#SIMPLY FORTRAN CUDA SUPPORT MAC OS#
#SIMPLY FORTRAN CUDA SUPPORT CODE#
#SIMPLY FORTRAN CUDA SUPPORT ISO#

Since we want to use the zero copy features on these two, we will allocate them with cudaHostAlloc.

#SIMPLY FORTRAN CUDA SUPPORT ISO#

We need to do a couple of extra steps: call the CUDA allocator in C, and then pass the C pointer to Fortran using the function C_F_Pointer provided by the iso C bindings.Ī is the input array, C is the output array from the GPU computation. Since we are using a standard Fortran 90 compiler, we can't use the built in allocator ( it has no knowledge of pinned memory).

This is achieved with calls to cudaHostGetDevicePointer. These are the pointers that we will pass to the CUDA kernels.

Get the device pointers to the mapped memory.

Allocate the host mapped arrays: this is achieved with cudaHostAlloc with the flag cudaHostAllocMapped.

Set the device flag for mapping host memory: this is achieved with a call to the cudaSetDeviceFlags with the flag cudaDeviceMapHost.To declare the mapped array, we will need to perform the following steps:

If you are not familiar with the zero-copy feature in CUDA C, it allows compute kernels to share host system memory and provides zero-copy support for direct access to host system memory when running on many newer CUDA-enabled graphics processors.

#SIMPLY FORTRAN CUDA SUPPORT MAC OS#

Although the examples can be compiled and run on any supported operating system in a variety of development environments, the examples in this document are compiled from the command line as one would do under Linux or Mac OS X.The basic idea is to use the original CUDA C functions to allocate host arrays that are page-locked ( aka pinned) and with the right attributes to be used by the zero copy feature of CUDA. This document makes use of the PGI 11.x compilers, which can be obtained from. The second part of this document is a collection of case studies that demonstrate how the principles in the fuirst section are applied to real-world examples.

#SIMPLY FORTRAN CUDA SUPPORT CODE#

This document is divided into two main sections, the first is a tutorial on CUDA Fortran programming, from the basics of writing CUDA Fortran code to some tips on optimization. CUDA is a hybrid programming model, where both GPU and CPU are utilized, so CPU code can be incrementally ported to the GPU. Part of the appeal of parallel programming on GPUs using CUDA is that the programming model is simple and novices can get parallel code up and running very quickly. However, no experience with parallel programming (on the GPU or otherwise) is required. The reader of this book should be familiar with Fortran 90 concepts, such as modules, derived types, and array operations. CUDA Fortran is simply the Fortran analog to CUDA C. PGI’s CUDA Fortran should be distinguished from the PGI Accelerator product, which is a directive based approach to using the GPU. This document pertains to the latter approach. There are many ways to access the CUDA architecture from a programmer’s perspective, either through C/C++ from CUDA C and Open CL, or through Fortran using PGI’s CUDA Fortran.

The CUDA architecture was developed by NVIDIA to allow use of the GPU for general purpose computing without requiring the programmer to have a background in graphics. The goal here is to provide the reader with the fundamentals of GPU programming using CUDA Fortran as well as some typical examples without having the task of developing CUDA Fortran code becoming an end in itself. This document in intended for scientists and engineers who develop or maintain computer simulations and applications in Fortran, and who would like to harness parallel processing power of graphics processing units (GPUs) to accelerate their code.

Simply fortran cuda support