Computer Architecture for Scientists
Computer Architecture for Scientists
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.
PRP: 570.83 Lei
Acesta este Pretul Recomandat de Producator. Pretul de vanzare al produsului este afisat mai jos.
513.75Lei
513.75Lei
570.83 LeiLivrare in 2-4 saptamani
Descrierea produsului
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.
Detaliile produsului