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Optimizing OpenMP Scheduling: Balancing Static and Dynamic Approaches

This guide discusses the principles of high-performance computing and shared-memory programming using OpenMP. It analyzes the effectiveness of static and dynamic scheduling in parallel processing, focusing on the impact of chunk sizes and iteration workloads. While static scheduling may seem optimal, it often suffers from load imbalance when iterations vary in complexity, such as in rendering or search operations. Dynamic scheduling can introduce overhead, but guided scheduling attempts to balance both approaches by adjusting chunk sizes throughout execution. Recommendations for selecting chunk sizes based on workload distribution are provided.

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Optimizing OpenMP Scheduling: Balancing Static and Dynamic Approaches

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  1. Principles of High Performance Computing (ICS 632) Shared-Memory Programming with OpenMP Threads

  2. OpenMP Scheduling chunk size = 6 Iterations = 18 Thread 1 Thread 2 Thread 3

  3. OpenMP Scheduling chunk size = 6 Iterations= 18 Thread 1 Thread 2 Thread 3 STATIC time Work in different iterations is identical.

  4. So, isn’t static optimal? • The problem is that in may cases the iterations are not identical • Some iterations take longer to compute than other • Example #1 • Each iteration is a rendering of a movie’s frame • More complex frames require more work • Example #2 • Each iteration is a “google search” • Some searches are easy • Some searches are hard • In such cases, load unbalance arises • which we know is bad

  5. OpenMP Scheduling chunk size = 6 Iterations=18 Thread 1 Thread 2 Thread 3 time STATIC Work in different iterations is NOT identical.

  6. OpenMP Scheduling chunk size = 2 Iterations=18 Thread 1 Thread 2 Thread 3 time DYNAMIC Work in different iterations is NOT identical.

  7. So isn’t dynamic optimal? • Dynamic scheduling with small chunks causes more overhead than static scheduling • In the static case, one can compute what each thread does at the beginning of the loop and then let the threads proceed unhindered • In the dynamic case, there needs to be some type of communication: “I am done with my 2 iterations, which ones do I do next?” • Can be implemented in a variety of ways internally • Using dynamic scheduling with a large chunk size leads to lower overhead, but defeats the purpose • with fewer chunks, load-balancing is harder • Guided Scheduling: best of both worlds • start with large chunks, ends with small ones

  8. Guided Scheduling • The chunk size is reduced in an exponentially decreasing manner with each dispatched piece of the iteration space. • chunk specifies the smallest piece (except possibly the last).

  9. OpenMP Scheduling chunk size = 2 Iterations= 18 Thread 1 Thread 2 Thread 3 time Guided 3 chunks of size 4 2 chunks of size 2

  10. What should I do? • Pick a reasonable chunk size • Use static if computation is evenly spread among iterations • Otherwise probably use guided

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