**Parallel Complexity Theory**

by Ian Parberry

**Publisher**: Prentice Hall 1987**ISBN/ASIN**: 0273087835**ISBN-13**: 9780273087830**Number of pages**: 212

**Description**:

Parallel complexity theory is one of the fastest-growing fields in theoretical computer science. This rapid growth has led to a proliferation of parallel machine models and theoretical frameworks. This book presents a unified theory of parallel computation based on a network model.

Download or read it online for free here:

**Download link**

(8.6MB, PDF)

## Similar books

**How to Write Parallel Programs: A First Course**

by

**Nicholas Carriero, David Gelernter**-

**MIT Press**

In the near future every programmer will need to understand parallelism, a powerful way to run programs fast. The authors of this straightforward tutorial provide the instruction that will transform ordinary programmers into parallel programmers.

(

**9692**views)

**Parallel Computing Works!**

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.

(

**10233**views)

**Parallel and Distributed Computation: Numerical Methods**

by

**Dimitri P. Bertsekas, John Tsitsiklis**-

**Athena Scientific**

This is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the issues associated with such algorithms.

(

**11475**views)

**BIG CPU, BIG DATA: Solving the World's Toughest Problems with Parallel Computing**

by

**Alan Kaminsky**-

**Rochester Institute of Technology**

With the book BIG CPU, BIG DATA, my goal is to teach you how to write parallel programs that take full advantage of the vast processing power of modern multicore computers, compute clusters, and graphics processing unit (GPU) accelerators.

(

**7160**views)