Cache Replacement Policies

Calvin Lin

ISBN 10: 3031006348 / ISBN 13: 9783031006340

Published by Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book summarizes the landscape of cache replacement policies for CPU data caches. The emphasis is on algorithmic issues, so the authors start by defining a taxonomy that places previous policies into two broad categories, which they refer to as coarse-grained and fine-grained policies. Each of these categories is then divided into three subcategories that describe different approaches to solving the cache replacement problem, along with summaries of significant work in each category. Richer factors, including solutions that optimize for metrics beyond cache miss rates, that are tailored to multi-core settings, that consider interactions with prefetchers, and that consider new memory technologies, are then explored. The book concludes by discussing trends and challenges for future work. This book, which assumes that readers will have a basic understanding of computer architecture and caches, will be useful to academics and practitioners across the field.

Seller Inventory # 9783031006340

Bibliographic Details

• Title: Cache Replacement Policies
• Publisher: Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3031006348
• ISBN 13: 9783031006340
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 6D millimeters 235 millimeters width by 191 millimeters height by 6 millimeters depth
• Weight: 183 grams

About this title

Synopsis

This book summarizes the landscape of cache replacement policies for CPU data caches. The emphasis is on algorithmic issues, so the authors start by defining a taxonomy that places previous policies into two broad categories, which they refer to as coarse-grained and fine-grained policies. Each of these categories is then divided into three subcategories that describe different approaches to solving the cache replacement problem, along with summaries of significant work in each category. Richer factors, including solutions that optimize for metrics beyond cache miss rates, that are tailored to multi-core settings, that consider interactions with prefetchers, and that consider new memory technologies, are then explored. The book concludes by discussing trends and challenges for future work. This book, which assumes that readers will have a basic understanding of computer architecture and caches, will be useful to academics and practitioners across the field.

About the Author

Akanksha Jain is a Research Associate at The University of Texas at Austin. She received her Ph.D. in Computer Science from The University of Texas in August 2016. In 2009, she received B. Tech and M. Tech degrees in Computer Science and Engineering from the Indian Institute of Technology Madras. Her research interests are in computer architecture, with a particular focus on the memory system and on using machine learning techniques to improve the design of memory system optimizations.Calvin Lin is a University Distinguished Teacher Professor of Computer Science at The University of Texas at Austin. Lin received the BSE in Computer Science from Princeton University in 1985 (Magna Cum Laude) and the Ph.D. in Computer Science from the University of Washington in December 1992. Lin was a postdoc at the University of Washington until 1996, when he joined the faculty at Texas. Lins research takes a broad view of how compilers and computer hardware can be used to improve system performance,system security, and programmer productivity. He is also Director of UTs Turing Scholars Honors Program, and when he is notworking, he can be found chasing his two young sons or coaching the UT mens ultimate frisbee team.