General

Research HPC Cluster Deployment

# Research HPC Cluster Deployment

Client Profile

**Industry:** Research & Academia

**Company Size:** 500+ researchers and staff

**Location:** Cambridge, UK

**Infrastructure:** Hybrid (on-premises HPC + cloud burst)

The Challenge

Computational Bottleneck

CERN-affiliated research institute needed to accelerate their particle physics simulations:

**Simulation queue time** averaged 14 days for compute jobs

**Cluster utilization** was only 45% due to heterogeneous workload requirements

**Storage performance** couldn't keep up with 100+ concurrent simulations

**Budget constraints** prevented building larger on-premises cluster

**Collaboration needs** required sharing data with 8 international partner institutions

Research Requirements

The physics simulations were extraordinarily demanding:

**Grid-based simulations** requiring 10,000+ CPU cores

**GPU acceleration** needed for machine learning analysis

**High-performance storage** with 100GB/s aggregate bandwidth

**Low-latency networking** - InfiniBand for inter-node communication

**Data-intensive workflows** - 50TB of simulation data per month

**Long-running jobs** - Some simulations run for 72+ hours continuously

Infrastructure Limitations

The existing infrastructure was severely outdated:

**10-year-old cluster** with heterogeneous hardware

**No GPU support** - ML analysis done on separate, slower system

**Shared storage** - NFS with 2GB/s total bandwidth

**Manual job scheduling** - Researchers submitted jobs via email

**No resource isolation** - One job could starve all others

**Limited monitoring** - No visibility into job performance

Assessment Findings

The technical assessment revealed critical issues:

**Hardware age** - 60% of nodes failing warranty, high failure rate

**Energy inefficiency** - 2x power consumption per FLOP vs. modern hardware

**Software fragmentation** - 15 different compiler versions, incompatible libraries

**Data management** - No lifecycle policy, 40TB of orphaned data

**Security** - No proper access controls, compliance concerns

The Solution

Phase 1: HPC Infrastructure Design (Months 1-2)

**Cluster Architecture**

Designed a modern HPC platform:

**Compute nodes** - 50x dual-socket AMD EPYC (128 cores each)

**GPU nodes** - 20x with 4x NVIDIA H100 (80GB each)

**Storage nodes** - 10x with NVMe caching layer

**Management nodes** - 3-node high-availability cluster

**Network fabric** - InfiniBand HDR (200Gb/s) for compute, 100GbE for storage

**Storage Architecture**

Built a multi-tier storage system:

**Parallel filesystem** - Lustre with 20TB/s aggregate bandwidth

**NVMe cache** - 500TB high-speed tier for active data

**HDD tier** - 2PB capacity tier for warm data

**Object storage** - MinIO for unstructured data and ML datasets

**Backup system** - Automated snapshots with 30-day retention

**Job Scheduling**

Implemented advanced scheduling:

**Slurm** - Industry-standard workload manager

**QoS policies** - Priority queues for different research groups

**Preemptible jobs** - Low-priority jobs can be interrupted

**GPU sharing** - MIG (Multi-Instance GPU) for better utilization

**Accounting** - Detailed usage tracking for billing

Phase 2: Software Stack (Months 3-4)

**Scientific Software Environment**

Built comprehensive software stack:

**Module system** - Environment modules for easy software management

**Container support** - Singularity/Apptainer for portable environments

**ML frameworks** - PyTorch, TensorFlow, JAX with GPU support

**Scientific libraries** - MPI, OpenMP, BLAS, LAPACK optimized

**JupyterHub** - Web-based interactive computing environment

**Data Management**

Implemented data lifecycle management:

**Automated tiering** - Hot data on NVMe, cold data on HDD

**Data deduplication** - 40% storage savings on duplicate datasets

**Lifecycle policies** - Automatic archival after 90 days

**Data sharing** - Secure collaboration space for partner institutions

**Backup & recovery** - Automated backups with point-in-time recovery

**Monitoring & Observability**

Built comprehensive monitoring:

**Grafana dashboards** - Real-time cluster utilization visualization

**Prometheus** - Time-series metrics collection

**ELK stack** - Centralized logging for troubleshooting

**Job analytics** - Performance analysis and optimization insights

**Alerting** - Automated notifications for failures and issues

Phase 3: Optimization & Training (Months 5-8)

**Performance Optimization**

Optimized for research workloads:

**Job placement** - Data-aware scheduling to minimize network traffic

**GPU optimization** - Mixed precision training for 2x speedup

**Storage optimization** - I/O pattern analysis and tuning

**Network tuning** - InfiniBand parameter optimization

**Energy optimization** - Dynamic frequency scaling for power savings

**User Training & Support**

Ensured successful adoption:

**Workshops** - Weekly training sessions for researchers

**Documentation** - Comprehensive user guides and best practices

**Help desk** - Dedicated HPC support team

**Office hours** - One-on-one optimization consulting

**Community** - User forum for knowledge sharing

**Cloud Burst Integration**

Enabled hybrid cloud capabilities:

**Cloud connection** - Direct link to AWS for burst capacity

**Hybrid scheduling** - Slurm can submit jobs to cloud resources

**Data sync** - Automated data replication to cloud storage

**Cost optimization** - Intelligent burst decision based on queue time

**Security** - VPC peering with encryption in transit

The Results

Quantifiable Metrics

|--------|--------|-------|-------------|

| Cluster utilization | 45% | 78% | **73% increase** |

| Storage bandwidth | 2GB/s | 18GB/s | **800% increase** |

| GPU utilization | 12% | 65% | **442% increase** |

| Job completion rate | 87% | 98% | **13% improvement** |

Research Impact

**Scientific Output**

Papers published increased 85% year-over-year

Complex simulations that took 3 months now complete in 3 weeks

New research directions enabled by GPU-accelerated ML

International collaborations expanded with 3 new partner institutions

**Efficiency Gains**

Researchers spend 60% more time on analysis, 40% less on waiting

Job turnaround time enables rapid iteration and experimentation

GPU access allows adoption of modern ML techniques

Cloud bursting eliminates queue times during peak demand

**Cost Optimization**

Energy costs reduced 55% despite 3x compute capacity

Cloud bursting saved $450K vs. building additional on-premises capacity

Storage optimization reduced costs by 40% through lifecycle management

Hardware refresh cycle extended through better utilization

Client Testimonial

> "GMX TEC built a world-class HPC facility that has transformed our research capabilities. We're publishing more papers, faster, with more complex simulations. The GPU cluster alone has enabled entirely new research directions in ML-driven physics. This is exactly the kind of infrastructure that attracts top talent."

> **Prof. Elizabeth Harper**

> Director of Computational Research, Cambridge Institute

Lessons Learned

What Worked Well

1. **User-centered design** - Researchers involved in architecture decisions

2. **Phased rollout** - Pilot with 10 nodes, then scale based on feedback

3. **Training investment** - Comprehensive training ensured high adoption

4. **Cloud integration** - Hybrid approach provided flexibility

Challenges Encountered

1. **Legacy job porting** - Some codes required significant refactoring for modern hardware

2. **Data migration** - Moving 40TB of data required careful planning

3. **User expectations** - Researchers expected cloud-like simplicity for HPC

4. **Security compliance** - Export controls on GPU technology required legal review

Key Takeaways

1. **Start with workloads** - Design for actual research, not hypothetical needs

2. **Monitoring is critical** - You can't optimize what you can't measure

3. **Training matters** - Even expert researchers need HPC training

4. **Hybrid is powerful** - On-premises for control, cloud for flexibility

Next Steps

Ongoing Relationship

The research institute continues the partnership with:

**Platform optimization** and performance tuning

**Hardware refresh** planning and execution

**Software stack** updates and new tool integration

**Grant support** for HPC-related research proposals

Future Initiatives

Planned expansions include:

**Quantum computing** interface for hybrid classical-quantum workflows (Q2 2024)

**AI research platform** dedicated to ML model training (Q3 2024)

**Edge computing** integration for data collection from experiments (Q4 2024)

**Multi-institution federation** for resource sharing across universities (Q1 2025)

*This case study represents a typical engagement for GMX TEC's research and academic clients. Results may vary based on specific circumstances and requirements.*