https://arxiv.org/abs/2304.06835 Skip to main content Cornell University Served from the cloud We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2304.06835 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2304.06835 (cs) [Submitted on 13 Apr 2023 (v1), last revised 13 Nov 2023 (this version, v3)] Title:Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms Authors:Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma, Tim Besard, Prakitr Srisuma, Tim Gymnich, Adam R. Gerlach, Alan Edelman, George Barbastathis, Richard D. Braatz, Christopher Rackauckas Download a PDF of the paper titled Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms, by Utkarsh Utkarsh and 10 other authors Download PDF Abstract:We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia's DifferentialEquations.jl) and enables GPU acceleration without requiring code changes by the user. Our approach achieves state-of-the-art performance compared to hand-optimized CUDA-C++ kernels while performing 20--100$\times$ faster than the vectorizing map (vmap) approach implemented in JAX and PyTorch. Performance evaluation on NVIDIA, AMD, Intel, and Apple GPUs demonstrates performance portability and vendor-agnosticism. We show composability with MPI to enable distributed multi-GPU workflows. The implemented solvers are fully featured -- supporting event handling, automatic differentiation, and incorporation of datasets via the GPU's texture memory -- allowing scientists to take advantage of GPU acceleration on all major current architectures without changing their model code and without loss of performance. We distribute the software as an open-source library this https URL Comments: 14 figures Distributed, Parallel, and Cluster Computing Subjects: (cs.DC); Mathematical Software (cs.MS); Numerical Analysis (math.NA) Cite as: arXiv:2304.06835 [cs.DC] (or arXiv:2304.06835v3 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2304.06835 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Computer Methods in Applied Mechanics and Engineering, Volume 419, 2024 https://doi.org/10.1016/j.cma.2023.116591 Related DOI: Focus to learn more DOI(s) linking to related resources Submission history From: Utkarsh Utkarsh [view email] [v1] Thu, 13 Apr 2023 21:57:51 UTC (8,296 KB) [v2] Fri, 21 Apr 2023 04:03:33 UTC (8,301 KB) [v3] Mon, 13 Nov 2023 05:11:50 UTC (2,693 KB) Full-text links: Access Paper: Download a PDF of the paper titled Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms, by Utkarsh Utkarsh and 10 other authors * Download PDF * PostScript * Other Formats [by-nc-nd-4] Current browse context: cs.DC < prev | next > new | recent | 2304 Change to browse by: cs cs.MS cs.NA math math.NA References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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