OpenMP is a tool for parallel programming using shared memory multiprocessing. It allows users to split their program into threads that can run simultaneously on multiple processors. OpenMP uses compiler directives to indicate which parts of a program should be run in parallel. It is simple to use as it does not require extensive code changes, and works across platforms supporting C, C++, and Fortran. An experiment showed a sequential program taking 3347.68 ms to run versus 983.576 ms when parallelized using OpenMP, demonstrating its ability to speed up programs by distributing work across multiple threads and processors.

1. OpenMP
Presented by :
Mohammad Radpour
Amirali Sharifian
Farbod Nosrat Nezami

2. Introduction
• What is parallel processing ?
It is ability of processing more than one job simultaneously.
• Why going parallel ?
• Great deal of data to be processed
• Time needed to calculate an engineering equation
• Need jobs to be done faster
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3. Technologies
• What technologies used for parallel processing ?
• Network based parallel processing
• Utilizing CPU free time and power
• Fact is most of CPU time and power is wasting
• Tearing down jobs and run them on resources
• Local parallelism on multicore/multiprocessor systems
• Utilize the concept of multithreading
• Utilize the concept of share memory
• Can be run on either GPU or CPU
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4. Tools and Technics
• What tools used for parallel processing ?
• Network based parallel processing
• Gird based parallel computing
• Cloud based parallelism and Cloud computing
• Local parallelism on multicore/multiprocessor systems
• NVidia® CUDA™
• MPI
• Posix Threads
• OpenMP
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5. What is OpenMP
• OpenMP
• In simple word runs a user program in parallel.
• It utilize to main concepts for parallelism
• Multithreading
• Shared Memory
• It takes user application, tear it down into group of threads and
runs them on a shared memory foundation
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6. Why using OpenMP
• It is simple to use it
• Most of the times there is no need to change program code
• It utilize compiler directives to demonstrate parallel region
• It is cross platform
• It supports by Fortran and C / C++
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7. Programming Model
• Shared Memory
• Parallelism by threading
• Fork-Join model
• Explicit Parallelism
• Nested Parallelism
• Dynamic Threads
• Input / Output
• Memory model
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8. Shared Memory
• What is shared memory ?
• Why using shared memory?
• Shared Memory in OpenMP
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9. Shared Memory (Cont.)
• Following system can be used for shared memory access
• a single core chip (older PC’s, sequential execution)
• a multicore chip (such as your laptop?)
• multiple single core chips in a NUMA system
• multiple multicore chips in a NUMA system (VT SGI system)
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10. UMA Vs. NUMA
• Unified Memory Access ( UMA )
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11. UMA Vs. NUMA (Cont.)
• Non Unified Memory Access ( NUMA )
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12. Multi Threading
• What is Multi Threading
• What is Intel Hyper-Threading
• Why using Multi Threading
• Multi Threading in OpenMP
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13. Fork – Join Model
• What is Fork
• What is Join
• How Multi Threading works in OpenMP
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14. Fork – Join Model (Cont.)
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F
J
Master
Thread
Thread

15. OpenMP Elements
• Compiler Directives
• Runtime Libraries
• Environmental Variables
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16. How to use OpenMP
• OpenMP implemented for C/C++ and Fortran
• In C/C++ we use compiler directives
• We only need to specify the parallel region
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17. How to use OpenMP
• In non Microsoft compiler:
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18. How to use OpenMP (Cont.)
• In Visual Studio :
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19. Real Experiment
void main()
{
omp_set_num_threads(6);
LARGE_INTEGER frequency; // ticks per secon
LARGE_INTEGER t1, t2; // ticks
double elapsedTime;
// get ticks per second
QueryPerformanceFrequency(&frequency);
// start timer
QueryPerformanceCounter(&t1);
#pragma omp parallel for
for(int i =0 ; i < 999999 ; i++)
for(int i =0 ; i < 1000 ; i++);
// stop timer
QueryPerformanceCounter(&t2);
elapsedTime= (t2.QuadPart - t1.QuadPart) * 1000.0 / frequency.QuadPart;
cout << elapsedTime << " ms.n";
}
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20. Experiment Result - Sequential
• It took 3347.68 milliseconds to run
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21. Experiment Result - Parallel
• It took 983.576 milliseconds to run
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22. The End
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