Co-processing SPMD Computation on GPUs in addition to CPUs with MapReduce Interface on Shared Memory System Outline Research Goal Overview Code Samples

Co-processing SPMD Computation on GPUs in addition to CPUs with MapReduce Interface on Shared Memory System Outline Research Goal Overview Code Samples www.phwiki.com

Co-processing SPMD Computation on GPUs in addition to CPUs with MapReduce Interface on Shared Memory System Outline Research Goal Overview Code Samples

Canosa, John, Contributing Editor has reference to this Academic Journal, PHwiki organized this Journal Co-processing SPMD Computation on GPUs in addition to CPUs with MapReduce Interface on Shared Memory SystemDate: 10/05/2012OutlineOverview GPU in addition to CPU ArchitecturesProgramming Tools on GPUs in addition to CPUsApplications on GPUs in addition to CPUsP in addition to a: MapReduce Framework on GPU’s in addition to CPU’sDesign ImplementationApplications in addition to EvaluationConclusion in addition to LessonsResearch Goalprovide a MapReduce programming model that works on HPC Clusters or Virtual Clusters cores on traditional Intel architecture chip, cores on GPU.

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MulticoreModest parallelismSIMD, MIMDFast as long as threading code OpenMP, PthreadsParallel Programming Models on Shared Memory SystemTask parallelismExplicit parallel threadsData parallelismOperate simultaneously on bulk data (SPMD)GPUMassive parallelismSIMTFast as long as vector codeCUDA, MAGMA OverviewCode SamplesSPMD as long as (int tid = 0;tidp in addition to a-cpu-task[tid],&exitstat)!=0) perror(“joining failed”); }// as long as SIMDvoid add(uint32-t a, uint32-t b, uint32-t c, int n) { as long as (int i=0; i Memory complexitySample: Matrix AlgebraGPU Tools: CUBLAS, MAGMA, PLASMA, OpenACC, Accelerate, CUDA, OpenCL OutlineOverview P in addition to a: MapReduce Framework on GPU’s in addition to CPU’sDesign ImplementationApplications in addition to EvaluationC-meansMatrix MultiplicationWord CountConclusion in addition to Lessons

P in addition to a: MapReduce Framework on GPU’s in addition to CPU’sCurrent Version 0.32Features:Run on multiple GPUsRun on GPUs in addition to CPUs simultaneouslyRegion Based memory managementAuto TuningIterative MapReduceLocal CombinerApplications:C-means clusteringMatrix MultiplicationWord countHeterogeneous MapReduce Programming Model P in addition to a Architecture 0.4GPU Host Mappers CUDA/MAGMAShuffle Intermediate Key/Value Pairs in CPU MemoryMerge OutputHeterogeneous MapReduce Interface (gpu-host-map, gpu-kernel-map(), cpu-host-map, cpu-thread-map)Meta-scheduler (split job into sub-jobs)IterationsGPU Kernel MappersSchedule map tasksCPU MappersSchedule map tasks31656121013721115491681123465789GPU Host ReducersCUDA/MAGMAGPU Reducers Schedule reduce tasksCPU ReducersSchedule reduce tasksMeta-scheduler (split job into sub-jobs)LocalCombiner

APISample Code of Heterogeneous MapReduce-device- void gpu-reduce(void KEY, ){int count = 0; as long as (int i=0;i

Multi Core ArchitectureSophisticated mechanism in optimizing instruction in addition to cachingCurrent trends: Adding many cores, MIC, many integrated coresMore SIMD: SSE3/AVXApplication specific extensions: VT-x, AES-NIFermi GPU ArchitectureGeneric many core GPUNot optimized as long as single-threaded per as long as mance, are designed as long as work requiring lots of throughputLow latency hardware managed thread switchingLarge number of ALU per “core” with small user managed cache per core Memory bus optimized as long as b in addition to width GPU Applications Classes

DGEMM using CPU in addition to GPU Per as long as mance of PMM using CPU in addition to GPU matrix algebra tools on shared memory systemPer as long as mance of PMM using CPU in addition to GPU matrix algebra tools on distributed memory systemCUDA Threading ModelOctober 5, 2012B524 Parallelism Languages in addition to SystemsEach thread uses indices to decide what data to work onblockIdx: 1D, 2D, or 3D (CUDA 4.0)threadIdx: 1D, 2D, or 3D CUDA: Thread ModelKernelA device function invoked by the host computerLaunches a grid with multiple blocks, in addition to multiple threads per blockBlocksIndependent tasks comprised of multiple threadsno synchronization between blocksSIMT: Single-Instruction Multiple-ThreadMultiple threads executing time instruction on different data (SIMD), can diverge if neccesaryImage from [3]

CUDA: Software StackImage from [5]CUDA: Program FlowCPUMain MemoryDevice MemoryGPU CoresPCI-ExpressDeviceHost

Canosa, John Contributing Editor

Canosa, John is from United States and they belong to Embedded Systems Design and they are from  San Francisco, United States got related to this Particular Journal. and Canosa, John deal with the subjects like Electronic Components and Semiconductors; Information/Knowledge Management; Software Applications

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