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High-performance parallel save/load for large NumPy arrays using shared memory and multiprocessing

2 starsPython

High-performance parallel save/load for large NumPy

by NoteDance·May 26, 2026·2 points·0 comments

AI Analysis

●●SolidBig BrainShip It

Zero-copy shared memory beats pickle for large arrays, but Zarr already does parallel chunks.

Strengths
  • Standard library multiprocessing and shared memory avoid heavy external dependencies.
  • Zero-copy data sharing between processes reduces serialization overhead significantly.
  • Preserves dictionary keys and array metadata through dedicated sidecar files.
Weaknesses
  • No benchmarks prove speedup over standard np.save or established Zarr.
  • Fragmented file output lacks atomicity and chunking of established formats.
Target Audience

Python developers working with large NumPy arrays

Similar To

Zarr · HDF5 · Joblib

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