Simply fortran cuda support11/21/2023 Performed simply by assignment statements. The device attribute is that transfers between the host and device can be One consequence of the strong typing in Fortran coupled with the presence of To the device attribute and allocated with the F90 allocate statement. X_d and y_d could have been declared with the allocatable in addition The GPU and transfer data between the CPU and GPU, CUDA Fortran uses theĭevice variable attribute to indicate which data reside in device memoryĪnd uses conventional means to allocate and transfer data. As with CUDA C, the host and device in CUDAįortran have separate memory spaces, both of which are managed from host code.īut while CUDA C declares variables that reside in device memory in aĬonventional manner and uses CUDA-specific routines to allocate data on The real arrays x and y are the host arrays, declared in the typicalįashion, and the x_d and y_d arrays are device arrays declared with Real :: x ( N ), y ( N ), a real, device :: x_d ( N ), y_d ( N ) Let’s begin our discussion of this program with the host code. That is performed on the GPU, and the program testSaxpy is the host code. The module mathOps above contains the subroutine saxpy, which is the kernel Module mathOps contains attributes ( global ) subroutine saxpy ( x, y, a ) implicit none real :: x (:), y (:) real, value :: a integer :: i, n n = size ( x ) i = blockDim % x * ( blockIdx % x - 1 ) + threadIdx % x if ( i > ( x_d, y_d, a ) y = y_d print *, "Size of arrays: ", N print *, 'Grid : ', grid print *, 'Threads per block: ', tBlock print *, "Constant a:", a print *, 'Average values ', sum ( abs ( x ))/ N, sum ( abs ( y ))/ N end program testSaxpy Version of SAXPY, explaining in detail what is done and why. SAXPY stands for “Single-precision A*X Plus Y”, and is a good “hello world”Įxample for parallel computation. Keeping this sequence of operations in mind, let’s look at a CUDA Fortran Transfer results from the device to the host.Transfer data from the host to the device.Declare and allocate a host and device memory.Sequence of operations for a CUDA Fortran code is: Given the heterogeneous nature of the CUDA programming model, a typical These kernels are executed by many GPU threads in parallel. While the device refers to the GPU and its memory.Ĭode running on the host manages the memory on both the host and deviceĪnd also launches kernels which are subroutines executed on the device. In CUDA, the host refers to the CPU and its memory, The CUDA programming model is a heterogeneous model in which both the CPUĪnd GPU are used. CUDA Programming Model Basicsīefore we jump into CUDA Fortran code, those new to CUDA will benefit from aīasic description of the CUDA programming model and some of the terminology There are a few differences in how CUDA concepts are expressed usingįortran 90 constructs, but the programming model for both CUDA Fortran andĬUDA Fortran is essentially Fortran with a few extensions that allow one toĮxecute subroutines on the GPU by many threads in parallel. If you are familiar with CUDA C, then you are already well on your way to usingĬUDA Fortran is based on the CUDA C runtime API. Rendering, but are general enough to be useful in many cases involvingĭata-parallelism, linear algebra, and other common use cases in scientificĬUDA Fortran is the Fortran interface to the CUDA parallel computing platform. GPU designs are optimized for the kind of computations found in graphics Parallel computational units with very high memory bandwidth, and potential Graphic processing units or GPUs have evolved into programmable, highly
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