Managing Single and Multithreaded Implementations

General Usage Performance
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As a library maintainer what are some good idioms for managing implementations of single threaded and multi threaded algorithm implementations?

Yesterday I implemented an algorithm three ways, with channels, locks, and thread-local arrays. For my algorithm (isosurface extraction), locks give the best overall performance on multiple threads. However, in the single thread case I notice 50% -15% slower performance. My first thought was to implement two loops for the single thread and multi threaded cases. If I do this, I am left with two implementations.

What is the expectation for package maintainers? Is it reasonable to expect users will run julia with >1 thread in the future, and keep the code terse? Is it worth maintaining two implementations to keep good single thread performance?

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I’ve been maintaining two versions, and using a key word argument threaded::Bool = Threads.nthreads() > 1 to determine if it’s threaded.

It’ll default to using multiple threads if they have several, but I still want to give them (or myself) the option to use a single thread, in case they want to call that function in parallel from multiple threads, rather that run the function in parallel itself.

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