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A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainability

Lois Curfman McInnes, Michael Heroux, David E. Bernholdt, Anshu Dubey, Elsa Gonsiorowski, Rinku Gupta, Osni Marques, J. David Moulton, Hai Ah Nam, Boyana Norris, Elaine M. Raybourn, Jim Willenbring, Ann Almgren, Ross Bartlett, Kita Cranfill, Stephen Fickas, Don Frederick, William Godoy, Patricia Grubel, Rebecca Hartman-Baker, Axel Huebl, Rose Lynch, Addi Malviya Thakur, Reed Milewicz, Mark C. Miller, Miranda Mundt, Erik Palmer, Suzanne Parete-Koon, Megan Phinney, Katherine Riley, David M. Rogers, Ben Sims, Deborah Stevens, Gregory R. Watson

TL;DR

The paper addresses the urgent need to improve developer productivity, software quality, and reproducibility in data-enabled and exascale science, where software sustains experiments and discoveries. It describes IDEAS as a multiphase DOE initiative that incubates, curates, and disseminates knowledge and methodologies to build robust, sustainable scientific software ecosystems across architectures up to exascale ($10^{18}$ FLOPS). The authors trace IDEAS’ lineage (IDEAS-Classic, IDEAS-ECP, IDEAS-Watersheds) and summarize core outputs, including the xSDK/E4S ecosystems, PSIP, BSSw.io, and wide-ranging outreach (HPC-BP, tutorials, Fellowships) that have improved builds, CI, interoperability, and collaboration. They argue that sustaining culture change and long-term funding is essential to make software productivity and sustainability a core professional expectation, enabling open, reproducible science on future hybrid hardware and AI-enabled workflows.

Abstract

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.

A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainability

TL;DR

The paper addresses the urgent need to improve developer productivity, software quality, and reproducibility in data-enabled and exascale science, where software sustains experiments and discoveries. It describes IDEAS as a multiphase DOE initiative that incubates, curates, and disseminates knowledge and methodologies to build robust, sustainable scientific software ecosystems across architectures up to exascale ( FLOPS). The authors trace IDEAS’ lineage (IDEAS-Classic, IDEAS-ECP, IDEAS-Watersheds) and summarize core outputs, including the xSDK/E4S ecosystems, PSIP, BSSw.io, and wide-ranging outreach (HPC-BP, tutorials, Fellowships) that have improved builds, CI, interoperability, and collaboration. They argue that sustaining culture change and long-term funding is essential to make software productivity and sustainability a core professional expectation, enabling open, reproducible science on future hybrid hardware and AI-enabled workflows.

Abstract

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.
Paper Structure (8 sections, 1 figure)

This paper contains 8 sections, 1 figure.

Table of Contents

  1. Introduction
  2. A Brief History of IDEAS
  3. Fostering software communities
  4. Incubating and curating methodologies and resources
  5. Disseminating knowledge
  6. Achievements and opportunities
  7. Acknowledgments
  8. Author bios

Figures (1)

  • Figure 1: The IDEAS team engages with DOE application and software teams---and the broader HPC community---to reduce technical risks and build a firmer foundation for next-generation computational science. Our strategy of being a persistent catalyst means we must always incubate, curate, disseminate, and repeat---ensuring we transfer our discoveries and knowledge to the broader community to create sustainable and evolving impact.