A 256 x 256 hyperslab of a contiguous f32 dataset read at an eighth of
h5py's speed: partial_read copied the bounding box out of the file, the
extractor then walked it element by element (a recursive call and two
bounds checks per element) into a second buffer, and read_f32_selection
converted that into a third.
Selections of contiguous data are now copied straight from the file, one
memcpy per run of elements contiguous in the file (gather.rs: a block
along the last dimension, touching blocks as one range, whole rows
merged), with no zero-filled intermediate and no full copy for large
selections. The typed selection readers copy into their Vec<T> directly
when the dataset stores T natively (new data_read::read_selection_native
and sealed NativeElement trait, which the read_as_* fast paths now share;
read_as_u64 gains one) and convert as before otherwise. The general
extractor used by the chunked paths runs on the same run walker, keeping
its old handling of unvalidated selections.
Checked against h5py (contiguous_read_interop.rs) for strided, blocked,
adjacent-block and whole-row hyperslabs, points and empty selections of
every 1-8-byte type in both byte orders, ranks 1-4.
Also keeps the huge-page threshold constant out of no_std builds, where
it was unused.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
A full read of a contiguous dataset is one memcpy from the mapped file,
yet ran at a quarter of h5py's speed on one thread: the fresh output Vec
took a page fault and a kernel page clear for every 4 KiB page written,
16384 per 64 MiB, costing several times the copy (the benchmark spent
6.2 s of 8 s in the kernel, 4.3M minor faults). numpy, so h5py, madvises
MADV_HUGEPAGE on allocations of 4 MiB or more; the typed readers' output,
the raw contiguous read and the chunk assembly buffer now do the same
(Linux only, libc as a Linux-only dependency; no-op otherwise).
New h5py comparison tests cover full and selection reads of contiguous
data for every 1-8-byte integer and float type, both byte orders, ranks
1-4, empty selections, and datasets past the 4 MiB threshold.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
