Papers › Massively Parallel Computation of Similarity Matrices from Piecewise Constant Invariants
Massively Parallel Computation of Similarity Matrices from Piecewise Constant Invariants
Björn H. Wehlin
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We present a computational framework for piecewise constant functions (PCFs) and use this for several types of computations that are useful in statistics, e.g., averages, similarity matrices, and so on. We give a linear-time, allocation-free algorithm for working with pairs of PCFs at machine precision. From this, we derive algorithms for computing reductions of several PCFs. The algorithms have been implemented in a highly scalable fashion for parallel execution on CPU and, in some cases, (multi-)GPU, and are provided in a \proglang{Python} package. In addition, we provide support for multidimensional arrays of PCFs and vectorized operations on these. As a stress test, we have computed a distance matrix from 500,000 PCFs using 8 GPUs.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections