000 | 04024nam a22005415i 4500 | ||
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001 | 978-3-031-01737-7 | ||
003 | DE-He213 | ||
005 | 20240730163655.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2012 sz | s |||| 0|eng d | ||
020 |
_a9783031017377 _9978-3-031-01737-7 |
||
024 | 7 |
_a10.1007/978-3-031-01737-7 _2doi |
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050 | 4 | _aTK7867-7867.5 | |
072 | 7 |
_aTJFC _2bicssc |
|
072 | 7 |
_aTEC008010 _2bisacsh |
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072 | 7 |
_aTJFC _2thema |
|
082 | 0 | 4 |
_a621.3815 _223 |
100 | 1 |
_aKim, Hyesoon. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979873 |
|
245 | 1 | 0 |
_aPerformance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU) _h[electronic resource] / _cby Hyesoon Kim, Richard Vuduc, Sara Baghsorkhi, Jee Choi, Wen-mei W. Hwu. |
250 | _a1st ed. 2012. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2012. |
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300 |
_aXII, 88 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_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. | ||
650 | 0 |
_aElectronic circuits. _919581 |
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650 | 0 |
_aMicroprocessors. _979874 |
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650 | 0 |
_aComputer architecture. _93513 |
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650 | 1 | 4 |
_aElectronic Circuits and Systems. _979875 |
650 | 2 | 4 |
_aProcessor Architectures. _979876 |
700 | 1 |
_aVuduc, Richard. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979877 |
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700 | 1 |
_aBaghsorkhi, Sara. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979878 |
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700 | 1 |
_aChoi, Jee. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979879 |
|
700 | 1 |
_aHwu, Wen-mei W. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979880 |
|
710 | 2 |
_aSpringerLink (Online service) _979881 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031006098 |
776 | 0 | 8 |
_iPrinted edition: _z9783031028656 |
830 | 0 |
_aSynthesis Lectures on Computer Architecture, _x1935-3243 _979882 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01737-7 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c84863 _d84863 |