seniorNumPy
How does NumPy handle performance degradation in non-contiguous memory?
Updated May 17, 2026
Short answer
Non-contiguous arrays reduce cache efficiency and force stride-based memory access.
Deep explanation
When arrays are non-contiguous, CPU cache lines are not used efficiently. Each access may require jumping across memory locations using stride offsets, increasing latency and reducing SIMD optimization effectiveness.
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