面向科学门户的可复现跨境高性能计算
Reproducible Cross-border High Performance Computing for Scientific Portals
AI总结：
针对科学工作流跨边界访问远程HPC资源以实现可复现性的挑战，提出基于容器打包与设置自动化的解决方案，使科学门户能跨边界访问远程计算与数据资源，增强eScience的可复现性。
摘要：
为了复现eScience，需要解决几个挑战：科学工作流需要自动化；涉及的软件版本需要以明确的方式提供；输入数据需要易于访问；通常涉及高性能计算（HPC）集群，并且为了实现位对位的可复现性，甚至可能需要在特定集群上执行代码，以避免由不同HPC平台引起的差异（除非这是科学家的本地集群，否则需要跨（行政）边界访问）。理想情况下，为了让没有经验的用户也能（重新）生成结果，所有这些都应该是用户友好的。虽然一些易于使用的基于Web的科学门户已经支持访问HPC资源，但这通常仅指本地的计算和数据资源。以生物多样性和气候研究领域的两个特定社区门户为例，我们提出了一种从科学门户跨边界访问远程HPC（和云）计算与数据资源的解决方案，涉及严格的基于容器的软件版本打包和设置自动化，从而增强了可复现性。
英文摘要：
To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance Computing (HPC) clusters are often involved and to achieve bit-to-bit reproducibility, it might be even necessary to execute the code on a particular cluster to avoid differences caused by different HPC platforms (and unless this is a scientist's local cluster, it needs to be accessed across (administrative) borders). Preferably, to allow even inexperienced users to (re-)produce results, all should be user-friendly. While some easy-to-use web-based scientific portals support already to access HPC resources, this typically only refers to computing and data resources that are local. By the example of two community-specific portals in the fields of biodiversity and climate research, we present a solution for accessing remote HPC (and cloud) compute and data resources from scientific portals across borders, involving rigorous container-based packaging of the software version and setup automation, thus enhancing reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
