PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909673114828800 |
|---|---|
| author | Bu, Fanchen Shin, Kijung |
| author_facet | Bu, Fanchen Shin, Kijung |
| contents | Geometric learning has emerged as a powerful paradigm for modeling non-Euclidean data, especially graph-structured ones, with applications spanning social networks, molecular structures, knowledge graphs, and recommender systems. While Nvidia's CUDA-enabled graphics processing units (GPUs) largely dominate the hardware landscape, emerging accelerators such as Intel's Gaudi Habana Processing Units (HPUs) offer competitive performance and energy efficiency. However, the usage of such non-CUDA processing units requires significant engineering effort and novel software adaptations. In this work, we present our experiences porting PyTorch-based geometric learning frameworks to Gaudi-v2 HPUs. We introduce a collection of core utilities that restore essential operations (e.g., scatter, sparse indexing, k-nearest neighbors) on Gaudi-v2 HPUs, and we consolidate sixteen guided tutorials and eleven real-world examples with diagnostic analyses of encountered failures and detailed workarounds. We collect all our experiences into a publicly accessible GitHub repository. Our contributions lower the barrier for researchers to experiment with geometric-learning algorithms and models on non-CUDA hardware, providing a foundation for further optimization and cross-platform portability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs Bu, Fanchen Shin, Kijung Machine Learning Software Engineering Geometric learning has emerged as a powerful paradigm for modeling non-Euclidean data, especially graph-structured ones, with applications spanning social networks, molecular structures, knowledge graphs, and recommender systems. While Nvidia's CUDA-enabled graphics processing units (GPUs) largely dominate the hardware landscape, emerging accelerators such as Intel's Gaudi Habana Processing Units (HPUs) offer competitive performance and energy efficiency. However, the usage of such non-CUDA processing units requires significant engineering effort and novel software adaptations. In this work, we present our experiences porting PyTorch-based geometric learning frameworks to Gaudi-v2 HPUs. We introduce a collection of core utilities that restore essential operations (e.g., scatter, sparse indexing, k-nearest neighbors) on Gaudi-v2 HPUs, and we consolidate sixteen guided tutorials and eleven real-world examples with diagnostic analyses of encountered failures and detailed workarounds. We collect all our experiences into a publicly accessible GitHub repository. Our contributions lower the barrier for researchers to experiment with geometric-learning algorithms and models on non-CUDA hardware, providing a foundation for further optimization and cross-platform portability. |
| title | PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2507.01031 |