Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. DOE Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities-building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software, its sustainability, and the trustworthiness of the results that it produces. Members of the IDEAS project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This paper discusses how these synergistic activities are advancing scientific discovery-mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

翻译：计算与数据驱动的科学与工程正以各种计算规模，彻底推动着科学与社会各领域的进步。例如，美国能源部百亿亿次计算项目的团队通过利用前所未有的百亿亿次计算能力，攻克建模、模拟与分析的新前沿——构建支持下一代应用并应对计算机架构颠覆性变革的先进软件生态系统。然而，科学软件开发者的生产力、软件的可持续性及其产出结果的可靠性日益引发担忧。IDEAS项目成员通过培育软件社区、孵化和梳理方法论与资源、传播知识以提升开发者生产力与软件可持续性，成为应对这些挑战的催化剂。本文探讨这些协同活动如何推进科学发现——通过为从笔记本电脑到集群、百亿亿次乃至更大规模计算中可复现且可持续的科学奠定更坚实基础，从而降低技术风险。