CUDA_Training_Program_Day_1_Session_1.ppt

TrainingProgramonGPUProgrammingwithCUDA 31stJuly 7thAug 14thAug2011CUDATeachingCenter UoM TrainingProgramonGPUProgrammingwithCUDA SanathJayasenaCUDATeachingCenter UoM Day1 Session1Introduction Outline TrainingProgramDescriptionCUDATeachingCenteratUoMSubjectMatterIntroductiontoGPUComputingGPUComputingwithCUDACUDAProgrammingBasics July Aug2011 3 CUDATrainingProgram OverviewofTrainingProgram 3Sundays starting31stJulyScheduleandprogramoutlineMainresourcepersonsSanathJayasena JayathuSamarawickrama KishanWimalawarna LochandakaRanathungaDeptofComputerScience Eng DeptofElectronic Telecom Engineering ofFacultyofEngineering andFacultyofIT July Aug2011 CUDATrainingProgram 4 CUDATeachingCenter UoMwasselectedasaCTCAgroupofpeoplefrommultipleDepts July Aug2011 CUDATrainingProgram 5 GPUComputing Introduction GraphicsProcessingUnits GPUs high performancemany coreprocessorsthatcanbeusedtoaccelerateawiderangeofapplicationsGPGPU General PurposecomputationonGraphicsProcessingUnitsGPUsleadtheraceforfloating pointperformancesincestartof21stcenturyGPUsarebeingusedasparallelprocessors July Aug2011 CUDATrainingProgram 6 GPUComputing Introduction Generalcomputing untilendof20thcenturyReliedontheadvancesinhardwaretoincreasethespeedofsoftware appsSloweddownsincethenduetoPowerconsumptionissuesLimitedproductivitywithinasingleprocessorSwitchtomulti coreandmany coremodelsMultipleprocessingunits processorcores usedineachchiptoincreasetheprocessingpowerImpactonsoftwaredevelopers July Aug2011 CUDATrainingProgram 7 GPUComputing Introduction Asequentialprogramwillonlyrunononeofthecores whichwillnotbecomeanyfasterWitheachnewgenerationofprocessorsSoftwarethatwillcontinuetoenjoyperformanceimprovementwillbeparallelprogramsWhere multiplethreadsofexecutioncooperatetoachievethefunctionalityfaster July Aug2011 CUDATrainingProgram 8 CPU GPUPerformanceGap July Aug2011 CUDATrainingProgram 9 Source CUDAProg Guide4 0 CPU GPUPerformanceGap July Aug2011 CUDATrainingProgram 10 Source CUDAProg Guide4 0 GPGPU CUDA GPUdesignedasanumericcomputingengineWillnotperformwellonsometasksasCPUsMostapplicationswillusebothCPUsandGPUsCUDANVIDIA sparallelcomputingarchitectureaimedatincreasingcomputingperformancebyharnessingthepoweroftheGPUAprogrammingmodel July Aug2011 CUDATrainingProgram 11 MoreDetailsonGPUs GPUistypicallyacomputercard installedintoaPCIExpress16xslotMarketleaders NVIDIA Intel AMD ATI ExampleNVIDIAGPUs donatedtoUoM GeForceGTX480 Tesla2070 July Aug2011 12 CUDATrainingProgram ExampleSpecifications July Aug2011 13 CUDATrainingProgram CPUvs GPUArchitecture TheGPUdevotesmoretransistorsforcomputation July Aug2011 14 CUDATrainingProgram CPU GPUCommunication July Aug2011 15 CUDATrainingProgram CUDAArchitecture CUDAisNVIDA ssolutiontoaccesstheGPUCanbeseenasanextensiontoC C CUDASoftwareStack July Aug2011 16 CUDATrainingProgram CUDAArchitecture TherearetwomainpartsHost CPUpart SingleProgram SingleDataDevice GPUpart SingleProgram MultipleData July Aug2011 17 CUDATrainingProgram CUDAArchitecture GRIDArchitecture July Aug2011 18 CUDATrainingProgram TheGridAgroupofthreadsallrunningthesamekernelCanrunmultiplegridsatonce TheBlockGridscomposedofblocksEachblockisalogicalunitcontaininganumberofcoordinatingthreadsandsomeamountofsharedmemory SomeApplicationsofGPGPU ComputationalStructuralMechanics Bio InformaticsandLifeSciences ComputationalElectromagneticsandElectrodynamics ComputationalFinance July Aug2011 19 CUDATrainingProgram SomeApplications ComputationalFluidDynamics DataMining Analytics andDatabases ImagingandComputerVision MedicalImaging July Aug2011 20 CUDATrainingProgram SomeApplications MolecularDynamics NumericalAnalytics Weather Atmospheric OceanModelingandSpaceSciences July Aug2011 21 CUDATrainingProgram CUDAProgrammingBasics Accessing UsingtheCUDA GPUs YouhavebeengivenaccesstoourclusterUseraccountson192 248 8 13xItisaLinuxsystemCUDAToolkitandSDKfordevelopmentIncludesCUDAC C compilerforGPUs nvcc WillneedC C compilerforCPUcodeNVIDIAdevicedriversneededtorunprogramsForprogramstocommunicatewithhardware July Aug2011 CUDATrainingProgram 23 ExampleProgram1 global saysthefunctionistobecompiledtorunona device GPU not host CPU Anglebrackets forpassingparams argstoruntime July Aug2011 CUDATrainingProgram 24 include include global voidkernel void intmain void kernel printf HelloWorld n return0 AfunctionexecutedontheGPU device isusuallycalleda kernel ExampleProgram2 Part1 July Aug2011 CUDATrainingProgram 25 Ascanbeseeninnextslide WecanpassparameterstoakernelaswewouldwithanyCfunctionWeneedtoallocatememorytodoanythingusefulonadevice suchasreturnvaluestothehost ExampleProgram2 Part2 intmain void intc dev c cudaMalloc void July Aug2011 CUDATrainingProgram 26 ExampleProgram3 Withinhost CPU code callthekernelbyusing specifyingthegridsize numberofblocks and ortheblocksize numberofthreads moredetailslater July Aug2011 27 CUDATrainingProgram ExampleProgram3 contd July Aug2011 28 CUDATrainingProgram Note DetailsonthreadsandthreadIDswillcomelater ExampleProgram4 July Aug2011 29 CUDATrainingProgram Grids BlocksandThreads July Aug2011 30 CUDATrainingProgram Agridofsize6 3x2blocks Eachblockhas12threads 4x3 Conclusion InthissessionwediscussedIntroductiontoGPUComputingGPUComputingwithCUDACUDAProgrammingBasicsNextsessionDataParallelismCUDAProgrammingModelCUDAThreads July Aug2011 CUDATrainingProgram 31 ReferencesforthisSession C