Introduction to Concurrency in Programming Languages

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A01=Craig E Rasmussen
A01=Matthew J. Sottile
A01=Timothy G. Mattson
algorithm
Algorithm Patterns
Amdahl's Law
Amdahl’s Law
Array Notation
Author_Craig E Rasmussen
Author_Matthew J. Sottile
Author_Timothy G. Mattson
Cache Coherence Protocol
Category=UB
Category=UKC
Category=UMX
Category=UMZ
Category=UYF
Cellular Automaton
Co-array Fortran
CoArray Fortran
concurrency control in software engineering
concurrent
Concurrent Programming
Concurrent Programs
Data Parallel Algorithms
data parallel patterns
distributed computing models
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Functional Language
Geometric Decomposition
High Level Language Constructs
HPCS
Lock Acquisition
Matthew Sottile
memory
Modern Languages
MPI Program
multi-core
Multi-core Processor
multicore processor optimisation
Multicore Processors
Multitasking Operating Systems
multithreaded algorithms
Null Pointer Exception
Omp Parallel
parallel
parallel languages
parallel programming
PGAS
posix
POSIX Threads
processor
Runtime System
shared memory systems
software
STM
synchronisation techniques
threads
transactional
Von Neumann Model

Product details

  • ISBN 9780367385156
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Exploring how concurrent programming can be assisted by language-level techniques, Introduction to Concurrency in Programming Languages presents high-level language techniques for dealing with concurrency in a general context. It provides an understanding of programming languages that offer concurrency features as part of the language definition.

The book supplies a conceptual framework for different aspects of parallel algorithm design and implementation. It first addresses the limitations of traditional programming techniques and models when dealing with concurrency. The book then explores the current state of the art in concurrent programming and describes high-level language constructs for concurrency. It also discusses the historical evolution of hardware, corresponding high-level techniques that were developed, and the connection to modern systems, such as multicore and manycore processors. The remainder of the text focuses on common high-level programming techniques and their application to a range of algorithms. The authors offer case studies on genetic algorithms, fractal generation, cellular automata, game logic for solving Sudoku puzzles, pipelined algorithms, and more.

Illustrating the effect of concurrency on programs written in familiar languages, this text focuses on novel language abstractions that truly bring concurrency into the language and aid analysis and compilation tools in generating efficient, correct programs. It also explains the complexity involved in taking advantage of concurrency with regard to program correctness and performance.

Matthew J. Sottile is a research associate and adjunct assistant professor in the Department of Computer and Information Sciences at the University of Oregon. He has a significant publication record in both high performance computing and scientific programming. Dr. Sottile is currently working on research in concurrent programming languages and parallel algorithms for signal and image processing in neuroscience and medical applications.

Timothy G. Mattson is a principal engineer at Intel Corporation. Dr. Mattson’s noteworthy projects include the world’s first TFLOP computer, OpenMP, the first generally programmable TFLOP chip (Intel’s 80 core research chip), OpenCL, and pioneering work on design patterns for parallel programming.

Craig E Rasmussen is a staff member in the Advanced Computing Laboratory at Los Alamos National Laboratory (LANL). Along with extensive publications in computer science, space plasma, and medical physics, Dr. Rasmussen is the principal developer of PetaVision, a massively parallel, spiking neuron model of visual cortex that ran at 1.14 Petaflops on LANL’s Roadrunner computer in 2008.

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