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.