000			03937nam a22004335i 4500
999			_c387933 _d387933
001			387933
003			ES-MaUEC
005			20230509110904.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230509s2016 sz | s |||| 0|eng d
020			_a9783031017513
024	7		_a10.1007/978-3-031-01751-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D33 _b2016 EB
100	1		_aSardashti, Somayeh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688447
245	1	2	_aA Primer on Compression in the Memory Hierarchy _cby Somayeh Sardashti, Angelos Arelakis, Per Stenström, David A. Wood
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVIII, 70 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Architecture _x1935-3243
505	0		_aList of Figures -- List of Tables -- Preface -- Acknowledgments -- Introduction -- Compression Algorithms -- Cache Compression -- Memory Compression -- Cache/Memory Link Compression -- Concluding Remarks -- References -- Authors' Biographies .
520			_aThis synthesis lecture presents the current state-of-the-art in applying low-latency, lossless hardware compression algorithms to cache, memory, and the memory/cache link. There are many non-trivial challenges that must be addressed to make data compression work well in this context. First, since compressed data must be decompressed before it can be accessed, decompression latency ends up on the critical memory access path. This imposes a significant constraint on the choice of compression algorithms. Second, while conventional memory systems store fixed-size entities like data types, cache blocks, and memory pages, these entities will suddenly vary in size in a memory system that employs compression. Dealing with variable size entities in a memory system using compression has a significant impact on the way caches are organized and how to manage the resources in main memory. We systematically discuss solutions in the open literature to these problems. Chapter 2 provides the foundations of data compression by first introducing the fundamental concept of value locality. We then introduce a taxonomy of compression algorithms and show how previously proposed algorithms fit within that logical framework. Chapter 3 discusses the different ways that cache memory systems can employ compression, focusing on the trade-offs between latency, capacity, and complexity of alternative ways to compact compressed cache blocks. Chapter 4 discusses issues in applying data compression to main memory and Chapter 5 covers techniques for compressing data on the cache-to-memory links. This book should help a skilled memory system designer understand the fundamental challenges in applying compression to the memory hierarchy and introduce him/her to the state-of-the-art techniques in addressing them.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _9679195 _aCompresión de datos (Informática)
700	1		_aArelakis, Angelos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688448
700	1		_aStenström, Per _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688449
700	1		_aWood, David A _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031006234
776	0	8	_iPrinted edition: _z9783031028793
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01751-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI