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008 220601s2016 sz | s |||| 0|eng d
020 _a9783031017490
_9978-3-031-01749-0
024 7 _a10.1007/978-3-031-01749-0
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
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072 7 _aTJFC
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082 0 4 _a621.3815
_223
100 1 _aBordawekar, Rajesh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980046
245 1 0 _aAnalyzing Analytics
_h[electronic resource] /
_cby Rajesh Bordawekar, Bob Blainey, Ruchir Puri.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aX, 118 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Computer Architecture,
_x1935-3243
505 0 _aIntroduction -- Overview of Analytics Exemplars -- Accelerating Analytics -- Accelerating Analytics in Practice: Case Studies -- Architectural Desiderata for Analytics -- Bibliography -- Authors' Biographies .
520 _aThis book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
650 0 _aElectronic circuits.
_919581
650 0 _aMicroprocessors.
_980047
650 0 _aComputer architecture.
_93513
650 1 4 _aElectronic Circuits and Systems.
_980048
650 2 4 _aProcessor Architectures.
_980049
700 1 _aBlainey, Bob.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980050
700 1 _aPuri, Ruchir.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980051
710 2 _aSpringerLink (Online service)
_980052
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031006210
776 0 8 _iPrinted edition:
_z9783031028779
830 0 _aSynthesis Lectures on Computer Architecture,
_x1935-3243
_980053
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01749-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c84892
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