Palace: A Library for Interactive GPU-Accelerated Large Tensor Processing and Visualization
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909816680611840 |
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| author | Drees, Dominik Risse, Benjamin |
| author_facet | Drees, Dominik Risse, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Palace: A Library for Interactive GPU-Accelerated Large Tensor Processing and Visualization Drees, Dominik Risse, Benjamin Graphics 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. |
| title | Palace: A Library for Interactive GPU-Accelerated Large Tensor Processing and Visualization |
| topic | Graphics |
| url | https://arxiv.org/abs/2509.26213 |