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      Computer Architecture for Scientists: Principles and Performance

      1 in stock

      Firm sale: non returnable item
      SKU 9781316518533 Categories ,
      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 c...

      £49.99

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      Description

      Product ID:9781316518533
      Product Form:Hardback
      Country of Manufacture:GB
      Title:Computer Architecture for Scientists
      Subtitle:Principles and Performance
      Authors:Author: Andrew A. Chien
      Page Count:264
      Subjects:Computer architecture and logic design, Computer architecture & logic design
      Description: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.
      Imprint Name:Cambridge University Press
      Publisher Name:Cambridge University Press
      Country of Publication:GB
      Publishing Date:2022-03-10

      Additional information

      Weight644 g
      Dimensions176 × 250 × 19 mm