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Introduction
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Shared-memory parallel programs break up large problems into a number of smaller ones and execute them simultaneously
OpenMP programs are limited to a single physical machine
OpenMP libraries are built into all commonly used C, C++, or Fortran compilers
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A Parallel Hello World Program
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OpenMP pragmas direct the compiler what to parallelize and how to parallelize it.
By using the environment variable OMP_NUM_THREADS, it is possible to control how many threads are used.
The order in which parallel elements are executed cannot be guaranteed.
A compiler that isn’t aware of OpenMP pragmas will compile a single-threaded program.
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OpenMP Work Sharing Constructs
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Data parallelism refers to the execution of the same task simultaneously on multiple computing cores.
Functional parallelism refers to the concurrent execution of different tasks on multiple computing cores.
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Parallel Operations with Arrays
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With the parallel for pragma, a loop is executed in parallel
When a variable is accessed by different threads, incorrect results can be produced.
Each thread receives a copy of the variable created by the private clause
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Race Conditions with OpenMP
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OpenMP Tasks
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OpenMP can manage general parallel tasks
tasks now allow the parallelization of applications exhibiting irregular parallelism such as recursive algorithms, and pointer-based data structures.
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Calculating Many-Body Potentials
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Programming GPUs with OpenMP
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Introduction to GPU Programming with OpenACC
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