Boosting Program Performance with OpenMP

OpenMP allows multiple threads to run simultaneously on CPU cores, providing almost linear time speedup in most applications. Learn about OpenMP directives, strategies to convert CUDA to OpenMP, and considerations for thread management to enhance program efficiency.

• OpenMP
• Parallel Programming
• Performance Optimization
• Multithreading

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Uploaded on Apr 12, 2025 | 0 Views

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Presentation Transcript

1. OpenMP Usman Roshan

2. Why OpenMP? Allows multiple threads to run simultaneously on CPU cores Almost linear time speedup in most applications (with n cores your program is n times faster) Available in C and Cython (Python with embedded C)

3. OpenMP directives We use the command #pragma to specify openmp directives Straighforward to convert a CUDA program into OpenMP We will look at the OpenMP commands to get thread ID and the main directive to run a function in parallel Data access by thread ID is similar to what we did for CUDA but coalescent memory access is note required

4. Strategy to convert Chi2 CUDA to Chi2 OpenMP Remove references to cuda functions, blocks, and threads Replace input number of threads to number of cores Get thread id with omp_get_thread_num() Insert #pragma omp parallel num_threads(nprocs) just before the function to parallelize In the function use the thread ID to access portions of the data

5. Chi2 OpenMP threads Do we want each thread to access a single column or a set of columns? Remember that if you specify too many threads and you have a few cores then your program will be very slow due to frequent job swaps