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Advanced Topic 35 of 51 Concurrency & Parallelism

OpenMP

Directive-based shared-memory parallel programming.

How it works

OpenMP is an API for shared-memory parallel programming in C, C++ and Fortran based on compiler directives, pragmas, with a runtime library. A directive such as #pragma omp parallel for splits loop iterations among threads, with clauses for private and shared variables, reduction, scheduling policies such as static, dynamic and guided, synchronization with critical, atomic and barrier, task constructs, SIMD vectorization and offloading to accelerators. Code remains valid serial code if the pragmas are ignored.

Where it is used

Scientific and engineering simulations, numerical libraries and data-processing loops on multicore machines are commonly parallelized incrementally with OpenMP.

Common pitfall

Data races from shared variables that should be private are the main bug, as are loop-carried dependencies the compiler cannot see. False sharing, load imbalance and too many threads reduce performance. Nested parallelism and thread affinity need configuration. Always check correctness against the serial version, use reduction clauses, set thread counts and affinity deliberately and profile.

Where it fits in the course

OpenMP is topic 35 of 51 in Concurrency & Parallelism, rated Advanced. It follows Parallel Prefix Sum. The next topic is MPI.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Concurrency & Parallelism quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is OpenMP?
Directive-based shared-memory parallel programming. It belongs to Concurrency & Parallelism and is an advanced-level topic.
Where do people use OpenMP?
Scientific and engineering simulations, numerical libraries and data-processing loops on multicore machines are commonly parallelized incrementally with OpenMP.
What is the most common mistake with OpenMP?
Data races from shared variables that should be private are the main bug, as are loop-carried dependencies the compiler cannot see.
What should I learn before this topic?
Start with Parallel Prefix Sum, the topic just before it in Concurrency & Parallelism.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Concurrency & Parallelism quiz.
Is OpenMP hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn OpenMP?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to OpenMP?
Parallel Prefix Sum, MPI, Parallel Reduction are closely connected. The full list is in the related topics section of this page.
What should I study after OpenMP?
Continue with MPI, the next topic in Concurrency & Parallelism.

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