2 months ago
- OpenMP is an API for parallel programming that simplifies multicore code by adding compiler directives.
- Embarrassingly parallel problems, like rendering the Mandelbrot Set, can be easily parallelized with OpenMP.
- A #pragma omp parallel for directive can parallelize a for-loop, with private variables to avoid race conditions.
- Race conditions occur when threads share variables; using private or block-local variables can prevent them.
- The Mandelbrot renderer example uses memory-mapped files for concurrent output without locking.
- OpenMP provides critical sections (e.g., #pragma omp critical) to control thread execution for tasks like progress display.
- Parallelizing per-row instead of per-pixel reduces thread overhead and improves performance.
- Performance gains from multithreading depend on hardware, as seen with hyperthreaded single-core vs. genuine dual-core processors.
- I/O handling in parallel programs requires manual management, as OpenMP does not address it directly.