2022 | ISBN: 1984659944 | English | 336 pages | True PDF | 27 MB
High Performance Computing (HPC) is an interdisciplinary field having important impact on many areas of science, technology, medicine, commerce, and national security. The graying of the HPC community is in evidence in academia, industry, and national institutions with an inadequate number of new young participants educated in the relevant sub-disciplines entering the field. As an interdisciplinary field, HPC engages talents in the broad areas of hardware architecture and design, system software, programming languages and tools, and parallel algorithms and computational techniques.
This book is an introductory on high-performance computing. The goal of this book is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. High-performance computing (HPC) plays an indispensable role in addressing many of the challenges of today's society. From cancer fighting to astrophysics, supercomputers are used to solve extremely large problems in a reasonable time. As all fields of science evolve, more and more computational power is required to process the ever-increasing datasets. There are big challenges associated with all of the following three broad domains: hardware, software, and applications. Hardware support for exascales can be accomplished by increased energy-friendly heterogeneity. This heterogeneity is no longer exclusive to processing elements (e.g., CPUs and accelerators), as modern memory hierarchies tend to expose a variety of memory subsystems composed of different technologies. Another hardware-related challenge is the significant increase in the number of compute nodes compared with previous systems, which brings the potential for serious congestion problems because of the higher generated network traffic. These hardware-related challenges pose major implications in terms of all software layers, which tend to be under a heavier burden to hide the complexity of managing the hardware resources from the programmer. The techniques you'll encounter cover the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this book is implementing the algorithms and techniques you'll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. This book, therefore, introduces the fundamentals of high-performance and parallel computing. It is designed for senior undergraduate students in computer science as well as graduate students interested in computational science and engineering. Please Login or Register to see this code