cuNumeric.jl

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HDF5 ​

h5read and h5write transfer datasets between HDF5 files and runtime-managed NDArrays without gathering them into Julia Arrays.

Example ​

julia
using cuNumeric

field = cuNumeric.fill(3.5f0, 128, 64)
cuNumeric.h5write("checkpoint.h5", "field", field)
cuNumeric.Legate.runtime_sync()

restored = cuNumeric.h5read("checkpoint.h5", "field"; layout=:row)
@assert size(restored) == (128, 64)
@assert eltype(restored) == Float32
@assert cuNumeric.compare(fill(3.5f0, 128, 64), restored, 0, 0)

h5write submits work to Legate and can return before the file write has completed. Synchronize before accessing the file outside the runtime, moving or deleting it, or exiting immediately after the write.

Dataset layout ​

julia
row_major = cuNumeric.h5read("python.h5", "field")
column_major = cuNumeric.h5read("julia.h5", "field"; layout=:col)

layout=:row is the default for NumPy/h5py, cuPyNumeric, and cuNumeric.h5write. Use layout=:col for multidimensional datasets written by HDF5.jl. One-dimensional datasets are unaffected. Other keywords are forwarded to Legate.h5read.

Tests cover Float32, Float64, Int32, and Int64 arrays with one to three dimensions. Other types depend on the Legate HDF5 backend.

API reference ​

h5read ​

cuNumeric.h5read Function
julia
h5read(path::String, dataset::String; layout::Symbol=:row) -> NDArray

Read a dataset from an HDF5 file into an NDArray.

Arguments

  • path: Path to the HDF5 file.

  • dataset: Name of the dataset to read.

Keywords

  • layout: On-disk memory order, either :row (default) or :col.
source

h5write ​

cuNumeric.h5write Function
julia
h5write(path::String, dataset::String, arr::NDArray)

Write an NDArray directly to an HDF5 dataset without a host copy or dimension flip.

Arguments

  • path: Path to the HDF5 file.

  • dataset: Name of the dataset to write.

  • arr: The array to write.

source