LEADER 04079nam 22006615 450 001 9910299586103321 005 20200703104754.0 010 $a3-319-71489-9 024 7 $a10.1007/978-3-319-71489-9 035 $a(CKB)4100000001381502 035 $a(DE-He213)978-3-319-71489-9 035 $a(MiAaPQ)EBC5210243 035 $a(PPN)222226056 035 $a(EXLCZ)994100000001381502 100 $a20171222d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomatic Tuning of Compilers Using Machine Learning$b[electronic resource] /$fby Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XVII, 118 p. 23 illus., 6 illus. in color.) 225 1 $aPoliMI SpringerBriefs,$x2282-2577 311 $a3-319-71488-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aBackground -- DSE Approach for Compiler Passes -- Addressing the Selection Problem of Passes using ML -- Intermediate Speedup Prediction for the Phase-ordering Problem -- Full-sequence Speedup Prediction for the Phase-ordering Problem -- Concluding Remarks. . 330 $aThis book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space exploration and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and that, in fact, many of the available passes tend to counteract one another. After providing a comprehensive survey of currently available methodologies, including many experimental comparisons with state-of-the-art compiler frameworks, the book describes new approaches to solving the problem of selecting the best compiler optimizations and the phase-ordering problem, allowing readers to overcome the enormous complexity of choosing the right order of optimizations for each code segment in an application. As such, the book offers a valuable resource for a broad readership, including researchers interested in Computer Architecture, Electronic Design Automation and Machine Learning, as well as computer architects and compiler developers. 410 0$aPoliMI SpringerBriefs,$x2282-2577 606 $aComputational intelligence 606 $aProgramming languages (Electronic computers) 606 $aComputer simulation 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aProgramming languages (Electronic computers). 615 0$aComputer simulation. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aSimulation and Modeling. 615 24$aArtificial Intelligence. 676 $a005.453 700 $aAshouri$b Amir H$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063891 702 $aPalermo$b Gianluca$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCavazos$b John$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSilvano$b Cristina$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299586103321 996 $aAutomatic Tuning of Compilers Using Machine Learning$92535168 997 $aUNINA