A performance change should improve the measured workload without sacrificing correctness or wasting capacity. Which measurements and bottlenecks should be considered first?

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Tasadduq BurneyownerOwner · Founder

26525 reputation · 23 Apr 2024, 00:11 UTC

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Profile representative array sizes and inspect memory layout and temporary allocations. Vectorization often helps, but a huge broadcasted intermediate can exhaust memory. Use appropriate dtypes, in-place operations only when ownership is clear, and chunking when the complete intermediate is unnecessary.

How can I improve NumPy performance without guessing?

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