Graphics processing units or GPUs have evolved into programmable, highly parallel computational units with very high memory bandwidth. GPU designs are optimized for the computations found in graphics rendering, but are general enough to be useful in many data-parallel, compute-intensive programs common in high-performance computing (HPC).
CUDA is a parallel computing platform and programming model for graphics processing unit (GPU). The original CUDA programming environment was comprised of an extended C compiler and tool chain, known as CUDA C. CUDA C allowed direct programming of the GPU from a high level language.
In mid 2009, PGI and NVIDIA cooperated to develop CUDA Fortran. CUDA Fortran includes a Fortran 2003 compiler and tool chain for programming NVIDIA GPUs using Fortran. Available in PGI 2010 and later release, CUDA Fortran is supported on Linux, OS X and Windows.
A free trial of CUDA Fortran is available as part of the standard PGI Fortran download packages. These packages include installation and configuration information, along with the CUDA Fortran Programming Guide and Reference.
CUDA supports four key abstractions: cooperating threads organized into thread groups, shared memory and barrier synchronization within thread groups, and coordinated independent thread groups organized into a grid. A CUDA programmer is required to partition the program into coarse grain blocks that can be executed in parallel. Each block is partitioned into fine grain threads, which can cooperate using shared memory and barrier synchronization. A properly designed CUDA program will run on any CUDA-enabled GPU, regardless of the number of available processor cores
When called from the host Fortran program, CUDA Fortran defined subroutines execute in parallel on the GPU. Calls to such subroutines—also known as kernels—specify how many parallel instances of the kernel to execute. Each instance is executed by a CUDA thread. CUDA threads are organized into thread blocks. Each thread has a global thread block index and a local thread index within its thread block.
Q What is the difference between OpenACC and CUDA Fortran? Why do we need both models?
A The OpenACC is a high-level implicit programming model for host+accelerator systems, similar to OpenMP for multi-core x64 systems. OpenACC:
CUDA Fortran is an analog to NVIDIA's CUDA C compiler. CUDA C and CUDA Fortran are lower-level explicit programming models with substantial runtime library components that give expert programmers direct control of all aspects of GPGPU programming. For example:
OpenACC together with CUDA Fortran enables acceleration using a high-level implicit model, or to drop into the lower-level explicit model of CUDA Fortran where needed.
OpenACC is included with the PGI Accelerator Fortran, C and C++ compilers. CUDA Fortran is included with the PGI Accelerator Fortran compilers.