HPC Clusters

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Technical Deep Dive: High-Performance Computing (HPC) Cluster Configuration

This document details the architecture, performance metrics, operational considerations, and comparative analysis of a modern, high-density High-Performance Computing (HPC) Cluster configuration designed for large-scale scientific simulation and data-intensive workloads.

1. Hardware Specifications

The HPC Cluster detailed here is architected for maximum computational density, low-latency interconnectivity, and balanced memory bandwidth, adhering to modern high-throughput computing (HTC) and tightly-coupled parallel processing requirements. The configuration assumes a modular, rack-scale deployment utilizing densely packed compute nodes.

1.1 Compute Node Architecture (The Core Element)

Each compute node ($N_c$) is optimized for floating-point operations per second (FLOPS) and high memory throughput.

Compute Node ($N_c$) Specifications
Component Specification Detail Rationale
Processor (CPU) 2x Intel Xeon Scalable (5th Gen, e.g., Emerald Rapids, 64 Cores per socket, 3.5 GHz Base, 4.5 GHz Turbo Max, 128 Threads per socket) High core count and superior AVX-512/AMX support for dense linear algebra operations.
CPU TDP 350W per socket (Total 700W per node, excluding accelerators) Balancing power density with sustained performance under heavy load.
System Memory (RAM) 1 TB DDR5 ECC RDIMM @ 5600 MT/s (32x 32GB modules) Large memory footprint crucial for in-memory datasets in CFD and molecular dynamics simulations. High bandwidth is critical.
Memory Bandwidth ~750 GB/s per CPU socket (Total ~1.5 TB/s aggregate) Essential for minimizing CPU stall time during data movement.
Local Storage (Boot/Scratch) 2x 1.92 TB NVMe SSD (PCIe Gen 5.0, 12 GB/s sequential read) Fast I/O for OS, application staging, and temporary checkpointing.
Accelerator Support (Optional Module) 4x NVIDIA H200 Tensor Core GPUs (141 GB HBM3e memory each) Required for AI/ML training and highly parallelizable physics simulations (e.g., Lattice QCD).
Network Interconnect (Control Plane) 1x 10 GbE Base-T (Management) Standardized management and monitoring access.
Network Interconnect (Compute Fabric) 2x 400 Gb/s InfiniBand (IB) NDR per node (Point-to-Point) Ultra-low latency communication required for tightly coupled MPI workloads.

1.2 Interconnect Fabric

The performance of an HPC cluster is often bottlenecked by the network. This configuration mandates a high-radix, non-blocking topology utilizing InfiniBand (IB) for the primary compute fabric.

  • **Technology:** InfiniBand Network Datacenter Rack (NDR) 400 Gb/s.
  • **Topology:** Fat-Tree (3:1 Oversubscription Ratio for scale-out clusters, 1:1 for smaller, high-priority systems).
  • **Switching Hardware:** Modular, non-blocking switches (e.g., Cisco Nexus 400G series or equivalent Mellanox/NVIDIA Quantum series).
  • **Latency Target:** Sub-500 nanoseconds (HCA to HCA round trip).
  • **Management Network:** Standard 10/25 GbE for IPMI/BMC communication and job scheduling monitoring.

1.3 Shared Storage Subsystem (The Parallel File System)

A high-throughput, scalable parallel file system is mandatory to feed the thousands of CPU/GPU cores simultaneously. We employ a distributed file system architecture.

  • **File System Type:** Lustre or GPFS (IBM Spectrum Scale).
  • **Metadata Servers (MDS):** Minimum of 4 dedicated high-core count servers with high-speed, low-latency NVMe storage arrays for metadata operations.
  • **Object Storage Targets (OSTs):** A large array of high-capacity, high-IOPS storage devices distributed across multiple chassis.
   *   **Capacity Tier:** 3 PB raw capacity utilizing 18 TB SAS SSDs (RAID-6 configuration).
   *   **Performance Tier (Burst Buffer):** 500 TB dedicated NVMe storage pool (PCIe Gen 4/5) acting as a high-speed cache layer between compute nodes and the main storage array.
  • **Aggregate Throughput Goal:** Sustained read/write bandwidth exceeding 1.5 TB/s across the cluster.

1.4 Cluster Management and Head Node

The Head Node (HN) coordinates job scheduling, resource allocation, and access control.

  • **Hardware:** High-reliability server (e.g., 2U rackmount) with ECC memory and redundant power supplies.
  • **Software Stack:**
   *   Operating System: Rocky Linux or RHEL (Kernel tuned for low-latency networking).
   *   Resource Manager/Scheduler: Slurm Workload Manager (configured with Multi-Factor Job Submission).
   *   Cluster Management Tools: xCAT or Ansible for configuration management and provisioning.
   *   Monitoring: Prometheus/Grafana stack monitoring CPU utilization, InfiniBand fabric health, and power draw.
File:HPC Cluster Architecture Diagram.png
Conceptual Diagram of a Fat-Tree HPC Cluster Topology

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2. Performance Characteristics

Evaluating an HPC cluster requires metrics that go beyond simple clock speed or core count. We focus on sustained throughput, latency, and scaling efficiency.

2.1 Computational Benchmarks

The primary measure of performance is sustained floating-point operations per second (FLOPS). We use standardized benchmarks targeting different precision levels.

Benchmark Results (Per Node with 4x H200 GPUs)
Benchmark Suite Metric Single Node Result (CPU Only) Single Node Result (CPU + 4x H200)
LINPACK (HPL) Theoretical Peak (TFLOPS FP64) 4.8 TFLOPS 45.0 TFLOPS
HPL (Observed Sustained) Measured TFLOPS FP64 (A=10000) 3.9 TFLOPS (81% Efficiency) 36.5 TFLOPS (81% Efficiency)
STREAM Benchmark Triad Bandwidth (GB/s) 1450 GB/s (Aggregate) 1450 GB/s (CPU Memory remains the bottleneck if GPUs are idle)
HPCG Benchmark Measured GFLOP/s 5.1 GFLOP/s 55.0 GFLOP/s
  • Note: HPL efficiency is often lower on CPU-only nodes due to memory bandwidth saturation before compute saturation. GPU-accelerated nodes show superior scaling efficiency in HPL.*

2.2 Interconnect Performance Metrics

The efficiency of inter-node communication is quantified by latency and bandwidth under MPI (Message Passing Interface) stress tests.

  • **OSU Micro-Benchmarks (OMB):**
   *   **Ping-Pong Latency (1-byte message):** 450 nanoseconds (Target: < 500 ns). This confirms the effectiveness of the NDR InfiniBand fabric and optimized verbs layer.
   *   **Maximum Bandwidth (1M-byte message):** 365 GB/s bidirectional transfer rate between nodes. This approaches the theoretical limit of the 400 Gb/s link speed (approximately 50 GB/s per link, or 100 GB/s aggregated across two links).

2.3 Scaling Efficiency Analysis

Scaling efficiency ($\eta$) measures how well performance increases as the number of cores ($P$) increases, defined as: $$\eta(P) = \frac{T_1}{P \cdot T_P}$$ Where $T_1$ is the time taken on 1 processor, and $T_P$ is the time taken on $P$ processors.

For tightly coupled simulations (e.g., Domain Decomposition methods):

  • **Scaling Target:** Maintain $\eta > 0.85$ up to 1024 cores.
  • **Observed Scaling:** For well-optimized codes utilizing collective communication primitives (like MPI_Allreduce), the observed efficiency typically drops to $\eta \approx 0.92$ at 1024 cores, indicating minimal communication overhead relative to computation time.

2.4 Storage I/O Performance

The parallel file system must sustain the aggregate I/O demands of the cluster during checkpointing or large data loading phases.

  • **Small File I/O (1KB writes):** Performance is limited by Metadata Server (MDS) latency. Target sustained rate: 50,000 IOPS across the cluster.
  • **Large File I/O (1MB+ reads):** Limited by OST bandwidth. Target sustained rate: 1.6 TB/s aggregate read bandwidth, achievable by simultaneous access from 512 nodes reading large contiguous blocks.

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3. Recommended Use Cases

This specific hardware configuration, characterized by high core density, massive memory capacity per node, and ultra-low-latency interconnects, is optimized for workloads that exhibit high communication intensity and require significant working memory.

3.1 Computational Fluid Dynamics (CFD)

CFD simulations, especially those involving transient, high-fidelity turbulence modeling (e.g., Large Eddy Simulation - LES), demand both massive parallelization and low latency.

  • **Requirement Fit:** The low latency of the IB fabric ensures that boundary condition updates and pressure Poisson equation solves across simulation domains remain fast, preventing communication waits from dominating computation time.
  • **Memory Impact:** The 1TB DDR5 per node allows for large local domain decomposition, reducing the frequency of necessary inter-node communication.
  • **Example Codes:** ANSYS Fluent (MPI parallelized version), OpenFOAM (large-scale meshes).

3.2 Molecular Dynamics (MD) and Quantum Chemistry

Simulations involving millions of atoms (MD) or large basis sets (Quantum Chemistry) are memory and communication intensive.

  • **MD Requirements:** The speed of neighbor searching and force calculation benefits directly from high core counts and fast memory access. GPU acceleration (H200) is crucial for scaling beyond modest system sizes.
  • **Quantum Chemistry:** Large basis set calculations (e.g., Coupled Cluster methods) often require massive temporary data structures residing in memory. The 1TB RAM pool mitigates swapping or reliance on slower storage.
  • **Example Codes:** GROMACS, NAMD, NWChem.

3.3 Large-Scale Weather and Climate Modeling

Global circulation models (GCMs) partition the atmosphere/ocean onto a grid. These models are the canonical example of tightly coupled, massive-scale HPC problems.

  • **Scaling:** These models typically scale well to thousands of nodes, making the high-efficiency scaling of the interconnect paramount.
  • **I/O Pattern:** Frequent checkpointing of the entire model state requires the high aggregate bandwidth of the Lustre/GPFS system.

3.4 Deep Learning Training (Large Language Models - LLMs)

While specialized AI clusters exist, this general-purpose configuration is highly effective for training massive foundation models that require both high compute density (GPUs) and substantial CPU/RAM headroom for data loading, pre-processing, and model orchestration.

  • **Role of H200 GPUs:** Provide the raw matrix multiplication throughput.
  • **Role of High-Speed Interconnect:** Essential for gradient synchronization across nodes during distributed training algorithms (e.g., All-Reduce operations).

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4. Comparison with Similar Configurations

To understand the value proposition of this HPC Cluster, it must be contrasted against two common alternatives: (A) A traditional CPU-only cluster optimized for throughput, and (B) A GPU-dense AI/ML cluster optimized for single-node compute power.

4.1 Configuration A: High-Throughput CPU Cluster (HTC)

This configuration prioritizes core count and local storage capacity over low-latency interconnects.

| Feature | HPC Cluster (Current Spec) | HTC CPU Cluster (Comparison A) | | :--- | :--- | :--- | | **Primary Compute** | 128 Cores/Node (Xeon, AVX-512) | 160 Cores/Node (AMD EPYC, High Core Count) | | **Memory/Node** | 1 TB DDR5 | 512 GB DDR4 | | **Interconnect** | 400 Gb/s InfiniBand NDR | 100 GbE (RoCEv2) | | **Accelerator** | 4x H200 GPUs | None | | **Storage Fabric** | 1.5 TB/s Parallel File System (Lustre) | 500 GB/s Networked NAS (NFS) | | **Latency Target** | < 500 ns | ~5-10 $\mu$s | | **Best For** | Tightly-coupled, low-latency MPI | Embarrassingly parallel jobs (Monte Carlo, parameter sweeps) |

    • Analysis:** Configuration A excels in throughput (more total cores for independent tasks) but fails catastrophically in tightly-coupled simulations where the 100 GbE latency is prohibitive, and the lack of GPU acceleration limits modern physics modeling.

4.2 Configuration B: GPU-Dense AI/ML Cluster

This configuration maximizes GPU density and inter-GPU communication speed, often sacrificing CPU memory capacity.

| Feature | HPC Cluster (Current Spec) | AI/ML GPU Cluster (Comparison B) | | :--- | :--- | :--- | | **Primary Compute** | 2x CPU Sockets + 4x H200 | 2x CPU Sockets + 8x H200 | | **Memory/Node** | 1 TB DDR5 (CPU) | 256 GB DDR5 (CPU) | | **Interconnect (Node-to-Node)** | InfiniBand NDR (400G) | NVIDIA NVLink/NVSwitch (900G/node) + InfiniBand | | **Interconnect (Intra-Node)** | PCIe Gen 5.0 | NVLink/NVSwitch (Direct GPU-to-GPU) | | **Storage Fabric** | Tiered Parallel File System | High-speed NVMe-oF (RDMA) | | **FP64 Performance** | High (Excellent for Science) | Moderate (Optimized for FP16/TF32) | | **Best For** | Scientific Simulation, LLM Training | Inference, Small/Medium Model Training |

    • Analysis:** Configuration B offers superior raw GPU power and faster GPU-to-GPU communication (via NVLink) critical for model parallelism in AI. However, the current HPC configuration balances this with significantly more CPU memory (4x), which is often the limiting factor for traditional scientific codes that do not map perfectly onto GPU memory structures. Furthermore, this HPC cluster maintains superior FP64 performance, essential for double-precision scientific accuracy.

4.3 Cost-Performance Tradeoffs

The inclusion of 400 Gb/s InfiniBand and 4x H200 GPUs significantly elevates the capital expenditure (CapEx) compared to a purely Ethernet-based cluster. However, the **time-to-solution** for complex simulations is drastically reduced, leading to lower operational expenditure (OpEx) per completed simulation cycle. For instance, reducing a 30-day simulation to 5 days via superior interconnect technology provides significant scientific return on investment (ROI).

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5. Maintenance Considerations

Deploying and maintaining an environment with this level of density and performance requires specialized infrastructure planning, particularly concerning power delivery, thermal management, and fabric integrity.

5.1 Power Requirements and Density

The density of high-TDP components (350W CPUs and 700W GPUs) results in substantial power draw per rack unit (RU).

  • **Node Power Draw (Peak):** A fully loaded GPU node can draw between 1800W and 2200W (including NICs and drives).
  • **Rack Density:** Assuming 42U racks, a configuration utilizing 20-24 nodes per rack is feasible, leading to a peak rack power consumption of 40 kW to 50 kW.
  • **Power Distribution Units (PDU):** PDUs must be rated for high amperage (e.g., 60A 208V three-phase input) with sufficient headroom for transient spikes during initial job startup or power cycling. Redundant (A/B feed) power delivery is mandatory for head nodes and shared storage components.

5.2 Thermal Management and Cooling

Effective heat removal is the single most critical operational challenge for high-density HPC deployments.

  • **Cooling Infrastructure:** Standard air cooling (CRAC/CRAH units) may struggle to maintain acceptable inlet temperatures (ASHRAE Class A1/A2 limits). **Direct Liquid Cooling (DLC)**, specifically cold plate technology integrated onto the CPUs and GPUs, is highly recommended for nodes exceeding 1.5 kW per chassis.
  • **Fluid Requirements (DLC):** If DLC is implemented, a dedicated cooling distribution unit (CDU) is required to manage the flow, pressure, and temperature of the non-conductive coolant loop, interfacing with the building chilled water supply.
  • **Airflow Management:** For air-cooled nodes, maintaining strict hot/cold aisle containment is non-negotiable to prevent hot air recirculation, which rapidly degrades component lifespan and performance stability.

5.3 Network Fabric Integrity and Monitoring

The InfiniBand fabric requires dedicated monitoring beyond standard Ethernet tools.

  • **Fabric Health Checks:** Regular execution of diagnostics tools (e.g., `ibdiagnet`, `ibstat`) to monitor link speeds, error counters (CRC errors, excessive port flappings), and path latency stability.
  • **Firmware Management:** InfiniBand switches and Host Channel Adapters (HCAs) must maintain synchronized firmware versions across the cluster to avoid interoperability issues or performance regressions. This is often managed centrally via the Head Node using specialized vendor tools.
  • **RDMA Verification:** Since the entire workload relies on Remote Direct Memory Access (RDMA), periodic testing of large-scale RDMA transfers is necessary to ensure the low-latency path remains clear of congestion or hardware degradation.

5.4 Storage Maintenance

The parallel file system requires proactive maintenance to ensure sustained performance.

  • **Data Scrubbing:** Regular, scheduled data scrubbing across the OSTs (Object Storage Targets) is required to detect and correct silent data corruption before it impacts simulations. This can impose a temporary 10-20% load on the storage fabric.
  • **Metadata Balancing:** As job patterns shift, the metadata load may become unevenly distributed across the MDS servers. Routine analysis and potential rebalancing of metadata pools are necessary.
  • **Burst Buffer Management:** The NVMe burst buffer must be monitored for write amplification and wear leveling. If used heavily for checkpointing, its lifespan must be tracked closely against vendor endurance ratings (TBW).

5.5 Software Stack Management

Maintaining the compatibility between the OS kernel, MPI implementation (e.g., Open MPI, MVAPICH2), driver stacks (GPU/HCA), and the workload manager (Slurm) is complex.

  • **Environment Modules:** Use environment modules systems (e.g., Lmod) rigorously to isolate application builds and ensure that different users run against compatible library versions, preventing dependency hell.
  • **Patching Strategy:** Due to the long runtimes of core simulations, patching cycles must be carefully managed. A "rolling upgrade" strategy, where subsets of nodes are patched and validated before system-wide deployment, is preferred over disruptive downtime.

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Comprehensive Summary of Configuration Rationale

This HPC Cluster configuration represents a **hybrid, accelerated, tightly-coupled system**. It is designed not merely for high peak theoretical performance, but for high *sustained, scalable* performance on complex scientific problems.

The key design choices—the 400 Gb/s InfiniBand fabric, the 1TB DDR5 memory per node, and the inclusion of state-of-the-art accelerators (H200)—are all directed at overcoming the major bottlenecks in modern computational science: communication latency and memory capacity/bandwidth. While the initial cost is high, the efficiency gains in time-to-solution for fields like aerospace engineering, materials science, and fundamental physics simulations justify the investment by dramatically increasing computational throughput over time.

The maintenance section underscores that such powerful systems are not plug-and-play; they require specialized cooling infrastructure and dedicated fabric monitoring expertise to realize their full potential reliably.


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