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Introduction
Published in Randall L. Eubank, Ana Kupresanin, Statistical Computing in C++ and R, 2011
Randall L. Eubank, Ana Kupresanin
Since OpenMP is for shared memory computing one of the main issues that arises is protection of variables and objects that need to be the property of only a single thread. The default behavior is that memory is shared. Memory protection is achieved through the use of appropriate clauses. In this regard our interest will focus on the clauses private that forces allocation of separate, nonshared, memory for the objects and variables that are named in the list accompanying the clause. These private regions cannot be accessed by other processors which protects the data being stored in these areas from outside corruption. - firstprivate which has a similar function to private except that the objects/variables are initialized in the parallel section using initial values from a serial code segment.
Parallel Computing Programming Basics
Published in Vivek Kale, Parallel Computing Architectures and APIs, 2019
This chapter gives a thematic introduction to parallel computing programming APIs and programming languages. It introduces the three categories of parallel programming APIs, namely, shared memory, message passing, and stream processing programming. OpenMP is an API for shared-memory-based parallel programming. ‘An overarching goal of OpenMP is to enable programmers to incrementally parallelize existing serial applications written in C/C++ and Fortran. Haskell, the parallel language in this category, is usually considered the successor of the Miranda programming language, which was one of the most important lazy functional programming languages of the 1980s.
Parallel Programming Using OpenMP
Published in Subrata Ray, Fortran 2018 with Parallel Programming, 2019
The general rules for the directives are as follows: Appropriate compiler directive must be used during the compilation of the program containing the Openmp directive.Each line containing a compiler directive cannot contain more than one directive name.Although some directives end with optional end directive name, it is recommended to use this optional end directive for the sake of readability.All the Openmp directives should start with !$omp.Both uppercase and lowercase letters may be used.Conditional compilation is possible with the starting characters !$ as the first two characters. This line is treated as a comment by non-Openmp compliant compiler but accepted as a valid statement by the Openmp compliant compiler. The line may be continued by using the ampersand character as the last character and using the characters !$ as the first two characters of the next line.There should be at least one blank after !$omp or !$; otherwise, the directive sentinels are treated as a comment.
Numerical investigation of distinct flow modes in a wide-gap spherical annulus using OpenMP
Published in Waves in Random and Complex Media, 2023
OpenMP API is composed of a dozen of compiler directives, some environment variables, and library routines. Due to the simplicity of the OpenMP API, a serial code can easily be modified as an OpenMP parallel code and the modifications are very few. OpenMP provides a gradual parallelization capability so that the user can see how effective a specific modification is. OpenMP works with the fork-and-join model for parallel execution. The program starts with a single process known as the master thread, the master thread works in a sequential way until it encounters the first parallel region construct. Then the master thread is divided into a team of parallel threads and the statements within the parallel region are executed in parallel. When the team completes the statements within the parallel region, they again synchronize and leave only the master thread. This fork-and-join model is shown in Figure 1. The details of this model and runtime functions for the implementation of the pipeline algorithm can be found in [48–53].
Study and evaluation of automatic offloading method in mixed offloading destination environment
Published in Cogent Engineering, 2022
Like GPUs, multi-core CPUs utilize many computational cores and parallelize processing to speed up. Unlike the GPU, the multi-core CPU has a common memory so there is no need to consider the overhead due to data transfer between the CPU and the GPU memory, which is often a problem with offloading to the GPU. In addition, the OpenMP specification is frequently used for parallelizing program processing on a multi-core CPU. OpenMP is a specification that specifies parallel processing and other processing for a program with directives such as #pragma omp parallel for. The OpenMP programmer is responsible for parallelizing the processing in OpenMP. When an attempt is made to parallelize the processing that cannot be parallelized, the compiler does not output an error and the calculation result becomes wrong.
Parallel simulation of railway pneumatic brake using openMP
Published in International Journal of Rail Transportation, 2020
Ícaro P. Teodoro, Jony J. Eckert, Pedro F. Lopes, Thiago S. Martins, Auteliano A. Santos
OpenMP is an Application Programming Interface (API) that enables shared-memory parallel execution, i.e. it allows different threads to access the same information in the memory synchronously. The API decides the amount of code each thread (and core) will execute, allowing for the parallelization of the code blocks. For example, suppose a function that needs the previous iteration values to determinate the current step. OpenMP will act as presented in Figure 6., i.e. the API will determinate that the idle thread calculates the value of the next time step for each . If the number of xs is higher than the number of threads, the application will hold the calculation of the remaining xs until there is an idle core, thus, improving speed, for there are several steps being calculated at the same clock cycle.