Modern scientific workflows increasingly span diverse computing architectures, yet executing a single computational model across disparate systems often forces researchers to maintain fragmented, site-specific pipelines. In this paper, we address this challenge within the domain of computational neuroscience by presenting a unified, cloud-based workflow orchestrated via EBRAINS JupyterLab. This workflow enables users to transparently execute spiking neural networks on both von-Neumann supercomputers and neuromorphic hardware. Using a single federated identity, the system dispatches jobs to HPC sites (JUSUF, Galileo100) via PyUNICORE and to the SpiNNaker-1 neuromorphic system via the Neuromorphic Computing Platform Interface. To guarantee cross-site reproducibility and mitigate software version drift, we utilize a zero-installation execution mode that dynamically pulls PMIx-aware Apptainer containers to HPC compute nodes. Furthermore, we demonstrate genuine model-level portability using the NESTML domain-specific language, allowing custom neuron models to be written once and automatically compiled for either the NEST (C++) or sPyNNaker backends. Validated with a balanced random network case study, this work illustrates a practical, end-to-end path for hardware-agnostic workflows while highlighting the critical role of containerization and domain-specific languages in achieving true cross-platform reproducibility.

翻译：现代科学工作流日益跨越多样的计算架构，但跨异构系统执行统一计算模型时，研究人员往往被迫维护碎片化且特定于平台的流水线。本文针对计算神经科学领域中的这一挑战，提出一种基于EBRAINS JupyterLab编排的统一云工作流。该工作流使用户能够透明地在冯·诺伊曼超级计算机和神经形态硬件上执行脉冲神经网络。通过单一联邦身份，系统借助PyUNICORE将任务调度至HPC站点（JUSUF、Galileo100），并通过神经形态计算平台接口调度至SpiNNaker-1神经形态系统。为确保跨站点可重复性并缓解软件版本漂移，我们采用零安装执行模式，动态拉取PMIx感知的Apptainer容器至HPC计算节点。此外，我们利用NESTML领域特定语言展示真正的模型级可移植性，允许用户一次性编写定制神经元模型，并自动编译为NEST（C++）或sPyNNaker后端。通过平衡随机网络案例验证，本研究为硬件无关工作流展示了实用的端到端路径，同时凸显容器化与领域特定语言在实现真正跨平台可重复性中的关键作用。