Name: Computer Architecture for Scientists: Principles and Performance (en Inglés)
Price: 256.18 PEN
Availability: InStock
Author: Chien, Andrew A.
ISBN: 9781316518533

portada Computer Architecture for Scientists: Principles and Performance (en Inglés)

Formato

Libro Físico

Autor

Andrew A. Chien

Editorial

Cambridge University Press

Año

2022

Idioma

Inglés

N° páginas

264

Encuadernación

Tapa Dura

Dimensiones

25.4 x 17.8 x 0.5 cm

Peso

0.64 kg.

ISBN13

9781316518533

Categorías

Arquitectura y diseño lógico de ordenadores

Computer Architecture for Scientists: Principles and Performance (en Inglés)

Andrew A. Chien (Autor) · Cambridge University Press · Tapa Dura

Libro Físico

S/ 256,18

S/ 512,36

Ahorras: S/ 256,18

50% descuento

Envío normal

Origen: Reino Unido (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 17 de Junio y el Lunes 01 de Julio.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Computer Architecture for Scientists: Principles and Performance (en Inglés)"

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.