OpenMP: Introduction to OpenMP (Part 2)

Introduction to OpenMP: Harnessing the Power of Parallel Computing (Part 2)

Introduction to OpenMP: Harnessing the Power of Parallel Computing (Part 2)

Concurrency vs. Parallelism and Application

In the world of parallel programming, understanding the concepts of concurrency and parallelism is crucial. Concurrency refers to the ability of multiple tasks or processes to make progress independently, while parallelism involves the simultaneous execution of multiple tasks. An application is considered concurrent when it consists of multiple tasks that can be executed independently. On the other hand, an application is parallel when these tasks are executed simultaneously across multiple processing units or cores. By identifying the concurrency within an application, we can effectively exploit it to achieve parallelism and improve performance.

Steps to Harness Concurrency and Achieve Parallelism

  1. Identify Concurrency: The first step in harnessing concurrency is to analyze an application and identify the tasks or operations that can be executed independently. These independent tasks are potential candidates for parallel execution.
  2. Algorithmic Strategy: Once the concurrency is identified, the next step is to develop an algorithmic strategy that can exploit this concurrency. This involves designing a parallel algorithm that distributes the workload across multiple threads or processing units efficiently.
  3. Choose Programming Language: After determining the algorithmic strategy, the choice of programming language becomes essential. OpenMP, being a widely used API for parallel programming, offers excellent support for exploiting concurrency in C, C++, and Fortran.

Construction of OpenMP

  1. Shared Address Space Hardware: At the low level, OpenMP leverages shared address space hardware, which allows multiple threads to access the same memory space.
  2. Operating System (OS) and Multithreading: On top of the shared address space hardware, the operating system manages the threads and provides multithreading capabilities. This allows multiple threads to run concurrently on the available processing units.
  3. OpenMP Runtime Library: OpenMP provides a runtime library that sits on top of the operating system's multithreading capabilities. It offers a set of functions and procedures that enable developers to parallelize their code and control the behavior of parallel execution.
  4. Directives, Environment, Compiler OpenMP, Library Variables: OpenMP provides a set of directives, environment variables, compiler flags, and library functions that allow developers to define parallel regions, specify the level of parallelism, and control data sharing among threads.
  5. Applications and End Users: Finally, developers and end users utilize OpenMP to parallelize their applications and achieve improved performance by harnessing the power of concurrency and parallelism.

Basic Syntax of OpenMP

OpenMP follows a directive-based approach, where specific directives are added to the code to indicate the areas that can be parallelized. The basic syntax of an OpenMP directive includes a pragma followed by the directive keyword and optional clauses. For example, the #pragma omp parallel directive is used to create a parallel region in the code.

Example of a Structured Block in OpenMP

      #include <stdio.h>
      #include <omp.h>

      int main() {
        int num_threads;

        #pragma omp parallel
          num_threads = omp_get_num_threads();
          printf("Hello World from Thread %d of %d\n", omp_get_thread_num(), num_threads);

        return 0;

In the above example, the #pragma omp parallel directive creates a parallel region, and the code within the curly braces is executed by multiple threads. The omp_get_num_threads() and omp_get_thread_num() functions retrieve the total number of threads and the ID of each thread, respectively. The output will show the "Hello World" message from each thread, demonstrating parallel execution.


  • "A New Kind of Science" by Stephen Wolfram
  • "Parallel Programming with OpenMP" by Michael J. Quinn
  • "OpenMP: Portable Multithreaded Programming for Shared Memory Systems" by Barbara Chapman, Gabriele Jost, and Ruud van der Pas


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