CUDA Fortran 2013 Brent Leback The Portland Group
[email protected] Why Fortran? . Rich legacy in the scientific community . Semantics – easier to vectorize/parallelize . Array descriptors . Modules . Fortran 2003 Today’s Fortran delivers many of the abstraction features of C++ without compromising performance Tesla C1060 Commodity Multicore x86 + Commodity Manycore GPUs 4 – 24 CPU Cores 240 GPU/Accelerator Cores Tesla C2050 “Fermi” 4 – 24 CPU Cores 448 GPU/Accelerator Cores Tesla K20 “Kepler” GPU Programming Constants The Program must: . Initialize/Select the GPU to run on . Allocate data on the GPU . Move data from host, or initialize data on GPU . Launch kernel(s) . Gather results from GPU . Deallocate data 6 What Does CUDA Fortran Look Like? attributes(global) subroutine mm_kernel ( A, B, C, N, M, L ) real :: A(N,M), B(M,L), C(N,L), Cij integer, value :: N, M, L integer :: i, j, kb, k, tx, ty real, device, allocatable, dimension(:,:) :: real, shared :: Asub(16,16),Bsub(16,16) Adev,Bdev,Cdev tx = threadidx%x . ty = threadidx%y i = blockidx%x * 16 + tx allocate (Adev(N,M), Bdev(M,L), Cdev(N,L)) j = blockidx%y * 16 + ty Adev = A(1:N,1:M) Cij = 0.0 Bdev = B(1:M,1:L) do kb = 1, M, 16 Asub(tx,ty) = A(i,kb+tx-1) call mm_kernel <<<dim3(N/16,M/16),dim3(16,16)>>> Bsub(tx,ty) = B(kb+ty-1,j) ( Adev, Bdev, Cdev, N, M, L) call syncthreads() do k = 1,16 C(1:N,1:L) = Cdev Cij = Cij + Asub(tx,k) * Bsub(k,ty) deallocate ( Adev, Bdev, Cdev ) enddo call syncthreads() .