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Parallel algorithms Made EasyThe complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms-appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book:* Presents basic concepts in clear and simple terms* Incorporates numerous examples to enhance students' understanding* Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering* Employs extensive illustrations of new design techniques* Discusses parallel algorithms in the context of PRAM model* Includes end-of-chapter exercises and detailed references on parallel computing.This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.

Parallel algorithms --- Parallel processing (Electronic computers) --- Algorithmes parallèles --- Parallélisme (Informatique) --- Parallel algorithms. --- Parallel processing (Electronic computers). --- Algorithmes parallèles --- Parallélisme (Informatique)

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519.6 --- 681.3*G13 --- Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning; determinants; eigenvalues and eigenvectors; error analysis; linear systems; matrix inversion; pseudoinverses; singular value decomposition; sparse, structured, and very large systems (direct and iterative methods) --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Computer science --- Mathematical optimization --- Parallel algorithms --- Operations research --- Optimisation mathématique --- Algorithmes parallèles --- Recherche opérationnelle --- Algorithms --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Simulation methods --- System analysis --- Optimisation mathématique. --- Algorithmes parallèles. --- Recherche opérationnelle.

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This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners.

Machine learning --- Data mining --- Parallel algorithms --- Parallel programs (Computer programs) --- Apprentissage automatique --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Machine Learning --- Machine learning. --- Data mining. --- Parallel algorithms. --- Parallel computer programs --- Parallel processing (Electronic computers) --- Computer programs --- Algorithms --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Learning, Machine --- Artificial intelligence --- Machine theory

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Parallel processing (Electronic computers) --- Parallel algorithms --- Parallélisme (Informatique) --- Algorithmes parallèles --- 681.3 *G10 --- 681.3*C12 --- 681.3*F12 --- 681.3*G22 --- Algorithms --- High performance computing --- Multiprocessors --- Parallel programming (Computer science) --- Supercomputers --- Computerwetenschap--?*G10 --- Multiple data stream architectures (multiprocessors): MIMD; SIMD; pipeline and parallel processors; array-, vector-, associative processors; interconnection architectures: common bus, multiport memory, crossbar switch --- Modes of computation: alternation and nondeterminism; parallelism; probabilistic computation; relations among modes; relativized computation --- Graph theory: graph algorithms; network problems; path and tree problems; trees--See also {681.3*F22} --- Parallel algorithms. --- Parallel processing (Electronic computers). --- 681.3*G22 Graph theory: graph algorithms; network problems; path and tree problems; trees--See also {681.3*F22} --- 681.3*F12 Modes of computation: alternation and nondeterminism; parallelism; probabilistic computation; relations among modes; relativized computation --- 681.3*C12 Multiple data stream architectures (multiprocessors): MIMD; SIMD; pipeline and parallel processors; array-, vector-, associative processors; interconnection architectures: common bus, multiport memory, crossbar switch