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#VISUAL STUDIO EXTENSIONS LOOPS CODE#

Warning C4849: OpenMP ‘simdlen’ clause ignored in ‘simd’ directiveįor the following code: #pragma omp simd simdlen(8) They will be parsed but ignored by the compiler with a warning issued for user’s awareness. None of the SIMD clauses are effective in Visual Studio 2019 at the time of this writing. We focused on vectorizing innermost loops by improving the vectorizer and alias analysis. Mycode.cpp(96) : info C5001: Omp simd loop vectorizedĪs the first step of supporting OpenMP SIMD we have basically hooked up the SIMD pragma with the backend vectorizer under the new switch. Mycode.cpp(90) : info C5002: Omp simd loop not vectorized due to reason ‘1200’įor loops that are vectorized, the compiler keeps silent unless a vectorization logging switch is provided:Ĭl -O2 -openmp:experimental -Qvec-report:2 mycode.cpp Mycode.cpp(84) : info C5002: Omp simd loop not vectorized due to reason ‘1200’ Note that the SIMD directive and its clauses cannot be compiled with the -openmp switch.įor loops that are not vectorized, the compiler will issue a message for each of them like below. The -openmp:experimental switch subsumes the -openmp switch which means it is compatible with all OpenMP 2.0 features. Future iterations of the compiler may use this switch to enable additional OpenMP features and new OpenMP-related switches may be added. The name reflects that it doesn’t enable any complete subset or version of an OpenMP standard. While the name of this switch is “experimental”, the switch itself, and the functionality it enables is fully supported and production-ready. This new switch enables additional OpenMP features not available under -openmp.

reduction( reduction-identifier : list ) : specify customized reduction operationsĪn OpenMP-SIMD-annotated program can be compiled with a new CL switch -openmp:experimental.lastprivate( list ) : specify data privatization with final value from the last iteration.private( list ) : specify data privatization.aligned( list ): the alignment of data.

linear( list ) : the linear mapping from loop induction variable to array subscription.

safelen( length ) : specify the vector dependency distance.

simdlen( length ) : specify the number of vector lanes.

In addition, the OpenMP SIMD directive can take the following clauses to further enhance the vectorization:

Hybrid acceleration is achievable with #pragma omp for simd to enable coarse-grained multithreading and fine-grained vectorization.

Nested loops can be coalesced into one loop and vectorized.

Loops with function calls are vectorizable.

The compiler is always allowed to ignore any vector dependencies that are present.

However, the compiler can do more with OpenMP SIMD. #pragma omp simdĪs you may know, C++ in Visual Studio already provides similar non-OpenMP loop pragmas like #pragma vector and #pragma ivdep. The compiler respects users’ intention to have multiple loop iterations executed simultaneously. By annotating a loop with an OpenMP SIMD directive, the compiler can ignore vector dependencies and vectorize the loop as much as possible. It is so far the most widely used OpenMP feature in machine learning according to our research. OpenMP SIMD, first introduced in the OpenMP 4.0 standard, mainly targets loop vectorization. We continue our journey in Visual Studio 2019 by adding support for OpenMP SIMD.

#VISUAL STUDIO EXTENSIONS LOOPS CODE#

As the world is moving into an AI era, we see a growing opportunity to improve code quality by expanding support of the OpenMP standard in Visual Studio. Since 2005, Visual Studio has supported the OpenMP 2.0 standard which focuses on multithreaded parallelization. Over the years the OpenMP standard has been expanded to support additional concepts such as task-based parallelization, SIMD vectorization, and processor offloading. The OpenMP C/C++ application program interface was originally designed to improve application performance by enabling code to be effectively executed in parallel on multiple processors in the 1990s. We are proud to start offering C/C++ OpenMP SIMD vectorization in Visual Studio 2019.

The SIMD extension of OpenMP provides users an effortless way to speed up loops by explicitly leveraging the vector unit of modern processors. Such code usually does mathematical computation like matrix transformation and manipulation and it is usually in the form of loops. In the era of ubiquitous AI applications there is an emerging demand of the compiler accelerating computation-intensive machine-learning code for existing hardware.