Multicore and GPU Programming

Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines. Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance Particular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systems Download source code, examples, and instructor support materials on the book's companion website

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  • Author : Gerassimos Barlas
  • Publisher : Elsevier
  • Pages : 698 pages
  • ISBN : 0124171400
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMulticore and GPU Programming

Multicore and GPU Programming

Multicore and GPU Programming
  • Author : Gerassimos Barlas
  • Publisher : Elsevier
  • Release : 16 December 2014
GET THIS BOOKMulticore and GPU Programming

Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge

Multicore and GPU Programming

Multicore and GPU Programming
  • Author : Gerassimos Barlas
  • Publisher : Morgan Kaufmann
  • Release : 15 December 2021
GET THIS BOOKMulticore and GPU Programming

Multicore and GPU Programming: An Integrated Approach offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, CUDA, and other current tools it teaches the design and development of software capable of taking advantage of today's computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author

Parallel Programming

Parallel Programming
  • Author : Thomas Rauber,Gudula Rünger
  • Publisher : Springer Science & Business Media
  • Release : 13 June 2013
GET THIS BOOKParallel Programming

Innovations in hardware architecture, like hyper-threading or multicore processors, mean that parallel computing resources are available for inexpensive desktop computers. In only a few years, many standard software products will be based on concepts of parallel programming implemented on such hardware, and the range of applications will be much broader than that of scientific computing, up to now the main application area for parallel computing. Rauber and Rünger take up these recent developments in processor architecture by giving detailed

GPU Programming in MATLAB

GPU Programming in MATLAB
  • Author : Nikolaos Ploskas,Nikolaos Samaras
  • Publisher : Morgan Kaufmann
  • Release : 25 August 2016
GET THIS BOOKGPU Programming in MATLAB

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage

Parallel and Concurrent Programming in Haskell

Parallel and Concurrent Programming in Haskell
  • Author : Simon Marlow
  • Publisher : "O'Reilly Media, Inc."
  • Release : 12 July 2013
GET THIS BOOKParallel and Concurrent Programming in Haskell

If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate

Computational Physics

Computational Physics
  • Author : Rubin H. Landau,Manuel J Páez,Cristian C. Bordeianu
  • Publisher : John Wiley & Sons
  • Release : 11 June 2015
GET THIS BOOKComputational Physics

The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming. This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific

Multicore Computing

Multicore Computing
  • Author : Sanguthevar Rajasekaran,Lance Fiondella,Mohamed Ahmed,Reda A. Ammar
  • Publisher : CRC Press
  • Release : 12 December 2013
GET THIS BOOKMulticore 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 advent of multicore architectures. However, programming multicore machines is much more difficult due to oddities existing in the architectures. Offering insights into different facets of this area, Multicore

Programming Multicore and Many-core Computing Systems

Programming Multicore and Many-core Computing Systems
  • Author : Sabri Pllana,Fatos Xhafa
  • Publisher : John Wiley & Sons
  • Release : 06 February 2017
GET THIS BOOKProgramming Multicore and Many-core Computing Systems

Programming multi-core and many-core computing systems Sabri Pllana, Linnaeus University, Sweden Fatos Xhafa, Technical University of Catalonia, Spain Provides state-of-the-art methods for programming multi-core and many-core systems The book comprises a selection of twenty two chapters covering: fundamental techniques and algorithms; programming approaches; methodologies and frameworks; scheduling and management; testing and evaluation methodologies; and case studies for programming multi-core and many-core systems. Program development for multi-core processors, especially for heterogeneous multi-core processors, is significantly more complex than for single-core processors.

Task Scheduling for Multi-core and Parallel Architectures

Task Scheduling for Multi-core and Parallel Architectures
  • Author : Quan Chen,Minyi Guo
  • Publisher : Springer
  • Release : 23 November 2017
GET THIS BOOKTask Scheduling for Multi-core and Parallel Architectures

This book presents task-scheduling techniques for emerging complex parallel architectures including heterogeneous multi-core architectures, warehouse-scale datacenters, and distributed big data processing systems. The demand for high computational capacity has led to the growing popularity of multicore processors, which have become the mainstream in both the research and real-world settings. Yet to date, there is no book exploring the current task-scheduling techniques for the emerging complex parallel architectures. Addressing this gap, the book discusses state-of-the-art task-scheduling techniques that are optimized for

Distributed and Parallel Architectures for Spatial Data

Distributed and Parallel Architectures for Spatial Data
  • Author : Alberto Belussi,Sara Migliorini
  • Publisher : MDPI
  • Release : 20 January 2021
GET THIS BOOKDistributed and Parallel Architectures for Spatial Data

This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data.

Programming Multicore and Many-core Computing Systems

Programming Multicore and Many-core Computing Systems
  • Author : Sabri Pllana,Fatos Xhafa
  • Publisher : John Wiley & Sons
  • Release : 23 January 2017
GET THIS BOOKProgramming Multicore and Many-core Computing Systems

Programming multi-core and many-core computing systems Sabri Pllana, Linnaeus University, Sweden Fatos Xhafa, Technical University of Catalonia, Spain Provides state-of-the-art methods for programming multi-core and many-core systems The book comprises a selection of twenty two chapters covering: fundamental techniques and algorithms; programming approaches; methodologies and frameworks; scheduling and management; testing and evaluation methodologies; and case studies for programming multi-core and many-core systems. Program development for multi-core processors, especially for heterogeneous multi-core processors, is significantly more complex than for single-core processors.

Parallel Programming for Modern High Performance Computing Systems

Parallel Programming for Modern High Performance Computing Systems
  • Author : Pawel Czarnul
  • Publisher : CRC Press
  • Release : 05 March 2018
GET THIS BOOKParallel Programming for Modern High Performance Computing Systems

In view of the growing presence and popularity of multicore and manycore processors, accelerators, and coprocessors, as well as clusters using such computing devices, the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such

An Introduction to Parallel Programming

An Introduction to Parallel Programming
  • Author : Peter Pacheco,Matthew Malensek
  • Publisher : Morgan Kaufmann
  • Release : 27 August 2021
GET THIS BOOKAn Introduction to Parallel Programming

An Introduction to Parallel Programming, Second Edition presents a tried-and-true tutorial approach that shows students how to develop effective parallel programs with MPI, Pthreads and OpenMP. As the first undergraduate text to directly address compiling and running parallel programs on multi-core and cluster architecture, this second edition carries forward its clear explanations for designing, debugging and evaluating the performance of distributed and shared-memory programs while adding coverage of accelerators via new content on GPU programming and heterogeneous programming. New and

Hands-On GPU Programming with Python and CUDA

Hands-On GPU Programming with Python and CUDA
  • Author : Dr. Brian Tuomanen
  • Publisher : Packt Publishing Ltd
  • Release : 27 November 2018
GET THIS BOOKHands-On GPU Programming with Python and CUDA

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key Features Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book Description Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how