Advanced techniques and examples for materials science
著者
書誌事項
Advanced techniques and examples for materials science
（The art of high performance computing for computational science, v. 2）
Springer, c2019
大学図書館所蔵 件 / 全2件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book presents advanced and practical techniques for performance optimization for highly parallel processing. Featuring various parallelization techniques in material science, it is a valuable resource for anyone developing software codes for computational sciences such as physics, chemistry, biology, earth sciences, space science, weather, disaster prevention and manufacturing, as well as for anyone using those software codes.Chapter 1 outlines supercomputers and includes a brief explanation of the history of hardware. Chapter 2 presents procedures for performance evaluation, while Chapter 3 describes the set of tuned applications in materials science, nanoscience and nanotechnology, earth science and engineering on the K computer. Introducing the order-N method, based on density functional theory (DFT) calculation, Chapter 4 explains how to extend the applicability of DFT to large-scale systems by reducing the computational complexity. Chapter 5 discusses acceleration and parallelization in classical molecular dynamics simulations, and lastly, Chapter 6 explains techniques for large-scale quantum chemical calculations, including the order-N method.
This is the second of the two volumes that grew out of a series of lectures in the K computer project in Japan. The first volume addresses more basic techniques, and this second volume focuses on advanced and concrete techniques.
目次
Chapter 1: Supercomputers and application performance.- Chapter 2: Performance optimization of applications.- Chapter 3: Case studies of performance optimization of applications.- Chapter 4: O(N) methods.- Chapter 5: Acceleration of Classical Molecular Dynamics Simulations.- Chapter 6: Large scale quantum chemical calculation.
「Nielsen BookData」 より