Who controls parallelism? A disagreement that leads to slower code
If you’re using NumPy, Polars, Zarr, or many other libraries, setting a single environment variable or calling a single API function might make your code run 20%-80% faster. Or, more accurately, it may be that your code is running that much more slowly than it ought to. The problem? A conflict over who controls parallelism: your application, or the libraries it uses. Let’s see an example, and how you can solve it. The mystery of the speedy single-thread implementation We’re […]
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