Multithreaded calculation

Benchmarks for the threaded algorithm are shown here, for unmasked z, a problem size n of 1000 and a total_chunk_count of 40 for up to 6 threads.

For the simple dataset contour calculations are faster with more threads but it does not scale particularly well. The speedup with 6 threads is 2.4-2.5 for lines() and 2.5-2.6 for filled(). This problem dataset is perhaps not computationally expensive enough to justify the use of multiple threads.

For the random dataset contour calculations scale much better with increasing number of threads as long as one of the ChunkCombined... line or fill types is being used. Using 6 threads the speedup is 4.4 for lines() and 5.1-5.2 for filled().

The LineType and FillType options that do not scale well are those that return individual NumPy arrays for each line or polygon rather than combined arrays for each chunk. This is because the allocation of a new NumPy array can only be performed by one thread at a time, so the larger the number of arrays that are generated, the greater the likelihood that other threads are left waiting before they can allocate arrays.

Note

Whether it is worth using threaded rather than serial for a particular problem depends on the complexity of the dataset and what the calculated contours are to be used for. If they are only needed for rendering using the Matplotlib Agg renderer, then for complicated problems the rendering time usually far exceeds the calculation time so a reduction in calculation time may not be of much real-world benefit.