Tensor datasets (two-, three-, or higher-dimensional) are fundamental to many scientific fields utilizing imaging or simulation technologies. Advances in these methods have led to ever-increasing data sizes and, consequently, interest and development of out-of-core processing and visualization techniques, although mostly as specialized solutions. Here we present Palace, an open-source, cross-platform, general-purpose library for interactive and accelerated out-of-core tensor processing and visualization. Through a high-performance asynchronous concurrent architecture and a simple compute-graph interface, Palace enables the interactive development of out-of-core pipelines on workstation hardware. We demonstrate on benchmarks that Palace outperforms or matches state-of-the-art systems for volume rendering and hierarchical random-walker segmentation and demonstrate applicability in use cases involving tensors from 2D images up to 4D time series datasets.
@article{arxiv.2509.26213,
title = {Palace: A Library for Interactive GPU-Accelerated Large Tensor Processing and Visualization},
author = {Dominik Drees and Benjamin Risse},
journal= {arXiv preprint arXiv:2509.26213},
year = {2025}
}