LEADER 04328oam 2200697I 450 001 9910787572903321 005 20230124191000.0 010 $a0-429-10085-X 010 $a1-4665-7162-4 024 7 $a10.1201/b16051 035 $a(CKB)2670000000394416 035 $a(EBL)1378843 035 $a(SSID)ssj0001040386 035 $a(PQKBManifestationID)11668530 035 $a(PQKBTitleCode)TC0001040386 035 $a(PQKBWorkID)11001682 035 $a(PQKB)11487024 035 $a(MiAaPQ)EBC1378843 035 $a(OCoLC)869224365 035 $a(CaSebORM)9781466571648 035 $a(EXLCZ)992670000000394416 100 $a20180331h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aDesigning scientific applications on GPUs /$fedited by Raphael Couturier, University of Franche-Comte, Belfort, France 205 $a1st edition 210 1$aBoca Raton, FL :$cCRC Press,$d[2014] 210 4$dİ2014 215 $a1 online resource (496 p.) 225 1 $aChapman & Hall/CRC Numerical Analysis and Scientific Computing Series 225 0$aChapman & Hall/CRC numerical analysis and scientific computing ;$v21 300 $aDescription based upon print version of record. 311 $a1-4665-7164-0 320 $aIncludes bibliographical references. 327 $aFront Cover; Contents; List of Figures; List of Tables; Preface; I. Presentation of GPUs; 1. Presentation of the GPU architecture and of the CUDA environment; 2. Introduction to CUDA; II. Image processing; 3. Setting up the environment; 4. Implementing a fast median filter; 5. Implementing an efficient convolution operation on GPU; III. Software development; 6. Development of software components for heterogeneous many-core architectures; 7. Development methodologies for GPU and cluster of GPUs; IV. Optimization; 8. GPU-accelerated tree-based exact optimization methods 327 $a9. Parallel GPU-accelerated metaheuristics10. Linear programming on a GPU: a case study; V. Numerical applications; 11. Fast hydrodynamics on heterogeneous many-core hardware; 12. Parallel monotone spline interpolation and approximation on GPUs; 13. Solving sparse linear systems with GMRES and CG methods on GPU clusters; 14. Solving sparse nonlinear systems of obstacle problems on GPU clusters; 15. Ludwig: multiple GPUs for a complex fluid lattice Boltzmann application; 16. Numerical validation and performance optimization on GPUs of an application in atomic physics 327 $a17. A GPU-accelerated envelope-following method for switching power converter simulationVI. Other; 18. Implementing multi-agent systems on GPU; 19. Pseudorandom number generator on GPU; 20. Solving large sparse linear systems for integer factorization on GPUs 330 $aThis book covers designs of scientific applications for GPUs, beginning with a review of the principles of GPU programming. It then describes various scientific applications for GPUs and presents lessons learned. Scientific applications covered include computations on matrix operations, linear system solving, nonlinear system solving, image processing, and pseudo random number generation. Expert contributors discuss applications and the GPU porting in a pedagogical way, focusing their attention on the mechanisms they have used to obtain fast and interesting results--$cProvided by publisher. 410 0$aChapman & Hall/CRC Numerical Analysis and Scientific Computing Series 606 $aParallel programming (Computer science) 606 $aGraphics processing units$xProgramming 606 $aScience$xData processing 606 $aNumerical analysis$xComputer programs 606 $aApplication software$xDevelopment 615 0$aParallel programming (Computer science) 615 0$aGraphics processing units$xProgramming. 615 0$aScience$xData processing. 615 0$aNumerical analysis$xComputer programs. 615 0$aApplication software$xDevelopment. 676 $a006.6/63 686 $aMAT021000$aCOM000000$aCOM059000$2bisacsh 702 $aCouturier$b Raphael 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910787572903321 996 $aDesigning scientific applications on GPUs$93805433 997 $aUNINA