OpenACC

OpenACC
Name	OpenACC
Paradigm	Parallel computing, Directive (programming)
Developer	OpenACC-Standard.org, Cray, NVIDIA, PGI, CAPS Enterprise
Latest release version	3.3
Latest release date	November 2021
Influenced by	OpenMP, CUDA
Operating system	Linux, Microsoft Windows
License	Open standard

OpenACC. It is a programming standard for parallel computing designed to simplify parallel programming of heterogeneous CPU/GPU systems. The model uses compiler directives to allow programmers to specify regions of code for offloading from a host CPU to an attached accelerator, such as those from NVIDIA or AMD. By providing a high-level, portable approach, it aims to make parallel systems more accessible to scientists and engineers without requiring deep expertise in low-level accelerator programming.

Overview

OpenACC is defined by a consortium of organizations including Cray, NVIDIA, and PGI. The standard provides a collection of compiler directives, library routines, and environment variables that manage data movement and parallel execution. Its primary goal is to enable performance portability across a wide range of high-performance computing architectures, from multi-core x86 processors to many-core accelerators. The approach is similar in philosophy to OpenMP, but is specifically tailored for accelerators and coprocessors. Implementations are provided by commercial compilers from companies like NVIDIA and open-source projects such as GCC.

History and development

The OpenACC specification was first announced in November 2011 by a collaboration between Cray, CAPS Enterprise, NVIDIA, and PGI. This initiative was a response to the growing adoption of general-purpose GPU computing, spearheaded by NVIDIA's CUDA platform, and the desire for a more portable, directive-based standard. Version 1.0 of the specification was released in 2011. Subsequent major releases, including version 2.0 in 2013 and version 3.0 in 2015, added features like support for C++ and Fortran, deeper device memory management, and interoperability with low-level models like CUDA. The latest version, 3.3, was released in 2021 by OpenACC-Standard.org.

Programming model

The programming model centers on directives, which are special comments in C, C++, or Fortran source code that instruct the compiler to parallelize loops and manage data. Key constructs include `#pragma acc parallel` to define a parallel region and `#pragma acc kernels` to indicate loops that may contain parallelism. The model handles complex tasks like data transfer between the host processor and the accelerator device automatically, though programmers can optimize this using data directives. It also includes a runtime API for more explicit control and supports asynchronous operations and events for overlapping computation and communication.

Comparison with OpenMP and CUDA

OpenACC is often compared to OpenMP, which began adding accelerator support with its OpenMP 4.0 specification. While both use directives, OpenACC was designed from the outset for accelerators, whereas OpenMP originated for shared-memory CPU parallelism. OpenACC directives are often considered more implicit and higher-level, while OpenMP's accelerator model can be more explicit. Compared to NVIDIA's proprietary CUDA platform, OpenACC offers greater portability at the cost of less low-level control. CUDA requires rewriting code in a GPU-specific language, whereas OpenACC aims to annotate existing Fortran or C++ code, similar to the approach of OpenMP.

Implementations and compiler support

Major commercial implementations include the NVIDIA HPC SDK (formerly the PGI compilers), which supports NVIDIA GPUs and x86 multi-core CPUs. The Cray Programming Environment has long supported OpenACC for its systems, including the Cray XC series. Open-source support is provided through the GCC compiler, starting with significant work in GCC 6, and the LLVM-based Clang compiler via plugins and the Flang frontend for Fortran. These compilers target devices from AMD, NVIDIA, and even Intel Xeon Phi, though support levels vary.

Applications and performance

OpenACC is widely used in scientific computing domains such as computational fluid dynamics, climate modeling, and molecular dynamics. Notable applications and codes that have been ported using it include the WRF atmospheric model and various projects at U.S. Department of Energy laboratories like Lawrence Livermore National Laboratory and Oak Ridge National Laboratory. Performance can approach that of hand-coded CUDA or OpenMP when compilers effectively optimize data locality and kernel execution, though results are highly dependent on the application, compiler maturity, and the underlying hardware from vendors like NVIDIA or AMD.

Category:Parallel computing Category:Programming languages Category:Application programming interfaces