Is Parallel Programming Hard, And, If So, What Can You Do About It?
by Paul E. McKenney
Number of pages: 413
The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity. By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel projects.
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by Guy Blelloch - The MIT Press
Vector Models for Data-Parallel Computing describes a model of parallelism that extends and formalizes the Data-Parallel model on which the Connection Machine and other supercomputers are based. It presents many algorithms based on the model.
by G.C. Fox, R.D. Williams, P.C. Messina - Morgan Kaufmann Publishers
A clear illustration of how parallel computers can be successfully applied to large-scale scientific computations. The book demonstrates how various applications in physics, biology and other sciences were implemented on real parallel computers.
by Henri Casanova, et al. - CRC Press
This book provides a rigorous yet accessible treatment of parallel algorithms, including theoretical models of parallel computation, parallel algorithm design for homogeneous and heterogeneous platforms, complexity and performance analysis, etc.
by Ian Foster - Addison Wesley
Introduction to parallel programming and a guide for developing programs for parallel and distributed systems. Programs are developed in a methodical fashion and both cost and performance are considered at each stage in a design.