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Author: Frank Nielsen
Publisher: Springer
ISBN: 3319219030
Category : Computers
Languages : en
Pages : 304
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Book Description
This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems. Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book.
Author: Frank Nielsen
Publisher: Springer
ISBN: 3319219030
Category : Computers
Languages : en
Pages : 304
Get Book Here
Book Description
This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems. Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book.
Author: Georg Hager
Publisher: CRC Press
ISBN: 1439811938
Category : Computers
Languages : en
Pages : 350
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Book Description
Written by high performance computing (HPC) experts, Introduction to High Performance Computing for Scientists and Engineers provides a solid introduction to current mainstream computer architecture, dominant parallel programming models, and useful optimization strategies for scientific HPC. From working in a scientific computing center, the author
Author: Sergei Kurgalin
Publisher: Springer Nature
ISBN: 3030275582
Category : Computers
Languages : en
Pages : 210
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Book Description
The book discusses the fundamentals of high-performance computing. The authors combine visualization, comprehensibility, and strictness in their material presentation, and thus influence the reader towards practical application and learning how to solve real computing problems. They address both key approaches to programming modern computing systems: multithreading-based parallelizing in shared memory systems, and applying message-passing technologies in distributed systems. The book is suitable for undergraduate and graduate students, and for researchers and practitioners engaged with high-performance computing systems. Each chapter begins with a theoretical part, where the relevant terminology is introduced along with the basic theoretical results and methods of parallel programming, and concludes with a list of test questions and problems of varying difficulty. The authors include many solutions and hints, and often sample code.
Author: John Levesque
Publisher: CRC Press
ISBN: 1420077066
Category : Computers
Languages : en
Pages : 242
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Book Description
High Performance Computing: Programming and Applications presents techniques that address new performance issues in the programming of high performance computing (HPC) applications. Omitting tedious details, the book discusses hardware architecture concepts and programming techniques that are the most pertinent to application developers for achievi
Author: Victor Eijkhout
Publisher: Lulu.com
ISBN: 1257992546
Category : Computers
Languages : en
Pages : 536
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Book Description
This is a textbook that teaches the bridging topics between numerical analysis, parallel computing, code performance, large scale applications.
Author: Thomas Sterling
Publisher: Morgan Kaufmann
ISBN: 032390212X
Category : Computers
Languages : en
Pages : 537
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Book Description
Performance Computing: Modern Systems and Practices is a fully comprehensive and easily accessible treatment of high performance computing, covering fundamental concepts and essential knowledge while also providing key skills training. With this book, students will begin their careers with an understanding of possible directions for future research and development in HPC, domain scientists will learn how to use supercomputers as a key tool in their quest for new knowledge, and practicing engineers will discover how supercomputers can employ HPC systems and methods to the design and simulation of innovative products. This new edition has been fully updated, and has been reorganized and restructured to improve accessibility for undergraduate students while also adding trending content such as machine learning and a new chapter on CUDA. - Covers enabling technologies, system architectures and operating systems, parallel programming languages and algorithms, scientific visualization, correctness and performance debugging tools and methods, GPU accelerators, and big data problems - Provides numerous examples that explore the basics of supercomputing while also providing practical training in the real use of high-end computers - Helps users with informative and practical examples that build knowledge and skills through incremental steps - Features sidebars of background and context to present a live history and culture of this unique field
Author: Robert Robey
Publisher: Simon and Schuster
ISBN: 1638350388
Category : Computers
Languages : en
Pages : 702
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Book Description
Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. Summary Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. About the technology Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs. What's inside Planning a new parallel project Understanding differences in CPU and GPU architecture Addressing underperforming kernels and loops Managing applications with batch scheduling About the reader For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. About the author Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. Table of Contents PART 1 INTRODUCTION TO PARALLEL COMPUTING 1 Why parallel computing? 2 Planning for parallelization 3 Performance limits and profiling 4 Data design and performance models 5 Parallel algorithms and patterns PART 2 CPU: THE PARALLEL WORKHORSE 6 Vectorization: FLOPs for free 7 OpenMP that performs 8 MPI: The parallel backbone PART 3 GPUS: BUILT TO ACCELERATE 9 GPU architectures and concepts 10 GPU programming model 11 Directive-based GPU programming 12 GPU languages: Getting down to basics 13 GPU profiling and tools PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS 14 Affinity: Truce with the kernel 15 Batch schedulers: Bringing order to chaos 16 File operations for a parallel world 17 Tools and resources for better code
Author: Lloyd Dudley Fosdick
Publisher: MIT Press
ISBN: 9780262061810
Category : Computers
Languages : en
Pages : 838
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Book Description
Designed for undergraduates, An Introduction to High-Performance Scientific Computing assumes a basic knowledge of numerical computation and proficiency in Fortran or C programming and can be used in any science, computer science, applied mathematics, or engineering department or by practicing scientists and engineers, especially those associated with one of the national laboratories or supercomputer centers. This text evolved from a new curriculum in scientific computing that was developed to teach undergraduate science and engineering majors how to use high-performance computing systems (supercomputers) in scientific and engineering applications. Designed for undergraduates, An Introduction to High-Performance Scientific Computing assumes a basic knowledge of numerical computation and proficiency in Fortran or C programming and can be used in any science, computer science, applied mathematics, or engineering department or by practicing scientists and engineers, especially those associated with one of the national laboratories or supercomputer centers. The authors begin with a survey of scientific computing and then provide a review of background (numerical analysis, IEEE arithmetic, Unix, Fortran) and tools (elements of MATLAB, IDL, AVS). Next, full coverage is given to scientific visualization and to the architectures (scientific workstations and vector and parallel supercomputers) and performance evaluation needed to solve large-scale problems. The concluding section on applications includes three problems (molecular dynamics, advection, and computerized tomography) that illustrate the challenge of solving problems on a variety of computer architectures as well as the suitability of a particular architecture to solving a particular problem. Finally, since this can only be a hands-on course with extensive programming and experimentation with a variety of architectures and programming paradigms, the authors have provided a laboratory manual and supporting software via anonymous ftp. Scientific and Engineering Computation series
Author: Subodh Kumar
Publisher: Cambridge University Press
ISBN: 1009276301
Category : Computers
Languages : en
Pages :
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Book Description
In modern computer science, there exists no truly sequential computing system; and most advanced programming is parallel programming. This is particularly evident in modern application domains like scientific computation, data science, machine intelligence, etc. This lucid introductory textbook will be invaluable to students of computer science and technology, acting as a self-contained primer to parallel programming. It takes the reader from introduction to expertise, addressing a broad gamut of issues. It covers different parallel programming styles, describes parallel architecture, includes parallel programming frameworks and techniques, presents algorithmic and analysis techniques and discusses parallel design and performance issues. With its broad coverage, the book can be useful in a wide range of courses; and can also prove useful as a ready reckoner for professionals in the field.
Author: John M. May
Publisher: Morgan Kaufmann
ISBN: 9781558606647
Category : Computers
Languages : en
Pages : 392
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Book Description
"I enjoyed reading this book immensely. The author was uncommonly careful in his explanations. I'd recommend this book to anyone writing scientific application codes." -Peter S. Pacheco, University of San Francisco "This text provides a useful overview of an area that is currently not addressed in any book. The presentation of parallel I/O issues across all levels of abstraction is this book's greatest strength." -Alan Sussman, University of Maryland Scientific and technical programmers can no longer afford to treat I/O as an afterthought. The speed, memory size, and disk capacity of parallel computers continue to grow rapidly, but the rate at which disk drives can read and write data is improving far less quickly. As a result, the performance of carefully tuned parallel programs can slow dramatically when they read or write files-and the problem is likely to get far worse. Parallel input and output techniques can help solve this problem by creating multiple data paths between memory and disks. However, simply adding disk drives to an I/O system without considering the overall software design will not significantly improve performance. To reap the full benefits of a parallel I/O system, application programmers must understand how parallel I/O systems work and where the performance pitfalls lie. Parallel I/O for High Performance Computing directly addresses this critical need by examining parallel I/O from the bottom up. This important new book is recommended to anyone writing scientific application codes as the best single source on I/O techniques and to computer scientists as a solid up-to-date introduction to parallel I/O research. Features: An overview of key I/O issues at all levels of abstraction-including hardware, through the OS and file systems, up to very high-level scientific libraries. Describes the important features of MPI-IO, netCDF, and HDF-5 and presents numerous examples illustrating how to use each of these I/O interfaces. Addresses the basic question of how to read and write data efficiently in HPC applications. An explanation of various layers of storage - and techniques for using disks (and sometimes tapes) effectively in HPC applications.
