Efficient Algorithms for the Multicore and Heterogeneous Architecture: A Shortcut to the Efficient Utilization of the Multicore and Heterogeneous Architecture

Fan Ye

ISBN 10: 3841726674 / ISBN 13: 9783841726674

Published by Éditions universitaires européennes, 2016

About this Item

152 pages. 8.66x5.91x0.35 inches. In Stock.

Seller Inventory # __3841726674

Bibliographic Details

• Title: Efficient Algorithms for the Multicore and Heterogeneous Architecture: A Shortcut to the Efficient Utilization of the Multicore and Heterogeneous Architecture
• Publisher: Éditions universitaires européennes
• Publication Date: 2016
• Language: English
• ISBN 10: 3841726674
• ISBN 13: 9783841726674
• Binding: Paperback
• Condition: Brand New
• Weight: 0.27 kilograms

About this title

Synopsis

High performance computing (HPC) plays a fundamental role in tackling the most intractable problems across a wide range of disciplines and pushing the frontiers of science. It now becomes a third way to explore the unknown world besides theory and experiments. Manycore architecture and heterogeneous system is the main trend for future supercomputers. The programming methods, computing kernels, and parallel algorithms need to be thoroughly investigated on the basis of such parallel infrastructure. This book is driven by the real computational needs coming from scientific and industrial applications. For example in different fields of reactor physics such as neutronics or thermohydraulics, the eigenvalue problem and resolution of linear system are the key challenges that consume substantial computing resources. In this context, our objective is to design and improve the parallel computing techniques, including proposing efficient linear algebraic kernels and parallel numerical methods.

About the Author

Born in 1989, Fan YE received the Ph.D. degree in computer science from the Université Lille 1 Sciences et Technologies, Lille, France, in 2015. In 2016, he joined NVIDIA as a senior software engineer. His current working projects include deep learning optimization, accelerated machine learning methods, and GPU-based linear algebraic routines.