Programming multicore and many-core computing systems / / edited by Sabri Pllana, Fatos Xhafa

• Record Nr.: UNINA9910162722603321
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , 2017; ©2017
• ISBN: 1-119-33200-1; 1-119-33201-X; 1-119-33199-4
• Descrizione fisica: 1 online resource (521 pages) : illustrations, tables
• Collana: Wiley Series on Parallel and Distributed Computing; THEi Wiley ebooks.
• Disciplina: 005.2/75
• Soggetti: Parallel programming (Computer science); Coprocessors - Programming
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Multi- and many-cores, architectural overview for programmers -- Programming models for multicore and many-core computing systems -- Lock-free concurrent data structures -- Software transactional memory -- Hybrid/heterogeneous programming with OmpSs and its software/hardware implications -- Skeleton programming for portable many-core computing -- DSL stream programming on multicore architectures -- Programming with transactional memory -- Object-oriented stream programming -- Software-based speculative parallelization -- Autonomic distribution and adaptation -- PEPPHER: performance portability and programmability for heterogeneous many-core architectures -- Fastflow: high-level and efficient streaming on multicore -- Parallel programming framework for H.264/AVC video encoding in multicore systems -- Parallelizing evolutionary algorithms on GPGPU cards with the EASEA platform -- Smart interleavings for testing parallel programs -- Parallel performance evaluation and optimization -- A methodology for optimizing multithreaded system scalability on multicores -- Improving multicore system performance ; through data compression -- Programming and managing resources on accelerator-enabled clusters -- An approach for efficient execution of SPMD applications on multicore clusters -- Operating system and scheduling for future multicore and many-core platforms.