Tutorial: Introduction to CUDA Fortran | GTC 2013

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Computing π with CUF Kernel ! Compute pi using a Monte Carlo method program compute_pi use precision use cudafor ! CUDA Fortran runtime use curand ! CURAND interface implicit none real(fp_kind), allocatable, pinned:: hostData(:) real(fp_kind), allocatable, device:: deviceData(:) real(fp_kind):: pival integer :: inside_cpu,inside, i, iter, Nhalf integer(kind=8) :: gen, N, seed=1234 ! Define how many numbers we want to generate N=2000 Nhalf=N/2 ! Allocate arrays on CPU and GPU allocate(hostData(N), deviceData(N)) ! Create pseudonumber generator call curandCreateGenerator(gen, & CURAND_RNG_PSEUDO_DEFAULT) ! Set seed call curandSetPseudoRandomGeneratorSeed( gen, seed) ! Generate N floats or double on device call curandGenerateUniform(gen, deviceData, N) ! Copy the data back to CPU to check result later hostData=deviceData ! Perform the test on GPU using CUF kernel inside=0 !$cuf kernel do do i=1,Nhalf if( (deviceData(i)**2+deviceData(i+Nhalf)**2) &
Computing π pgf90 -O3 -Mpreprocess -o pi_gpu precision_module.cuf curand_module.cuf pi.cuf -lcurand Compute pi in single precision (seed 1234) Samples= 10000 Pi=3.11120009 Error= 0.3039E-01 Samples= 100000 Pi=3.13632011 Error= 0.5273E-02 Samples= 1000000 Pi=3.14056396 Error= 0.1029E-02 Samples= 10000000 Pi=3.14092445 Error= 0.6683E-03 Samples= 100000000 Pi=3.14158082 Error= 0.1192E-04 Compute pi in single precision (seed 1234567) Samples= 10000 Pi=3.16720009 Error= 0.2561E-01 Samples= 100000 Pi=3.13919997 Error= 0.2393E-02 Samples= 1000000 Pi=3.14109206 Error= 0.5007E-03 Samples= 10000000 Pi=3.14106607 Error= 0.5267E-03 Mismatch between CPU/GPU 78534862 78534859 Samples= 100000000 Pi=3.14139414 Error= 0.1986E-03 Where is the difference coming from? if( ( hostData(i)**2+ hostData(i+Nhalf)**2)
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 and 68: CUDA Fortran Sorting with Thrust pr
Page 69 and 70: Convolution Example A B Perform con
Page 71 and 72: program driver use cudafor use cuff
Page 73: CUDA Libraries from CUDA Fortran
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
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Tutorial: Introduction to CUDA Fortran | GTC 2013

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