Computer Architecture for Scientists

Andrew A. Chien is William Eckhardt Professor at the University of Chicago, Director of the CERES Center for Unstoppable Computing, and a Senior Scientist at Argonne National Laboratory. Since 2017, he has served as Editor-in-Chief of the Communications of the ACM. He is currently a member of the National Science Foundation's CISE Directorate Advisory Board. Chien is a global research leader in parallel computing, computer architecture, clusters, and cloud computing, and has received numerous awards for his research. In 1994 he was named a National Science Foundation Young Investigator. Dr. Chien served as Vice President of Research at Intel Corporation from 2005-2010, and on advisory boards for the National Science Foundation, Department of Energy, Japan RWCP, and distinguished universities such as Stanford, UC Berkeley, EPFL, and the University of Washington. From 1998-2005, he was SAIC Chair Professor at UCSD, and prior to that, a professor at the University of Illinois. Dr. Chien is a Fellow of the ACM, Fellow of the IEEE, and Fellow of the AAAS, and earned his PhD, MS, and BS from the Massachusetts Institute of Technology.

'Andrew Chien's Computer Architecture for Scientists: Principles and Practice is a timely and much-needed treatment of how computer architecture impacts the scalability and performance of the computing systems and the data-driven processes that operate at the upper levels of the software stack. Aimed at software engineers and data scientists, this book provides a holistic and principled coverage of technology-agnostic concepts that govern the interplay between hardware capabilities and software performance. Understanding this interplay is crucial as it allows practitioners not only to reason about the performance of the systems they develop, but in fact to design these systems in a way that leverages the architectural features of the hardware systems on which they are built.' Azer Bestavros, Associate Provost for Computing and Data Sciences, Boston University

• Preface; 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; References, Index.