bench: concurrent-read harness against h5py threads and processes
concurrent_read reads one shared File from 1-16 threads: every dataset in full (distinct datasets per thread) and random hyperslabs of one dataset, over a deflate and a contiguous file it generates (or reuses while manifest.json matches). It reports decoded MB/s and scaling efficiency, warm or --cold (posix_fadvise) page cache, sizes the decode pool with --decode-threads, and writes JSON. scripts/concurrent_read_h5py.py runs the same workload on the same files with h5py threads or spawned processes (same splitmix64 data and slab stream, checked at spot elements), and compare_concurrent_read.py prints one table and refuses runs with different workloads. A smoke test runs all three end to end on tiny files (h5py half honours CLAWHDF5_PYTHON / CLAWHDF5_REQUIRE_INTEROP). BENCHMARKS.md gets a "Concurrent reads" section with the commands, marked not yet measured. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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