将PyTorch图学习框架移植到英特尔Gaudi-v2加速器，降低非CUDA硬件使用门槛。
PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs
- 开发核心工具集，恢复Gaudi-v2上的散列、稀疏索引等关键操作
- 提供16个教程与11个真实案例，含故障诊断与修复方案
- 开源完整经验包，助力研究者在非英伟达硬件上开展图学习实验
几何学习已成为建模非欧几里得数据（尤其是图结构数据）的强大范式，广泛应用于社交网络、分子结构、知识图谱和推荐系统。尽管英伟达CUDA GPU主导硬件市场，但英特尔的Gaudi Habana Processing Units（HPUs）等新兴加速器在性能与能效方面具备竞争力。然而，使用此类非CUDA硬件需大量工程适配与软件改造。本文分享将基于PyTorch的几何学习框架迁移至Gaudi-v2 HPUs的经验，提出一套核心工具集以恢复散列、稀疏索引、k近邻等关键操作，并整合16个引导教程与11个真实案例，包含故障诊断与详细应对策略。所有经验已归档于公开GitHub仓库。本工作降低了研究者在非CUDA硬件上实验图学习算法的门槛，为后续优化与跨平台可移植性奠定基础。
原文摘要 · Abstract (English)
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.
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