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Peter Pacheco received a PhD in mathematics from Florida State University. After completing graduate school, he became one of the first professors in UCLA’s “Program in Computing,” which teaches basic computer science to students at the College of Letters and Sciences there. Since leaving UCLA, he has been on the faculty of the University of San Francisco. At USF Peter has served as chair of the computer science department and is currently chair of the mathematics department.

His research is in parallel scientific computing. He has worked on the development of parallel software for circuit simulation, speech recognition, and the simulation of large networks of biologically accurate neurons. Peter has been teaching parallel computing at both the undergraduate and graduate levels for nearly twenty years. He is the author of Parallel Programming with MPI, published by Morgan Kaufmann Publishers.

De la contraportada

Learn how to fully harness the power of parallel computing using MPI, PThreads, and OpenMP.

An Introduction to Parallel Programming

Peter S. Pacheco, University of San Francisco

With the coming of multicore processors and the cloud, parallel computing is most certainly not a niche area off in a corner of the computing world. Parallelism has become central to the efficient use of resources, and this new textbook by Peter Pacheco will go a long way toward introducing students early in their academic careers to both the art and practice of parallel computing.

Duncan Buell Department of Computer Science and Engineering University of South Carolina

An Introduction to Parallel Programming illustrates fundamental programming principles in the increasingly important area of shared memory programming using Pthreads and OpenMP and distributed memory programming using MPI. More importantly, it emphasizes good programming practices by indicating potential performance pitfalls. These topics are presented in the context of a variety of disciplines including computer science, physics and mathematics. The chapters include numerous programming exercises that range from easy to very challenging. This is an ideal book for students or professionals looking to learn parallel programming skills or to refresh their knowledge.

Leigh Little Department of Computational Science The College at Brockport, The State University of New York

An Introduction to Parallel Programming is a well written, comprehensive book on the field of parallel computing. Students and practitioners alike will appreciate the relevant, up-to-date information. Peter Pacheco’s very accessible writing style combined with numerous interesting examples keeps the reader’s attention. In a field that races forward at a dizzying pace, this book hangs on for the wild ride covering the ins and outs of parallel hardware and software.

Kathy J. Liszka Department of Computer Science University of Akron

Parallel computing is the future and this book really helps introduce this complicated subject with practical and useful examples.

Andrew N. Sloss FBCS Consultant Engineer, ARM Author of ARM System Developer’s Guide

Parallel programming is no longer just for specialists. If you want to fully exploit the power of clusters and multicore processors, it's essential that you learn the basics of distributed- and shared-memory parallel programming. An Introduction to Parallel Programming uses a tutorial approach to show you how to develop effective parallel programs with MPI, PThreads, and OpenMP. Whether you are a student or a practicing professional, Peter Pacheco’s new book will show you how to design, debug, and evaluate the performance of distributed and shared-memory programs.

Features

• Features a tutorial approach, starting with small programming examples and building progressively to more challenging examples
• Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs
• Stresses hands-on development of parallel programs, using MPI, PThreads, and OpenMP programming models

About the Author

Peter S. Pacheco Professor Computer Science and Mathematics University of San Francisco