Principles of High-performance Processor Design : For High Performance Computing, Deep Neural Networks and Data Science

Makino, Junichiro

ISBN 10: 3030768708 / ISBN 13: 9783030768706

Editore: Springer, 2021

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Dati bibliografici

• Titolo: Principles of High-performance Processor Design : For High Performance Computing, Deep Neural Networks and Data Science
• Editore: Springer
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030768708
• ISBN 13: 9783030768706
• Rilegatura: Rilegato
• Condizione: As New

Descrizione articolo

Riassunto

This book describes how we can design and make efficient processors for high-performance computing, AI, and data science. Although there are many textbooks on the design of processors we do not have a widely accepted definition of the efficiency of a general-purpose computer architecture. Without a definition of the efficiency, it is difficult to make scientific approach to the processor design. In this book, a clear definition of efficiency is given and thus a scientific approach for processor design is made possible. 
In chapter 2, the history of the development of high-performance processor is overviewed, to discuss what quantity we can use to measure the efficiency of these processors. The proposed quantity is  the ratio between the minimum possible energy consumption and the actual energy consumption for a given application using a given semiconductor technology. In chapter 3, whether or not this quantity can be used in practice is discussed, for many real-world applications. 
In chapter 4, general-purpose processors in the past and present are discussed from this viewpoint. In chapter 5, how we can actually design processors with near-optimal efficiencies is described, and in chapter 6 how we can program such processors.  This book gives a new way to look at the field of the design of high-performance processors.

Informazioni sull?autore

Junichiro Makino received PhD from the University of Tokyo. After he received PhD, he worked at University of Tokyo, the National Astronomical Observatory of Japan, and Tokyo Institute of Technology. Since Apr 2014, he is a subleader of the exascale computing project and the team leader of the Co-design team, AICS, RIKEN, and since Mar 2016 he works also at Kobe University. His research interests are stellar dynamics, large-scale scientific simulation and high-performance computing.

He has developed a series of special-purpose computers for many-body problems (GRAPE) and SIMD many-core processors (GRAPE-DR, MN-Core).