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Computer Architecture for Scientists: Principles and Performance [Hardcover]

$65.99       (Free Shipping)
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  • Category: Books (Computers)
  • Author:  Chien, Andrew A.
  • Author:  Chien, Andrew A.
  • ISBN-10:  1316518531
  • ISBN-10:  1316518531
  • ISBN-13:  9781316518533
  • ISBN-13:  9781316518533
  • Publisher:  Cambridge University Press
  • Publisher:  Cambridge University Press
  • Pages:  264
  • Pages:  264
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Oct-2022
  • Pub Date:  01-Oct-2022
  • SKU:  1316518531-11-MPOD
  • SKU:  1316518531-11-MPOD
  • Item ID: 104564887
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Oct 05 to Oct 07
  • Notes: Brand New Item. Not shipped to AK, HI, APO, FPO, AE.
A principled, high-level view of computer performance and how to exploit it. Ideal for software architects and data scientists.Rapidly growing cadres of sophisticated computing users need to understand how to exploit computing performance and the architecture of computers that give rise to it. With an accessible, principle-based approach, this book offers a high-level view of the four key pillars of performance. Ideal for computer, data, or social scientists and engineers.Rapidly growing cadres of sophisticated computing users need to understand how to exploit computing performance and the architecture of computers that give rise to it. With an accessible, principle-based approach, this book offers a high-level view of the four key pillars of performance. Ideal for computer, data, or social scientists and engineers.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 performance; Implicit parallelism  how a sequential program can be executed faster with parallelism; Dynamic locality  skirting physical limits, by arranging data in a smaller space; Parallelism  increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerators that have become increasingly important for mainstream applications.Preface; 1. Computing and the transformation of society; 2. Instruction sets, software, and instructilsU
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