Julian Shun Shared-Memory Parallelism Can Be Simple, Fast, and Scalable 9781970001884

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Автор: Julian Shun
Название: Shared-Memory Parallelism Can Be Simple, Fast, and Scalable
ISBN: 9781970001884
Издательство: Mare Nostrum (Eurospan)
Классификация:

Компьютерное программирование / разработка по

Архитектура и логическое построение вычислительных систем

ISBN-10: 1970001887
Обложка/Формат: Paperback
Страницы: 392
Вес: 0.76 кг.
Дата издания: 30.06.2017
Серия: Acm books
Язык: English
Размер: 235 x 191 x 23
Читательская аудитория: General (us: trade)
Ключевые слова: Computer programming / software development,Computer architecture & logic design, COMPUTERS / Programming / Algorithms,COMPUTERS / Programming / Parallel,COMPUTERS / Systems Architecture / General
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Поставляется из: Англии
Описание: Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings. This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results developed in this thesis serve to ease the transition into the multicore era.The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra , which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression.The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmic-depth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores.This is a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award.
Дополнительное описание: Programming and scripting languages: general|Computer architecture and logic design|Algorithms and data structures|Computer programming / software engineering

Shared-Memory Parallelism Can Be Simple, Fast, and Scalable, Julian Shun