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Franck Cappello

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Case report Open access Aug 2026

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

L. McInnes, Dorian Arnold, Prasanna Balaprakash et al. · 0 citations
Book Open access Jul 2026

TZ: Achieving High-Ratio Scientific Data Compression on GPUs with Global Data Decomposition

Evaluation on production-scale scientific datasets demonstrates that TZ achieves approximately 10 × higher compression ratios than state-of-the-art GPU compressors under the same error bound, while maintaining competitive, high-throughput performance.

Zhuoxun Yang, Ruoyu Li, A. Subrahmanya et al. · 0 citations
Book Jul 2026

Bridging Information Theory and Practice for Scientific Lossy Compression

This paper develops a novel framework that characterizes compressibility limits for scientific datasets under realistic tiling constraints, and is the first framework to rigorously characterize lossy compressibility limits for scientific datasets and compressor, moving beyond classical asymptotic 1D source models.

Sujata Sinha, Sheng Di, Vishwas Rao et al. · 0 citations
Book Open access Jul 2026

OPAL: On-demand Progressive Accelerated Scientific Lossy Compression

The OPAL universal framework is the OPAL universal framework, an adaptive architecture that endows generic lossy compressors with on-demand retrieval capabilities, enabling flexible and progressive access across multiple spatial regions, resolutions, and precisions.

Longtao Zhang, Ruoyu Li, Zhuoxun Yang et al. · 2 citations

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