High‐Performance Statistical Computing ( <scp>HPSC</scp> ): Challenges, Opportunities, and Future Directions

ABSTRACT We recognize the emergence of a statistical computing community focused on working with large computing platforms and producing software and applications that exemplify high‐performance statistical computing (HPSC). The statistical computing (SC) community develops software that is widely used across disciplines. However, it remains largely absent from the high‐performance computing (HPC) landscape, particularly on platforms such as those featured on the www.top500.org or Green500 lists. Many disciplines already participate in HPC, mostly centered around simulation science, although data‐focused efforts under the artificial intelligence (AI) label are gaining popularity. Bridging this gap requires both community adaptation and technical innovation to align statistical methods with modern HPC technologies. We can accelerate progress in fast and scalable statistical applications by building strong connections between the SC and HPC communities. We present a brief history of SC, a vision for how its strengths can contribute to statistical science in the HPC environment (such as HPSC), the challenges that remain, and the opportunities currently available, culminating in a possible roadmap toward a thriving HPSC community. This article is categorized under: Software for Computational Statistics &gt; High Performance Software Software for Computational Statistics &gt; Software/Statistical Software Algorithms and Computational Methods &gt; Methods for High Performance Computing

Authors

Institutions

Publication Details

Journal
Wiley Interdisciplinary Reviews Computational Statistics
Published
2025-12-01
DOI
https://doi.org/10.1002/wics.70052
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High‐Performance Statistical Computing ( HPSC ): Challenges, Opportunities, and Future Directions

David Elliot Keyes, Marc G. Genton, Mary Lai O. Salvaña, Sameh Abdulah et al.
Wiley Interdisciplinary Reviews Computational Statistics
Gaussian Processes and Bayesian Inference
article

High‐Performance Statistical Computing ( HPSC ): Challenges, Opportunities, and Future Directions

David Elliot Keyes, Marc G. Genton, Mary Lai O. Salvaña, Sameh Abdulah, Ying Sun
article en

Abstract

ABSTRACT We recognize the emergence of a statistical computing community focused on working with large computing platforms and producing software and applications that exemplify high‐performance statistical computing (HPSC). The statistical computing (SC) community develops software that is widely used across disciplines. However, it remains largely absent from the high‐performance computing (HPC) landscape, particularly on platforms such as those featured on the www.top500.org or Green500 lists. Many disciplines already participate in HPC, mostly centered around simulation science, although data‐focused efforts under the artificial intelligence (AI) label are gaining popularity. Bridging this gap requires both community adaptation and technical innovation to align statistical methods with modern HPC technologies. We can accelerate progress in fast and scalable statistical applications by building strong connections between the SC and HPC communities. We present a brief history of SC, a vision for how its strengths can contribute to statistical science in the HPC environment (such as HPSC), the challenges that remain, and the opportunities currently available, culminating in a possible roadmap toward a thriving HPSC community. This article is categorized under: Software for Computational Statistics &gt; High Performance Software Software for Computational Statistics &gt; Software/Statistical Software Algorithms and Computational Methods &gt; Methods for High Performance Computing

Wiley Interdisciplinary Reviews Computational StatisticsVol. 17(4)
University of Connecticut (US), King Abdullah University of Science and Technology (SA)
Openalex Percentile: Top 79%
Gaussian Processes and Bayesian Inference
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

High‐Performance Statistical Computing ( <scp>HPSC</scp> ): Challenges, Opportunities, and Future Directions — David Elliot Keyes, Marc G. Genton, et al. · Wiley Interdisciplinary Reviews Computational Statistics (2025) | TGRS Research Map | TGRS