- Last time: OpenMP generalities
- This time: OpenMP nuts & bolts
- OpenMP: portable and scalable model for shared memory parallel applications
- No need to dive deep and work with POSIX pthreads
- Under the hood, the compiler translates OpenMPfunctions and directives to pthread calls
- Structured block and OpenMP construct are the two sides of the “parallel region” coin
- In a structured block, the only "branches" allowed are exit() function calls. There is an implicit barrier after each structured block where threads wait on each other.
- The nested parallelism behavior can be controlled by using the OpenMP API
- The single directive identifies a section of the code that must be run by a single thread
- The difference between single and master is that in single, the code is executed by whichever thread reaches the region first
- Another diff is that for single, there is an implicit barrier upon completion of the region
- Work sharing is a general term used in OpenMP to describe the distribution of work across threads
- The three main constructs for automatic work division are:
- omp for
- omp sections
- omp task
- A #pragma omp for inside a #pragma omp parallel is equivalent to #pragma omp parallel for
- Most OpenMP implementations use default block partitioning, where each thread is assigned roughly n/thread_count iterations. This may lead to load imbalance if the work per iteration varies
- The schedule clause comes to the rescue!
- Usage example: #pragma omp parallel for schedule(static, 8)
- OpenMP will only parallelize for loops that are in canonical form. Counterintuitive behavior may happen
- The collapse clause supports collapsing the embedded loops into one uber loop
- For example, if the outer loop has 10 iters, the inner loop has 10^7 iters, and we have 32 threads: parallelizing the outer loop is bad (10<32), parallelizing the inner loop is good, but we can do better using collapse
