Tutorial: Introduction to CUDA Fortran | GTC 2013

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CUDA Programming • Heterogeneous programming model – CPU and GPU are separate devices with separate memory spaces – Host code runs on the CPU • Handles data management for both host and device • Launches kernels which are subroutines executed on the GPU – Device code runs on the GPU • Executed by many GPU threads in parallel – Allows for incremental development
Heterogeneous Programming • Host = CPU and its memory • Device = GPU and its memory Host Device • Typical code progression – Allocate memory on host and device – Transfer data from host to device – Execute kernel (device computation) CPU DRAM PCIe GPU DRAM – Transfer result from device to host – Deallocate memory
Page 1 and 2: Introduction to CUDA Fortran
Page 3: Introduction • CUDA is a scalable
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 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
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Tutorial: Introduction to CUDA Fortran | GTC 2013

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