Computer architecture for scientists : principles and performance / Andrew A. Chien

Includes bibliographical references and index.

1. Computing and the transformation of society -- 2. Instruction sets, software, and instruction execution -- 3. Processors : small is fast and scaling -- 4. Sequential abstraction, but parallel implementation -- 5. Memories : exploiting dynamic locality -- 6. The general-purpose computer -- 7. Beyond sequential : parallelism in multi-core and the Cloud -- 8. Accelerators : customized architectures for performance -- 9. Computing performance : past, present, and future.

This book is for the growing community of scientists and even engineers who use computing, and seek a scientific understanding of computer architecture. Those who view computation as an intellectual multiplier, and consequently are interested in capabilities, scaling, and limits, not mechanisms. That is, the scientific principles behind computer architecture, and how to reason about hardware performance for higher-level ends. With the dramatic rise of both data analytics and artificial intelligence, there is a rapid growth in interest and progress in data science. And further a shift in the center of mass of computer science upward and boundary outward - into a wide variety of sciences (physical, biological, and social) as well as nearly every aspect of society