Tutorial: Introduction to CUDA Fortran | GTC 2013

More documents

Recommendations

Info

CUDA Fortran Sorting with Thrust program timesort use cudafor use thrust real, allocatable :: cpuData(:) real, allocatable, device :: gpuData(:) integer:: N=100000000,istat ! cuda events for elapsing type (cudaEvent) :: startEvent, stopEvent real :: time, random !Allocate CPU and GPU arrays allocate(cpuData(N), gpuData(N)) !Fill the host array with random data do i=1,N cpuData(i)=random(i) end do ! Create events istat = cudaEventCreate ( startEvent ) istat = cudaEventCreate ( stopEvent ) ! Send data to GPU gpuData = cpuData !Sort the data istat = cudaEventRecord ( startEvent , 0) call thrustsort(gpuData,N) istat = cudaEventRecord ( stopEvent , 0) istat = cudaEventSynchronize ( stopEvent ) istat = cudaEventElapsedTime( time, & startEvent , stopEvent) !Copy the result back cpuData = gpuData print *," Sorted array in:",time," (ms)" print *,"After sorting", & cpuData(1:5),cpuData(N-4:N) !Deallocate arrays deallocate(cpuData,gpuData) end program timesort $ ./timesort Sorting array of 100000000 single precision Sorted array in: 194.6642 (ms) After sorting 7.0585919E-09 1.0318221E-08 1.9398616E-08 3.1738640E-08 4.4078664E-08 0.9999999 0.9999999 1.000000 1.000000 1.000000
Convolution Example A B Perform convolution of a stack of 2D arrays in frequency space 1. Send arrays to GPU memory 2. Transform arrays to frequency domain using 2D FFT from CUFFT 3. Perform point-wise complex multiplication and scaling (FFT(IFFT(A))=len(A)*A) 4. Transform result back to physical domain 5. Send result back to CPU memory
Page 1 and 2:
Introduction to CUDA Fortran
Page 3 and 4:
Introduction • CUDA is a scalable
Page 5 and 6:
Heterogeneous Programming • Host
Page 7 and 8:
Data Transfers program copyData use
Page 9 and 10:
Data Transfers program copyData use
Page 11 and 12:
F90 Array Increment module simpleOp
Page 13 and 14:
CUDA Fortran - Device Code F90 modu
Page 15 and 16:
Execution Model Software Thread Thr
Page 17 and 18: Mapping Arrays to Thread Blocks •
Page 19 and 20: Built-in Variables for Device Code
Page 21 and 22: Multidimensional Example - Host •
Page 23 and 24: 2D Example - Device Code module sim
Page 25 and 26: Kernel Loop Directives (CUF Kernels
Page 27 and 28: Reduction using CUF Kernels • Com
Page 29 and 30: Compilation • pgfortran - PGI’s
Page 31 and 32: Separate Compilation module kernel_
Page 33 and 34: Host-Device Transfers • Host-devi
Page 35 and 36: Page-Locked Data Transfers • Page
Page 37 and 38: Asynchronous Data Transfers • Asy
Page 39 and 40: GPU/CPU Synchronization • cudaDev
Page 41 and 42: Outline • Introduction • Perfor
Page 43 and 44: Misaligned Data Access • Use arra
Page 45 and 46: Strided Data Access • Use array i
Page 47 and 48: Shared Memory • On-chip • All t
Page 49 and 50: Matrix Transpose - Shared Memory at
Page 51 and 52: Textures • Different pathway for
Page 53 and 54: Textures - Host Code program tex us
Page 55 and 56: Execution Configuration • GPUs ar
Page 57 and 58: Thread-Level Parallelism attributes
Page 59 and 60: Thread-Level Parallelism • Mimic
Page 61 and 62: Instruction-Level Parallelism No Sh
Page 63 and 64: Calling CUBLAS from CUDA Fortran
Page 65 and 66: Calling Thrust from CUDA Fortran C
Page 67: CUDA Fortran Sorting with Thrust pr
Page 71 and 72: program driver use cudafor use cuff
Page 73 and 74: CUDA Libraries from CUDA Fortran
Page 75 and 76: Computing π pgf90 -O3 -Mpreprocess
Page 77 and 78: Reductions on GPU 3 1 7 0 4 1 6 3
Page 79 and 80: Computing π with CUDA Fortran Kern
Page 81 and 82: Computing π with an Atomic Lock In
Page 83 and 84: Multi-GPU Programming • Multi-GPU
Page 85 and 86: Multi-GPU Memory Allocation • All
Page 87 and 88: Unified Virtual Addressing • CPU
Page 89 and 90: Direct Transfers • Once direct ac
Page 91 and 92: Direct Transfer Example - Device Co
Page 93 and 94: Direct Transfer Example - Host Code
show all

Tutorial: Introduction to CUDA Fortran | GTC 2013

Create successful ePaper yourself

Delete template?

Save as template?