Tutorial: Introduction to CUDA Fortran | GTC 2013

More documents

Recommendations

Info

Calling CUBLAS from CUDA Fortran program cublasTest use cublas implicit none real, allocatable :: a(:,:),b(:,:),c(:,:) real, device, allocatable :: a_d(:,:),b_d(:,:),c_d(:,:) integer :: k=4, m=4, n=4 real :: alpha=1.0, beta=2.0, maxError allocate(a(m,k), b(k,n), c(m,n), a_d(m,k), b_d(k,n), c_d(m,n)) a = 1; a_d = a b = 2; b_d = b c = 3; c_d = c call cublasSgemm('N','N',m,n,k,alpha,a_d,m,b_d,k,beta,c_d,m) ! or sgemm(..) c=c_d write(*,*) 'Maximum error: ', maxval(abs(c-14.0)) deallocate (a,b,c,a_d,b_d,c_d) end program cublasTest
Calling Thrust from CUDA Fortran C wrapper for Thrust: csort.cu #include #include #include extern "C" { //Sort for integer arrays void sort_int_wrapper( int *data, int N) { } // Wrap raw pointer with a device_ptr thrust::device_ptr dev_ptr(data); // Use device_ptr in Thrust sort // algorithm thrust::sort(dev_ptr, dev_ptr+N); //Sort for single precision arrays void sort_float_wrapper( float *data, int N) { } thrust::device_ptr dev_ptr(data); thrust::sort(dev_ptr, dev_ptr+N); //Sort for double precision arrays void sort_double_wrapper(double *data, int N) { } } thrust::device_ptr dev_ptr(data); thrust::sort(dev_ptr, dev_ptr+N);
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: Calling CUBLAS from CUDA Fortran
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 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?