intellectually dishonest, as python's arguably most popular use case is scientific apps, which rely heavily on numpy, which is a frontend for LAPACK and BLAS, which are both implemented in fortran.https://twitter.com/philvenables/status/1218874905317408768 …
I recall the guy who found a new solution for the sum of 3 cubes used python. He had 512 university cores working on it for weeks. He expected it to take months. I gotta imagine another language coulda done it faster.
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Sum of 3 cubes is interesting, because memory speed and cache size help almost not at all. It’s all cpu cycles.
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I suppose my wonder is: if scientific communities were still using performance-minded languages, how much faster could science be progressing?
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