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008			220601s2012 sz | s |||| 0|eng d
020			_a9783031017377
024	7		_a10.1007/978-3-031-01737-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aT385 _b2012 EB
100	1		_aKim, Hyesoon, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686850 _d1974-
245	1	0	_aPerformance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU) _cby Hyesoon Kim, Richard Vuduc, Sara Baghsorkhi, Jee Choi, Wen-mei W. Hwu
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XII, 88 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		_aGPU Design, Programming, and Trends -- Performance Principles -- From Principles to Practice: Analysis and Tuning -- Using Detailed Performance Analysis to Guide Optimization.
520			_aGeneral-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques. Table of Contents: GPU Design, Programming, and Trends / Performance Principles / From Principles to Practice: Analysis and Tuning / Using Detailed Performance Analysis to Guide Optimization.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9158747 _aProceso paralelo (Informática)
650		7	_2embne _9141143 _aGráficos de ordenador
700	1		_aVuduc, Richard _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686849
700	1		_aBaghsorkhi, Sara _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686851
700	1		_aChoi, Jee _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686852
700	1		_aHwu, Wen-mei W. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686853
776	0	8	_iPrinted edition: _z9783031006098
776	0	8	_iPrinted edition: _z9783031028656
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01737-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI