The Big Picture
SeaWulf's architecture is designed around a simple principle: match computational problems with the most suitable hardware. Rather than having identical machines, we provide specialized node types optimized for different kinds of work.
This means you can choose hardware that's specifically designed for your computational patterns - whether that's high-parallelism number crunching, memory-intensive data analysis, or GPU-accelerated machine learning.
Hardware Generations and Access
SeaWulf's hardware spans multiple technology generations, accessible through different login nodes:
Legacy Platform (login1/login2)
The original SeaWulf hardware, still valuable for many applications:
- Haswell 28-core nodes: Mature, stable platform with AVX2 support
- GPU acceleration: K80, P100, and V100 GPUs for older CUDA applications
- Best for: Legacy software, budget-conscious computing, development work
Modern Platform (milan1/milan2)
The expanded SeaWulf infrastructure with cutting-edge hardware:
- Multiple CPU architectures: Intel Skylake, AMD Milan, Intel Sapphire Rapids
- Memory innovations: Standard DDR5, high-bandwidth HBM, and massive 1TB configurations
- Latest GPUs: NVIDIA A100 with 80GB memory each
- Specialized features: Shared access modes, ultra-high memory bandwidth
Understanding Performance Characteristics
CPU Architecture Differences
Different CPU generations have distinct performance profiles:
| Architecture | Strengths | Ideal Applications |
|---|---|---|
| Intel Haswell (28-core) | Mature, stable, wide software compatibility | Legacy codes, development, general computing |
| Intel Skylake (40-core) | Balanced performance, modern instruction sets | Most scientific computing workloads |
| AMD Milan (96-core) | Maximum parallelism, excellent price/performance | Highly parallel applications, parameter sweeps |
| Intel Sapphire Rapids (96-core) | Advanced instruction sets, HBM memory | AI/ML inference, memory-intensive applications |
Memory Architecture Impact
SeaWulf's memory configurations are designed for different access patterns:
- Standard DDR5: Good for most applications with balanced compute and memory needs
- High-Bandwidth Memory (HBM): Revolutionary for applications limited by memory bandwidth
- Large memory configurations: Essential for in-memory processing of massive datasets
Storage Architecture
SeaWulf uses a parallel file system that provides high-performance data access from any compute node. The storage hierarchy is designed around different data usage patterns:
| Storage Tier | Purpose | Performance | Capacity |
|---|---|---|---|
| Home Directories | Personal files, source code | Moderate | Limited, backed up |
| Scratch Storage | Temporary job data | Very high | Large, not backed up |
| Project Storage | Shared research datasets | High | Configurable, persistent |
Network and Interconnect
SeaWulf uses high-speed InfiniBand networking to connect all components:
- Low latency communication: Critical for tightly-coupled parallel applications
- High bandwidth: Supports intensive data movement between nodes
- Reliable messaging: Built-in error detection and automatic retry
- Scalable fabric: Performance doesn't degrade as the system grows
This architecture means that multi-node jobs can communicate efficiently, and data access performance is consistent regardless of which nodes your job runs on.
Shared vs Dedicated Access
SeaWulf pioneered an innovative approach to resource utilization through shared access modes:
Dedicated Access
- Your job gets exclusive access to entire nodes
- Guaranteed resources and performance
- Best for large, resource-intensive applications
- Higher resource cost per computation
Shared Access
- Multiple users can run jobs on the same node
- More efficient utilization of system resources
- Ideal for smaller jobs that don't need full nodes
- Faster queue times, lower resource cost
This dual approach maximizes both system efficiency and user flexibility, allowing small jobs to start quickly while ensuring large computations get the resources they need.
System Scalability
SeaWulf's architecture supports research computing at multiple scales:
- Single-core jobs: Small analyses and serial applications
- Single-node parallel: Multi-threaded applications using up to 96 cores
- Multi-node parallel: Distributed applications across dozens of nodes
- Massive parallel: Large-scale simulations using hundreds of nodes
The scheduling system automatically handles resource allocation and job placement, optimizing performance while maintaining fair access across all users.
Architectural Philosophy
SeaWulf's design reflects several key principles:
- Heterogeneity over homogeneity: Multiple specialized tools rather than one-size-fits-all
- Efficiency through choice: Match computational patterns to appropriate hardware
- Accessibility and fairness: Advanced resources available to all researchers
- Future adaptability: Architecture that can evolve with changing computational needs
This approach means SeaWulf can support everything from traditional HPC simulations to modern AI/ML workflows, often within the same research project.