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Computer Architecture for Scientists Principles and Performance

Authors

Andrew A. Chien
, University of Chicago
Published 2022

Description

The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives…

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Key features

  • Offers a high-level, approachable view of computing performance: how size scaling drives performance, implicit parallelism, dynamic locality, and parallelism
  • Presents principles and models for understanding and reasoning about how hardware architecture has enabled and continues to increase computing performance
  • Suitable for software-oriented computer scientists, data scientists, and anyone who needs to effectively exploit computing
  • Uses a simple subset of RISC-V to avoid instruction set complexities and provide access to a range of simple instructional tools
  • Coverage of parallelism and accelerators is unified, bridging hardware architecture features, and showing their connection to multicore and cloud programming
  • Impacts of fundamental scaling are well covered, providing an accessible and practically useful framework for software and data-science oriented computing students to understand future changes in the field

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