Parallel Programming: Practical Aspects, Models and Current Limitations

$205.00

Mikhail S. Tarkov, PhD (Editor)
Institute of Semiconductor Physics, Siberian Branch, Russian Academy of Sciences, Russia

Series: Mathematics Research Developments

Parallel programming is designed for the use of parallel computer systems for solving time-consuming problems that cannot be solved on a sequential computer in a reasonable time. These problems can be divided into two classes:

1. Processing large data arrays (including processing images and signals in real time)
2. Simulation of complex physical processes and chemical reactions

For each of these classes, prospective methods are designed for solving problems. For data processing, one of the most promising technologies is the use of artificial neural networks. Particles-in-cell method and cellular automata are very useful for simulation.

Problems of scalability of parallel algorithms and the transfer of existing parallel programs to future parallel computers are very acute now. An important task is to optimize the use of the equipment (including the CPU cache) of parallel computers. Along with parallelizing information processing, it is essential to ensure the processing reliability by the relevant organization of systems of concurrent interacting processes. From the perspective of creating qualitative parallel programs, it is important to develop advanced methods of learning parallel programming.

The above reasons are the basis for the creation of this book, chapters of which are devoted to solving these problems. We hope this book will be of interest to researchers, students and all those working in the field of parallel programming and high performance computing. (Imprint: Nova)

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Table of Contents

Table of Contents

Preface

Chapter 1 – Mapping Data Processing Neural Networks onto Distributed Computer Systems with Regular Structures (pp. 1-32)
Mikhail S. Tarkov (Institute of Semiconductor Physics SB RAS, Novosibirsk, Russia)

Chapter 2 – Mapping Parallel Program Graphs onto Graphs of Distributed Computer Systems by Neural Network Algorithms (pp. 33-58)
Mikhail S. Tarkov (Institute of Semiconductor Physics SB RAS, Novosibirsk, Russia)

Chapter 3 – Large-Scale and Fine-Grain Parallelism in Plasma Simulation (pp. 59-70)
A. Snytnikov (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia)

Chapter 4 – Numerical Modelling of Astrophysical Flow on Hybrid Architecture Supercomputers (pp. 71-116)
I. Kulikov, I. Chernykh, A. Snytnikov, V. Protasov, A. Tutukov, and B. Glinsky (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia)

Chapter 5 – Efficient Computational Approaches for Parallel Stochastic Simulation on Supercomputers (pp. 117-142)
Mikhail A. Marchenko (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia)

Chapter 6 – Lattice Gas Cellular Automata for a Flow Simulation and Their Parallel Implementation (pp. 143-158)
Yury G. Medvedev (Supercomputer Software Department, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia)

Chapter 7 – Parallel Simulation of Asynchronous Cellular Automata (pp. 159-174)
Konstantin Kalgin (Supercomputer Software Department, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia)

Chapter 8 – XPU: A C++ Metaprogramming Approach to Ease Parallelism Expression: Parallelization Methodology, Internal Design and Practical Application (pp. 175-198)
Nader Khammassi and Jean-Christophe Le Lann (Lab-STICC UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France)

Chapter 9 – An Approach to the Construction of Robust Systems of Interacting Processes (pp. 199-218)
Igor N. Skopin (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk, Russia)

Chapter 10 – Early Learning in Parallel Programming (pp. 219-230)
Igor N. Skopin (Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk, Russia)

Index

Reviews

Click here, to read the review by – Olga L. Bandman, Chief Researcher, Professor, Institute of Computational Mathematics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia.

 

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