High-Performance Statistical Computing Community Hub
Welcome to the premier international platform at the intersection of computer science, statistics, and large-scale data analytics. The High-Performance Statistical Computing (HPSC) community brings together researchers and practitioners advancing the frontiers of statistics on modern computational platforms.
As the central hub for computational statistics, HPSC highlights cutting-edge research, benchmarks, industry developments, and community achievements. We connect traditionally separate disciplines, foster collaboration, and define standards shaping the future of large-scale data science.
Our coverage spans parallel and distributed statistical algorithms, GPU-accelerated methods, scalable machine learning, and cloud-native frameworks. We also track emerging frontiers—from exascale analytics to next-generation computing infrastructures driving discovery.
Whether you are a computational scientist applying statistical methods, a statistician leveraging high-performance systems, or a data scientist working at unprecedented scales, HPSC is your gateway to the innovations transforming statistics and scientific exploration.
Message from Leadership
High-Performance Statistical Computing: Building a Statistics Community within Supercomputing
Leading voices in the field—professors, research scientists, and innovators—share why uniting the HPC and Statistics communities is vital for the next generation of scientific discovery.
Upcoming Events
Register for the event here
Latest HPSC News
Inside one of the world's most advanced supercomputers
Powering Our Research: High-Performance Computing
Community Activities
Seminars & Workshops
Monthly expert presentations and hands-on sessions
Research Highlights
Showcase of breakthrough computational statistics achievements
Performance Benchmarks
Industry-standard metrics and computational comparisons
Research Areas
HPSC advances computational statistics across diverse domains, bridging traditional boundaries between computing and Statistics:
Parallel Statistical Algorithms
Scalable implementations of statistical methods for multicore, GPU, and distributed computing environments.
Exascale Analytics
Next-generation statistical computing frameworks designed for exascale supercomputing architectures.
Scalable Machine Learning
Machine learning methods optimized for large-scale, high-dimensional data using parallel and distributed computing frameworks.
Cloud-Native Statistics
Containerized statistical workflows, serverless computing, and elastic scaling for data science applications.
Real-Time Statistical Inference
High-throughput statistical processing for streaming data, edge computing, and latency-critical applications.
Federated Statistical Learning
Privacy-preserving distributed statistics across heterogeneous computing environments and data sources.
Community Standards
HPSC establishes best practices and benchmarks for computational statistics, similar to how the supercomputing community maintains performance standards:
Scalability Metrics
Performance benchmarks for statistical algorithms
Efficiency Standards
Resource utilization and optimization guidelines
Interoperability
Cross-platform compatibility and integration protocols
Our standards ensure reproducible, scalable, and efficient computational statistics across diverse computing environments and research applications.
Contributors
- Sameh Abdulah (Senior Research Scientist, KAUST, Thuwal, KSA) - sameh.abdulah@kaust.edu.sa
- Mary Lai O. Salvaña (Assistant Professor, University of Connecticut, Storrs, CT, USA) - marylai.salvana@uconn.edu
- Yan Song (Assistant Professor, University of British Columbia, Vancouver, BC, Canada) - yan.song@kaust.edu.sa
- Ying Sun (Professor, KAUST, Thuwal, KSA) - ying.sun@kaust.edu.sa
- Marc G. Genton (Professor, KAUST, Thuwal, KSA) - marc.genton@kaust.edu.sa
- David E. Keyes (Professor, KAUST, Thuwal, KSA) - david.keyes@kaust.edu.sa