strided-perm

Cache-efficient tensor permutation / transpose, inspired by HPTT (Springer et al., 2017).

Usage

Copy from any strided-view source view into a mutable destination view with the same shape. The last argument is a [strided_exec::Exec] placement token: the copy splits within that budget and never starts work of its own.

use strided_exec::Exec;
use strided_perm::copy_into;
use strided_view::StridedArray;

let src = StridedArray::<f64>::from_fn_col_major(&[2, 3], |idx| {
    (idx[0] + 10 * idx[1]) as f64
});
let transposed = src.view().permute(&[1, 0]).unwrap();

let mut dst = StridedArray::<f64>::col_major(transposed.dims());
copy_into(&mut dst.view_mut(), &transposed, Exec::serial()).unwrap();

Use copy_into_col_major when the destination is known to be column-major and you want to make that layout contract explicit.

copy_into_uninit takes a StridedViewMut<MaybeUninit<T>> destination over fresh storage. It runs the same engine, selected on T rather than on MaybeUninit<T>, so the native f32/f64 kernels still apply; every element the views describe is written exactly once, through raw pointers only.

Threading

Exec::serial() runs the copy on the calling thread. A token minted from a strided_exec::Pool with Exec::new(&pool, budget) splits the permutation across budget tasks of that pool. The crate uses exactly the budget it is given: callers that want small copies to stay on one thread pass a serial token or narrow the token with Exec::limit. Without the parallel feature every token runs inline.

[dependencies]
strided-perm = { version = "0.7", features = ["parallel"] }

use strided_exec::{Exec, Pool};
use strided_perm::copy_into;
use strided_view::StridedArray;

let pool = Pool::builder().threads(2).build().unwrap();
let src = StridedArray::<f64>::col_major(&[2, 3]);
let mut dst = StridedArray::<f64>::col_major(&[2, 3]);
copy_into(&mut dst.view_mut(), &src.view(), Exec::new(&pool, 2).unwrap()).unwrap();

Benchmarks live in strided-rs-benchmark-suite.

Acknowledgments and License

The transpose engine in src/hptt/ reimplements the algorithm of HPTT by Paul Springer, Tong Su, and Paolo Bientinesi, following the structure of the original C++ implementation (P. Springer, T. Su, P. Bientinesi, "HPTT: A High-Performance Tensor Transposition C++ Library", ARRAY 2017). HPTT is licensed under BSD-3-Clause (Copyright 2018 Paul Springer); see the packaged THIRD-PARTY-LICENSES file for the full text.

This crate is therefore licensed as (MIT OR Apache-2.0) AND BSD-3-Clause.