areas, such as computational biology, takes time that is exponential in some of the underlying parameters. Parallel computing addresses this issue and has become affordable with the

Sanguthevar Rajasekaran is the UTC Chair Professor of Computer Science and Engineering and director of the Booth Engineering Center for Advanced Technologies at the University of Connecticut. He received a Ph.D. in computer science from Harvard University. He is a fellow of the IEEE and the AAAS and an elected member of the Connecticut Academy of Science and Engineering. His research interests include bioinformatics, parallel algorithms, data mining, randomized computing, computer simulations, and combinatorial optimization. Lance Fiondella is an assistant professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Dartmouth. He received a Ph.D. in computer science and engineering from the University of Connecticut. His research interests include algorithms, reliability engineering, and risk analysis. Mohamed Ahmed is a program manager at Microsoft Windows Azure Mobile. He received a PhD in computer science and engineering from the University of Connecticut. His research interests include multi/many-cores technologies, high-performance computing, parallel programming, cloud computing, and GPU programming. Reda A. Ammar is a professor and the head of the Department of Computer Science and Engineering at the University of Connecticut. He received a PhD in computer science from the University of Connecticut. He is the president of the International Society of Computers and Their Applications and editor-in-chief of the International Journal on Computers and Their Applications. His primary research interests encompass distributed and high-performance computing and real-time systems.

Memory Hierarchy for Multicore and Manycore Processors. FSB: A Flexible Set Balancing Strategy for Last Level Caches. The SPARC Processor Architecture. The Cilk and Cilk++ Programming Languages. Multithreading in the PLASMA Library. Efficient Aho-Corasick String Matching on Emerging Multicore Architectures. Sorting on a Graphics Processing Unit (GPU). Scheduling DAG Structured Computations. Evaluating Multicore Processors and Accelerators for Dense Numerical Computations. Sorting on the Cell Broadband Engine. GPU Matrix Multiplication. Backprojection Algorithms for Multicore and GPU Architectures. Index.