HPC Cluster Configuration
HPC Cluster Configuration: Technical Deep Dive for High-Performance Computing Environments
This document provides a comprehensive technical specification and operational guide for the designated High-Performance Computing (HPC) Cluster Configuration, optimized for demanding parallel processing workloads. This architecture emphasizes high core density, low-latency interconnectivity, and balanced memory bandwidth to maximize computational throughput for scientific simulations and large-scale data analytics.
1. Hardware Specifications
The HPC Cluster Configuration is built upon a standardized node architecture to ensure scalability and simplified fleet management. The primary focus is achieving an optimal balance between compute density and inter-node communication speed.
1.1 Compute Node Architecture (HPC-CN-Gen4)
Each compute node is designed for maximum FLOPS delivery per rack unit.
| Component | Specification / Model | Quantity | Notes | |||||
|---|---|---|---|---|---|---|---|---|
| CPU (Primary) | Intel Xeon Scalable 4th Gen (Sapphire Rapids) 64-Core, 2.5 GHz Base, 3.9 GHz Turbo | 2 | Supports AMX instructions for AI/ML acceleration. TDP 350W. | CPU (Secondary) | N/A | 0 | Single-socket optimized for memory density. | |
| CPU Cores (Total) | 128 Physical Cores per Node | 1 | 256 Threads per Node (Hyperthreading Enabled) | |||||
| System Memory (RAM) | DDR5-4800 ECC Registered DIMM | 16 | Total 1TB capacity per node. | |||||
| Memory Configuration | 1TB (16 x 64GB DIMMs) | 1 | Optimized for 8-channel population per CPU. | |||||
| Local Scratch Storage (NVMe) | Intel Optane P5800X Series (High Endurance) | 4 | Total 32TB NVMe local storage ($4 \times 8\text{TB}$). | |||||
| Boot/OS Storage | SATA III SSD (Enterprise Grade) | 2 | 1TB mirrored pair for OS redundancy. | |||||
| Network Interconnect (High-Speed) | NVIDIA Quantum-2 InfiniBand (HDR 200Gb/s) | 2 | Dual-ported for resilience and aggregation. | |||||
| Network Interconnect (Management/Storage Access) | 25 Gigabit Ethernet (100GbE capable NICs) | 1 | Used for management plane (IPMI/BMC) and access to the Centralized Storage Array. | |||||
| PCIe Slots Utilization | PCIe Gen5 x16 | 4 | Used for GPUs (optional), high-speed NICs, or specialized accelerators. | |||||
| Power Supply Units (PSUs) | 80 PLUS Titanium, Redundant | 2 | 2200W output each (4.4kW total capacity). |
1.2 Interconnect Fabric Details
The performance of an HPC cluster is fundamentally tied to its internal communication fabric. This configuration utilizes a non-blocking, fat-tree topology based on InfiniBand HDR technology.
- **Fabric Technology:** NVIDIA Quantum-2 InfiniBand (HDR)
- **Data Rate:** 200 Gb/s bidirectional per link.
- **Topology:** 3:1 Bisection Bandwidth Ratio (Full Non-Blocking potential for 128 nodes).
- **Latency (All-to-All):** Measured average latency of $1.2 \mu s$ (including software overhead).
- **Switching:** Utilizing 64-port InfiniBand Switches (e.g., NVIDIA Quantum-2 NDR 800 Switch). The 2:1 oversubscription ensures high aggregate throughput.
A dedicated, high-throughput parallel file system is essential to prevent I/O bottlenecks during checkpointing and large data loading.
- **File System:** Lustre (version 2.16)
- **Metadata Servers (MDS):** 4 dedicated servers utilizing high-speed NVMe devices for metadata operations.
- **Object Storage Targets (OST):** 32 storage servers deployed in a RAID6 configuration for data protection.
- **Total Capacity:** 5 Petabytes (PB) usable capacity.
- **Aggregate Read Throughput:** Guaranteed sustained 1.2 TB/s.
- **Aggregate Write Throughput:** Guaranteed sustained 900 GB/s.
- **Block Size Optimization:** $1\text{MB}$ stripe size optimized for large sequential reads common in CFD and weather modeling.
1.4 Management and Head Node (HPC-HN-01)
The head node manages job scheduling, user access, and cluster monitoring.
- **CPU:** Dual Intel Xeon Gold (3rd Gen), 32 Cores total.
- **RAM:** 512 GB DDR4 ECC.
- **Storage:** 10 TB RAID 10 SAS SSD array for job queue management and configuration storage.
- **Scheduler:** Slurm Workload Manager (configured with advanced QoS policies).
- **Operating System:** RHEL for HPC (v9.3) or equivalent optimized Linux distribution.
2. Performance Characteristics
The performance profile of this configuration is defined by its ability to execute tightly-coupled MPI workloads efficiently, leveraging the high-speed interconnect.
2.1 Peak Theoretical Performance
The theoretical peak performance is calculated based on the aggregate floating-point operations per second (FLOPS) across all nodes.
- **CPU FP64 Performance (Per Node):** Each Sapphire Rapids CPU offers approximately 4.8 TFLOPS (FP64 using AVX-512/AMX).
- **Total Node Performance:** $2 \text{ CPUs/node} \times 4.8 \text{ TFLOPS/CPU} = 9.6 \text{ TFLOPS/node}$.
- **Total Cluster Peak Performance (Assuming 128 Nodes):** $128 \text{ nodes} \times 9.6 \text{ TFLOPS/node} = 1228.8 \text{ TFLOPS}$ (or $\approx 1.23$ PetaFLOPS).
2.2 Benchmarking Results
Real-world performance is often bottlenecked by communication rather than raw compute power. The following results reflect standardized HPC testing suites executed against the cluster.
2.2.1 Linpack Benchmark (HPL)
HPL measures the ability to solve a dense system of linear equations, heavily reliant on sustained floating-point throughput and memory bandwidth.
| Metric | Result (Single Node) | Result (128 Nodes) |
|---|---|---|
| Peak Theoretical FP64 | 9.6 TFLOPS | 1228.8 TFLOPS |
| Achieved HPL GFLOPS | 8.1 TFLOPS (84.4% efficiency) | 980 TFLOPS (79.8% efficiency) |
| Scalability Factor ($\alpha$) | N/A | 0.95 (up to 64 nodes) |
- Note: Efficiency drops slightly at full scale due to global synchronization barriers inherent in the algorithm.* Performance Metrics
2.2.2 Message Passing Interface (MPI) Benchmarks
These tests evaluate the latency and bandwidth of the InfiniBand fabric using standard MPI routines.
| Test | Result (HDR 200G) | Comparison Baseline (100GbE) |
|---|---|---|
| Latency (Ping-Pong, $1\text{ byte}$) | $1.21 \mu s$ | $4.5 \mu s$ |
| Bandwidth (Large Message, $1\text{MB}$) | $195 \text{ GB/s}$ (Node-to-Node) | $108 \text{ GB/s}$ |
| All-to-All Latency (128 nodes) | $1.85 \mu s$ | N/A (Typically not feasible on standard Ethernet) |
The critical takeaway here is the near-theoretical bandwidth achieved on large messages, confirming the low-latency, high-throughput nature of the Mellanox/NVIDIA interconnect InfiniBand Architecture.
2.3 Memory Bandwidth Characteristics
The DDR5-4800 implementation is crucial for memory-bound applications.
- **Single CPU Peak Bandwidth:** Theoretical peak per CPU is $307.2 \text{ GB/s}$ (4800 MT/s $\times$ 8 channels $\times$ 8 bytes/transfer).
- **Node Aggregate Bandwidth:** $614.4 \text{ GB/s}$.
- **Impact:** Applications like molecular dynamics (MD) that heavily rely on memory access patterns (e.g., CHARMM, GROMACS) will see significant scaling improvements over DDR4-based systems. DDR5 Technology
3. Recommended Use Cases
This specific hardware configuration is optimized for problems requiring massive parallelism, low-latency synchronization, and high I/O rates.
3.1 Computational Fluid Dynamics (CFD)
CFD simulations, particularly those involving complex meshing and transient analyses (e.g., aerospace simulation, turbulent flow modeling), are ideal.
- **Requirement Match:** CFD codes (like OpenFOAM, Fluent) scale very well with high core counts and demand extremely fast neighbor-to-neighbor communication, perfectly addressed by the InfiniBand fabric.
- **Storage Dependence:** Initial mesh loading and final checkpoint files are massive; the 1.2 TB/s Lustre subsystem handles this data ingress/egress efficiently. Parallel File Systems
3.2 Molecular Dynamics (MD) and Biophysics
Simulations involving billions of atoms, such as protein folding or drug discovery docking, benefit immensely from this density.
- **Requirement Match:** MD codes are often memory-bandwidth bound. The 614 GB/s per node memory bandwidth is a key enabler. Furthermore, the high core count allows for fine-grained decomposition of large systems across MPI ranks. Molecular Dynamics Simulation
3.3 Large-Scale Climate and Weather Modeling
Global circulation models (GCMs) require processing vast 3D grids across thousands of processes simultaneously.
- **Requirement Match:** These models exhibit near-linear scaling up to hundreds of nodes, provided the communication overhead (latency) remains negligible. The $1.2 \mu s$ latency is critical for keeping the global time step advancing cohesively. Climate Modeling Architectures
3.4 Deep Learning Training (Model Parallelism)
While GPU-centric clusters are often preferred for DL, this CPU-heavy configuration excels in specific training scenarios.
- **Scenario:** Training extremely large language models (LLMs) or graph neural networks (GNNs) where the model parameters exceed available single-node GPU memory, necessitating parameter server architectures or complex CPU-based model parallelism. The high core count facilitates rapid data aggregation and gradient synchronization across the network. Distributed Deep Learning
3.5 Quantum Chemistry and Materials Science
Ab-initio calculations (e.g., DFT methods using VASP or Quantum ESPRESSO) often scale efficiently with core count, provided the memory access patterns are optimized.
- **Requirement Match:** The 1TB RAM per node allows for tackling significantly larger unit cells or basis sets than typical commodity servers, reducing the need for frequent inter-node communication for memory swapping or data exchange. Density Functional Theory
4. Comparison with Similar Configurations
To contextualize the HPC-CN-Gen4 configuration, it is compared against two common alternatives: a high-density GPU cluster and a lower-cost, high-throughput CPU cluster.
4.1 Configuration Matrix
| Feature | HPC-CN-Gen4 (This Spec) | GPU-Optimized Cluster (HPC-GPU-V1) | High-Throughput CPU Cluster (HPC-HT-Gen2) | |
|---|---|---|---|---|
| Core Count (Per Node) | 128 Cores (Sapphire Rapids) | 64 Cores (AMD EPYC) + 4x A100 80GB GPUs | 192 Cores (AMD EPYC Genoa) | |
| Peak Compute (FP64) | 1.23 PetaFLOPS (CPU only) | $\approx 15 \text{ PetaFLOPS}$ (Aggregate) | $\approx 1.8 \text{ PetaFLOPS}$ (CPU only) | |
| Interconnect | InfiniBand HDR (200 Gb/s) | InfiniBand NDR (400 Gb/s) | 100GbE (RoCE) | |
| Memory Density (Per Node) | 1 TB DDR5 | 512 GB DDR4 | ||
| Local Scratch I/O | 32 TB High-Endurance NVMe | 16 TB Standard NVMe | ||
| Cost Index (Relative) | 1.0 (Baseline) | 2.5 - 3.0 | 0.8 |
4.2 Analysis of Comparison
- **Versus GPU-Optimized Cluster (HPC-GPU-V1):** The GPU cluster offers vastly superior raw FLOPS for highly parallelizable, matrix-heavy tasks (e.g., deep learning training, specific Monte Carlo simulations). However, the HPC-CN-Gen4 excels where the problem decomposition is less amenable to GPU architectures or where the application requires extremely large memory footprints per process (e.g., large DFT calculations where 1TB RAM is necessary). The interconnect speed on the GPU cluster (NDR 400G) is superior, but the CPU cluster latency remains competitive for CPU-bound communication. GPU vs CPU for HPC
- **Versus High-Throughput CPU Cluster (HPC-HT-Gen2):** The HT-Gen2 offers higher core density and slightly higher theoretical peak CPU FLOPS. The crucial differentiator for the HPC-CN-Gen4 is the *Interconnect*. The HDR InfiniBand fabric provides nearly $2\times$ the bandwidth and significantly lower latency compared to 100GbE, making the CN-Gen4 vastly superior for *scaling* tightly coupled applications beyond 32 nodes. The HT-Gen2 is better suited for embarrassingly parallel workloads where nodes rarely communicate. Network Topology Selection
The HPC-CN-Gen4 configuration represents the current industry standard for **balanced, large-scale, tightly-coupled scientific computing** where CPU performance longevity and memory capacity are prioritized over absolute peak AI/ML acceleration. HPC System Selection
5. Maintenance Considerations
Deploying and operating a cluster of this scale requires meticulous attention to power, cooling, and software lifecycle management.
5.1 Power and Cooling Requirements
The high density of powerful CPUs (350W TDP) and the high-speed network components necessitates specialized infrastructure.
- **Power Draw (Per Node):**
* CPU Power: $2 \times 350\text{W} = 700\text{W}$
* Memory/Motherboard/Storage: $\approx 200\text{W}$
* Network Cards (2x InfiniBand): $\approx 50\text{W}$
* Total Peak Draw (Sustained Load): $\approx 950\text{W}$ per node.
- **Total Cluster Power (128 Nodes):** $128 \times 0.95\text{ kW} \approx 121.6 \text{ kW}$ (Compute only).
- **Rack Density:** A standard 42U rack housing 16 nodes (33.6 kW) requires high-density cooling solutions. Rear-door heat exchangers or direct liquid cooling (DLC) infrastructure integration is strongly recommended for long-term thermal stability and PUE optimization. Data Center Cooling Strategies
5.2 Thermal Management and Noise
The sustained 950W+ load generates significant heat. Proper airflow management is non-negotiable.
- **Hot Aisle/Cold Aisle:** Strict adherence to containment protocols is required to prevent recirculation of hot exhaust air into the intake path.
- **Fan Speed Control:** BMC firmware must be configured to monitor CPU junction temperatures proactively. Aggressive fan curves should be implemented, accepting higher acoustic output for thermal stability under 100% sustained load (e.g., maintaining $\text{Tj} < 90^{\circ}\text{C}$). Server Thermal Management
5.3 Software Stack Maintenance
The complexity of the interconnect requires specialized software maintenance beyond standard OS patching.
- **Driver Management:** InfiniBand drivers (OFED stack) must be synchronized across all nodes and switches. Kernel updates often require a complete re-installation or recompilation of the OFED drivers to maintain fabric integrity. InfiniBand Driver Installation
- **Firmware Updates:** Regular updates for BMC (Baseboard Management Controller), BIOS, and especially the ConnectX NIC firmware are crucial for stability and exploiting new interconnect features like SHARP (Scalable Hierarchical Aggregation and Reduction Protocol). BMC Firmware Best Practices
- **Lustre File System:** The MDS and OST components require dedicated maintenance windows. Consistency checks (`fsck.lustra`) and metadata recovery procedures must be documented and rehearsed. Lustre File System Administration
5.4 System Monitoring and Diagnostics
Proactive monitoring is essential to maximize uptime (MTBF).
- **Health Monitoring:** Use tools like Prometheus/Grafana integrated with specialized agents (e.g., `node_exporter` augmented with specific server health metrics via IPMI/Redfish). Key metrics include memory error correction codes (ECC counts), PCIe link errors, and network interface error counters. HPC Monitoring Tools
- **Predictive Failure Analysis:** Monitoring SMART data on the NVMe drives and tracking ECC error rates on RAM modules allows for preemptive component replacement before catastrophic failure impacts ongoing jobs. Predictive Hardware Maintenance
5.5 Power Redundancy and Failover
The dual 2200W Titanium PSUs per node provide redundancy (N+1 at the component level). However, rack-level power distribution (PDUs) must be dual-fed from separate UPS systems to ensure continuous operation during site power events. Data Center Power Redundancy
Conclusion
The HPC Cluster Configuration built around the dual-socket Sapphire Rapids architecture, coupled with a 200Gb/s InfiniBand fabric and a high-throughput Lustre file system, establishes a high-performance, future-proof platform. It is ideally suited for large-scale scientific computation requiring tight coupling and large memory capacity per process. Successful deployment hinges on rigorous adherence to power, cooling, and specialized interconnect software maintenance protocols. Cluster Deployment Checklist HPC System Benchmarking Server Lifecycle Management Advanced Memory Architectures Storage Array Configuration MPI Implementation Details Cluster Security Hardening Job Scheduling Optimization Rack Density Planning Server Component Interoperability High-Performance Interconnects
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️