High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917050936459264 |
|---|---|
| author | Abdulah, Sameh Salvana, Mary Lai O. Sun, Ying Keyes, David E. Genton, Marc G. |
| author_facet | Abdulah, Sameh Salvana, Mary Lai O. Sun, Ying Keyes, David E. Genton, Marc G. |
| contents | 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 Top500 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions Abdulah, Sameh Salvana, Mary Lai O. Sun, Ying Keyes, David E. Genton, Marc G. Distributed, Parallel, and Cluster Computing 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 Top500 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. |
| title | High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.04013 |