Overview
SeaWulf is Stony Brook University's flagship high-performance computing (HPC) cluster, representing a cutting-edge computational resource designed to accelerate research across diverse scientific disciplines. The system's name cleverly blends "Seawolf" (Stony Brook's mascot) with "Beowulf," paying homage to one of the earliest HPC cluster architectures.
Built with state-of-the-art components from leading technology partners including AMD, Dell, HPE, IBM, Intel, and Nvidia, SeaWulf serves the campus research community as well as industrial partners. The cluster is housed in Stony Brook's Computing Center and represents a significant investment in research infrastructure.
Technical Specifications
Core Architecture
| Component | Specification | Details |
|---|---|---|
| Total Nodes | 400+ | Heterogeneous mix of compute, GPU, and high-memory nodes |
| Total Cores | ~23,000 | Distributed across multiple node types |
| Peak Performance | 1.86 petaFLOPS | Combined CPU and GPU computational power |
| Interconnect | InfiniBand | 5-50 GB/s data transfer speeds |
| Storage | GPFS Array | Hybrid spinning disks and SSDs |
Node Types
- 40-core nodes: Standard compute nodes for general-purpose workloads
- 96-core nodes: High-core-count nodes for parallel processing
- GPU nodes: Equipped with 4x K80 24GB GPUs each for accelerated computing
- High-bandwidth memory (HBM) nodes: Intel Xeon CPU Max Series with HBM
- High-memory nodes: Up to 1TB DDR5 memory for memory-intensive applications
Recent Upgrades (2023)
The recent $1.6 million upgrade introduced:
- HPE ProLiant DL360 Gen11 servers
- Intel Xeon CPU Max Series processors with high-bandwidth memory
- Enhanced memory bandwidth resulting in 2-4x faster application performance
- Four nodes with 1TB DDR5 memory configured in cache mode
Key Capabilities
High-Speed Networking
SeaWulf's nodes are interconnected via high-speed InfiniBand networks by Nvidia, enabling exceptional data transfer rates ranging from 5 to 50 gigabytes per second. This high-performance networking infrastructure ensures efficient communication between nodes for parallel computing workloads.
Heterogeneous Computing
The cluster supports both CPU and GPU computing paradigms, making it suitable for a wide range of computational approaches including traditional HPC workloads, machine learning, and AI applications.
Memory Architecture
With the integration of Intel Xeon CPU Max Series processors featuring high-bandwidth memory, SeaWulf offers significant advantages for memory-intensive applications. The HBM technology dramatically improves data movement between memory and processors.
Research Applications
SeaWulf supports a diverse array of research disciplines and computational methodologies:
- Computational Physics: Large-scale simulations and modeling
- Climate Science: Weather prediction and climate modeling
- Bioinformatics: Genomics, proteomics, and molecular dynamics
- Materials Science: Quantum mechanical calculations and materials discovery
- Machine Learning & AI: Deep learning model training and inference
- Engineering: Computational fluid dynamics and structural analysis
- Data Science: Big data analytics and statistical computing
Access and Partnerships
SeaWulf serves both the Stony Brook University research community and external industrial partners, fostering collaboration between academia and industry. The system is available to researchers across all disciplines who require high-performance computing resources for their work.
Strategic Importance
As one of the most powerful academic computing resources in the region, SeaWulf positions Stony Brook University at the forefront of computational research. The cluster's cutting-edge architecture, including its pioneering use of high-bandwidth memory technology, makes it an invaluable tool for advancing scientific discovery and maintaining the university's competitive edge in research computing.
The system's heterogeneous design and substantial computational power enable researchers to tackle increasingly complex problems that would be impossible to solve with conventional computing resources, supporting breakthrough research across multiple scientific domains.