Tutorial: Introduction to CUDA Fortran | GTC 2013

More documents

Recommendations

Info

Computing π with CUDA Fortran π = 4 ∗ (Σ red points)/(Σ points) Simple example: –Generate random numbers ( CURAND) –Compute sum using of kernel loop directive –Compute sum using two stages reduction with Cuda Fortran kernels –Compute sum using single stage reduction with Cuda Fortran kernel –Accuracy
CUDA Libraries from CUDA Fortran • All the toolkit libraries have C interfaces • Use F90 interfaces and ISO C Binding to use from CUDA Fortran interface curandGenerateUniform !curandGenerateUniform(curandGenerator_t generator, float *outputPtr, size_t num); subroutine curandGenerateUniform(generator, odata, numele) & bind(C,name='curandGenerateUniform') use iso_c_binding integer(c_size_t),value:: generator !pgi$ ignore_tr odata real(c_float), device:: odata(*) integer(c_size_t),value:: numele end subroutine curandGenerateUniform !curandGenerateUniformDouble(curandGenerator_t generator, double *outputPtr, size_t num); subroutine curandGenerateUniformDouble(generator, odata, numele) & bind(C,name='curandGenerateUniformDouble') use iso_c_binding integer(c_size_t),value:: generator !pgi$ ignore_tr odata real(c_double), device:: odata(*) integer(c_size_t),value:: numele end subroutine curandGenerateUniformDouble end interface curandGenerateUniform
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: program driver use cudafor use cuff
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?