|Written in||C, C++, and Fortran|
OpenACC (for Open Accelerators) is a programming standard for parallel computing developed by Cray, CAPS, Nvidia and PGI. The standard is designed to simplify parallel programming of heterogeneous CPU/GPU systems.
Like in OpenMP, the programmer can annotate C, C++ and Fortran source code to identify the areas that should be accelerated using compiler directives and additional functions. Unlike OpenMP in versions before 4.0, code can be started not only on the CPU, but also on the GPU. With new features of OpenMP 4.0 this changed completely.
OpenACC members have worked as members of the OpenMP standard group to merge into OpenMP specification to create a common specification which extends OpenMP to support accelerators in a future release of OpenMP. These efforts resulted in a technical report for comment and discussion timed to include the annual Supercomputing Conference (November 2012, Salt Lake City) and to address non-Nvidia accelerator support with input from hardware vendors who participate in OpenMP.
In November 12, 2012, at the SC12 conference, a draft of the OpenACC version 2.0 specification was presented. New suggested capabilities include new controls over data movement (such as better handling of unstructured data and improvements in support for non-contiguous memory), and support for explicit function calls and separate compilation (allowing the creation and reuse of libraries of accelerated code).
Support of OpenACC is available in commercial compilers from PGI (from version 12.6), Cray, and CAPS. OpenUH is an Open64 based open source OpenACC compiler, developed by HPCTools group from University of Houston. An open source compiler, accULL, is also developed by the University of La Laguna (C language only). GNU GCC is also working on adding OpenACC support.
In a way similar to OpenMP 3.x on homogeneous system or the earlier OpenHMPP, the primary mode of programming in OpenACC is directives. The specifications also include a runtime library defining several support functions. To exploit them, user should include "openacc.h" in C or "openacc_lib.h" in Fortran; and then call acc_init() function.
OpenACC defines an extensive list of pragmas (directives), for example:
#pragma acc parallel #pragma acc kernels
#pragma acc data
Is the main directive to define and copy data to and from the accelerator.
#pragma acc loop
Is used to define the type of parallelism in a
#pragma acc cache #pragma acc update #pragma acc declare #pragma acc wait
There are some runtime API functions defined too: acc_get_num_devices(), acc_set_device_type(), acc_get_device_type(), acc_set_device_num(), acc_get_device_num(), acc_async_test(), acc_async_test_all(), acc_async_wait(), acc_async_wait_all(), acc_init(), acc_shutdown(), acc_on_device(), acc_malloc(), acc_free().
OpenACC generally takes care of work organisation for the target device however this can be overridden through the use of gangs and workers. A gang consists of workers and operates over a number of processing elements (as with a workgroup in OpenCL).
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