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Latest revision as of 22:06, 2 October 2025
Technical Deep Dive: Slurm Workload Manager Optimized Server Configuration (Model: HPC-S24-SLRM)
This document details the specifications, performance metrics, operational considerations, and recommended deployment scenarios for a high-performance computing (HPC) node specifically optimized for operation as a primary or secondary node within a Slurm cluster environment. This configuration prioritizes computational density, low-latency interconnectivity, and robust I/O capabilities essential for modern scientific simulations and data-intensive workloads managed by Slurm.
1. Hardware Specifications
The HPC-S24-SLRM configuration is engineered for maximum throughput under the scheduling directives provided by Slurm, ensuring optimal utilization of allocated resources. The system is built upon a dual-socket architecture leveraging the latest Intel Xeon Scalable Processor family, optimized for floating-point operations critical in many HPC domains.
1.1 Central Processing Unit (CPU)
The choice of CPU directly impacts the job execution time, which is a key metric monitored and optimized by the Slurm scheduler via the Slurm Accounting Database (Slurmdbd).
| Parameter | Specification | Rationale |
|---|---|---|
| Processor Model | 2 x Intel Xeon Platinum 8592+ (Sapphire Rapids) | High core count (64 cores/128 threads per socket) for parallel tasks. |
| Core Count (Total) | 128 Physical Cores / 256 Logical Threads | Maximizes job density and parallelism within a single allocation. |
| Base Clock Speed | 2.1 GHz | Balanced frequency for sustained high-load operation. |
| Turbo Frequency (Max Single Core) | Up to 3.9 GHz | Beneficial for latency-sensitive, serial portions of hybrid workloads. |
| L3 Cache (Total) | 192 MB (96 MB per CPU) | Large cache minimizes trips to main memory, crucial for memory-bound applications. |
| Instruction Set Architecture (ISA) | AVX-512, AMX (Advanced Matrix Extensions) | Essential for accelerating deep learning inference and certain scientific kernels (BLAS implementations). |
| Thermal Design Power (TDP) | 350W per socket | Requires robust cooling infrastructure, detailed in Section 5. |
The choice of the Platinum series ensures maximum PCIe lanes (112 lanes per CPU), vital for high-speed storage and network interconnectivity required by Slurm jobs demanding rapid data shuffling.
1.2 System Memory (RAM)
Slurm's memory management depends heavily on accurate reporting of available physical memory. This configuration utilizes high-density, high-speed DDR5 memory.
| Parameter | Specification | Notes |
|---|---|---|
| Total Capacity | 2 TB (Terabytes) | Sufficient for large-scale molecular dynamics or CFD simulations. |
| Memory Type | DDR5 ECC Registered (RDIMM) | High reliability and speed (4800 MT/s minimum sustained). |
| Configuration | 32 x 64 GB DIMMs (Populated across 16 channels per CPU) | Optimal channel utilization for maximum memory bandwidth. |
| Memory Bandwidth (Theoretical Peak) | ~1.2 TB/s (Bi-directional) | Critical for avoiding memory bottlenecks in data-intensive tasks. |
| NUMA Topology | Dual-Socket Non-Uniform Memory Access (NUMA) | Slurm job allocation policies (e.g., `--membind`) must respect the two distinct NUMA domains. |
Proper configuration of NUMA awareness within the Slurm job configuration files (`sbatch` directives) is paramount to achieving the advertised memory bandwidth.
1.3 Storage Subsystem
Effective storage is crucial, particularly for I/O-heavy workloads like seismic processing or large-scale genomics analysis. This configuration employs a tiered storage approach managed locally, with high-speed scratch space accessible via the cluster filesystem.
1.3.1 Local Boot and Configuration Storage
A dedicated NVMe drive is used for the operating system (typically CentOS Stream or Rocky Linux) and Slurm configuration files.
- **Drive 1 (OS/Boot):** 2 x 960 GB Enterprise NVMe SSDs in RAID 1 (for redundancy).
- **Purpose:** Housing `/etc/slurm/`, system logs, and the local Slurm daemon (`slurmd`) binaries.
1.3.2 High-Speed Scratch Storage (Local Burst Buffer)
For jobs requiring extremely fast local I/O access that exceeds Lustre or GPFS network performance, a dedicated local NVMe array is configured.
| Parameter | Specification | Configuration |
|---|---|---|
| Drives | 8 x 7.68 TB Enterprise U.2 NVMe SSDs | High endurance (DWPD). |
| Total Capacity (Usable) | ~50 TB (After RAID 10 parity) | Provides high IOPS for checkpointing and intermediate results. |
| Interface | Direct connection via PCIe Gen 5.0 HBA/RAID Controller | Bypassing traditional SAS/SATA bottlenecks. |
| Target Filesystem | ZFS or LVM striped over XFS | Optimized for high sequential throughput. |
This local scratch space is often mounted as `/scratch_local` and referenced within Slurm job scripts using environment variables like `$SCRATCH_LOCAL`.
1.4 High-Speed Interconnect (Networking)
Slurm performance in tightly coupled applications (e.g., MPI applications) scales directly with network latency and bandwidth between compute nodes.
| Network Type | Specification | Purpose / Protocol |
|---|---|---|
| Management/Control Network | 2 x 10 GbE (RJ-45) | Slurm communication (`scontrol`, `squeue`), SSH access, monitoring (e.g., Nagios). |
| High-Performance Interconnect (HPI) | 2 x NVIDIA ConnectX-7 InfiniBand (HDR 200 Gb/s) | Primary fabric for MPI traffic. Supports Remote Direct Memory Access. |
| Cluster Filesystem Access | 1 x 100 GbE (QSFP28) | Dedicated link for accessing the shared Lustre or GPFS parallel filesystem. |
The dual-port InfiniBand configuration allows for resilient, high-bandwidth communication paths, essential when Slurm allocates large contiguous job blocks across multiple nodes.
1.5 Accelerator Support (Optional Module)
While the base configuration is CPU-centric, the platform supports the integration of accelerators, often managed by Slurm via the GRES (Generic Resource) mechanism.
- **Support:** Up to 8 x NVIDIA H100 SXM5 GPUs.
- **Interconnect:** NVLink (4th Generation) for intra-node GPU communication, and NVSwitch for full-mesh connectivity.
- **Slurm Integration:** Requires the `gres.conf` file to correctly enumerate these resources, allowing users to request GPUs via `#SBATCH --gres=gpu:H100:4`.
2. Performance Characteristics
The performance evaluation of a Slurm-managed node focuses on how efficiently it executes jobs under various constraints (CPU-bound, memory-bound, I/O-bound). Benchmarks are typically run using standardized HPC application suites.
2.1 CPU Benchmarks (Synthetic Tests)
These synthetic tests measure the raw computational capability of the dual-socket configuration.
| Benchmark Tool | Metric | Result | Notes |
|---|---|---|---|
| HPL (High-Performance Linpack) | GFLOPS (Double Precision) | ~10.5 TFLOPS (Sustained) | Reflects peak theoretical double-precision floating-point performance. |
| STREAM (Memory Bandwidth) | MB/s (Copy Operation) | ~1,100,000 MB/s | Confirms effective utilization of DDR5 bandwidth. |
| SPEC CPU 2017 (Integer Rate) | Score | ~2,400 | Indicates strong performance for inherently serial or branching workloads. |
| STREAM (Latency) | Nanoseconds (First Access) | ~60 ns (NUMA local access) | Lower latency is crucial for synchronization primitives in MPI. |
The high HPL score demonstrates that this hardware is well-suited for traditional CFD and physics simulations where the workload is dominated by matrix operations.
2.2 Slurm Job Execution Profiling
Performance in a Slurm environment is often measured by **Job Turnaround Time (JTT)** and **Resource Utilization Efficiency (RUE)**.
- 2.2.1 MPI Job Scaling
For tightly coupled MPI codes (e.g., OpenFOAM, LAMMPS), performance is measured by weak and strong scaling efficiency across multiple nodes.
- **Strong Scaling Test (128 Cores / 1 Node):** Execution time reduces by 92% when scaling from 16 cores to 128 cores on this single node, indicating excellent intra-node communication via NVLink (if GPUs are present) or high-speed PCIe/DDR5 bus utilization.
- **Inter-Node Scaling (Over InfiniBand):** When scaling to 16 nodes (2048 cores), the measured overhead (latency penalty) averages 1.8x compared to ideal scaling, which is attributed primarily to the standard MPI barrier synchronization latency over the 200 Gb/s fabric.
- 2.2.2 I/O Performance Testing
Using `ior` utility, the performance against the cluster's shared parallel filesystem (assuming a high-performance Lustre deployment) is measured.
- **Sequential Write Throughput:** 85 GB/s sustained across 128 cores writing simultaneously.
- **Random Read IOPS (4K Blocks):** 450,000 IOPS sustained.
If a job utilizes the local NVMe scratch (Section 1.3.2), the sequential write throughput jumps to over 150 GB/s, highlighting the benefit of local burst buffering for I/O spikes during checkpointing operations managed by Slurm's checkpoint/restart features.
2.3 Thermal and Power Performance
Sustained peak load running 100% utilization (e.g., during a stress test managed by Slurm's monitoring tools) results in significant power draw.
- **Peak Power Draw (CPU Only):** ~1200W.
- **Peak Power Draw (CPU + Max RAM + Network):** ~1500W.
- **Thermal Output:** Requires maintaining ambient rack temperature below 25°C to ensure sustained clock speeds above the base frequency.
Effective Power Management policies within the BIOS, often controlled by Slurm's configuration hooks, are necessary to balance power consumption against performance targets.
3. Recommended Use Cases
The HPC-S24-SLRM configuration is a versatile workhorse, but its high core count and substantial memory capacity make it optimally suited for specific computationally intensive workloads managed by Slurm's priority and quality-of-service (QoS) mechanisms.
3.1 Computational Fluid Dynamics (CFD)
CFD simulations (using solvers like ANSYS Fluent, OpenFOAM, or Nek5000) typically require large amounts of memory per core and benefit significantly from high core counts for parallel decomposition of the mesh.
- **Slurm Benefit:** Users can request large contiguous blocks of memory (`#SBATCH --mem=256g`) on a single node for high-fidelity local runs, or scale across many nodes using MPI, relying on the InfiniBand fabric for fast inter-node data exchange.
3.2 Molecular Dynamics (MD) and Quantum Chemistry
These applications (e.g., GROMACS, NAMD, VASP) are often highly parallelizable but have significant memory requirements per atom or basis set element.
- **Memory Intensive:** The 2TB RAM capacity allows for simulations with millions of particles without excessive swapping or reliance on slower network storage for intermediate states.
- **Checkpointing:** Slurm's ability to pause and resume jobs (`scontrol suspend/resume`) is critical for long-running MD simulations, which frequently use the fast local NVMe scratch for frequent checkpoint saves.
3.3 Large-Scale Data Analytics and Machine Learning Preprocessing
While dedicated GPU nodes handle model training, CPU-heavy nodes like this are ideal for data ingestion, feature engineering, and hyperparameter sweeps managed by distributed frameworks like Dask or Spark, orchestrated via Slurm job arrays.
- **Job Array Efficiency:** Slurm Job Arrays (`#SBATCH --array=1-1000`) allow rapid launching of thousands of independent data processing tasks, where each task leverages the full 256 threads for fast local processing before reporting results back to a central SQL or NoSQL database.
3.4 Weather Modeling and Global Climate Simulation
These simulations rely on massive grid computations. The high core clock speed (when not fully saturated) and the massive memory bandwidth ensure that the physics kernels execute efficiently before data aggregation.
- **I/O Spikes:** The configuration handles the inevitable I/O spikes associated with writing global state snapshots (which can reach hundreds of gigabytes) to the parallel filesystem rapidly.
4. Comparison with Similar Configurations
To contextualize the HPC-S24-SLRM, it is useful to compare it against two common alternatives in a heterogeneous Slurm cluster environment: a **Memory-Optimized Node (HPC-M4TB)** and a **GPU-Accelerated Node (HPC-G200)**.
The comparison highlights where the HPC-S24-SLRM achieves the best balance of generalized throughput and computational density.
| Feature | HPC-S24-SLRM (Current) | HPC-M4TB (Memory Optimized) | HPC-G200 (GPU Accelerated) |
|---|---|---|---|
| CPU (Cores/Threads) | 128 / 256 (High Density) | 96 / 192 (Slightly Lower Density) | 64 / 128 (Lower Density) |
| System RAM | 2 TB DDR5 | 4 TB DDR5 (Dual the capacity) | 1 TB DDR5 (Less system RAM) |
| Onboard NVMe Scratch | 50 TB (RAID 10) | 25 TB (RAID 1) | 75 TB (High IOPS focus) |
| High-Speed Interconnect | 200 Gb/s InfiniBand (2 Ports) | 200 Gb/s InfiniBand (2 Ports) | 400 Gb/s InfiniBand (4 Ports, mandatory for GPU peer-to-peer) |
| Accelerator Support | None (CPU-centric) | None (CPU-centric) | 8 x H100 SXM5 GPUs |
| Ideal Slurm Workload | General Purpose HPC, Large MPI jobs, Data Preprocessing | Large in-memory databases, Genomics assembly, In-memory caching | Deep Learning Training, AI Inference, Monte Carlo methods requiring GPU acceleration |
4.1 CPU vs. GPU Focus
The primary differentiator is the absence of GPUs. While the HPC-S24-SLRM can utilize GRES for GPU allocation if temporarily fitted, its peak performance in pure floating-point operations (TFLOPS) is significantly lower than the HPC-G200 node *if the workload is GPU-accelerated*.
However, for workloads that cannot be efficiently ported to GPU programming models (CUDA/OpenCL) or those that rely heavily on complex branching logic where high core count uniformity is preferred, the S24-SLRM excels. Slurm administrators must carefully manage the GRES reservations to ensure GPU-native jobs are routed to the G200 nodes via constraints like `#SBATCH --constraint=gpu_heavy`.
4.2 Comparison to Memory-Optimized Nodes
The HPC-M4TB node offers double the system memory (4TB). This is crucial for workloads that exceed the 2TB limit of the S24-SLRM, such as massive relational database processing or genome assembly where the entire dataset must reside in RAM for speed.
The S24-SLRM compensates by offering higher aggregate CPU throughput (128 cores vs. 96 cores) and faster local NVMe storage, making it better for CPU-bound simulations that require frequent local data manipulation rather than pure memory residency.
5. Maintenance Considerations
Deploying and maintaining a high-density compute node like the HPC-S24-SLRM requires rigorous attention to power delivery, thermal management, and software integrity, particularly concerning the Slurm daemons (`slurmd`).
5.1 Power Requirements and Redundancy
With a steady-state operational power draw approaching 1.5 kW, power density within the rack is a primary concern.
- **Power Delivery:** Each chassis must utilize dual, redundant 240V AC power supplies (N+1 configuration). The rack PDU infrastructure must be rated to handle sustained 80A per rack unit for a full enclosure of these nodes.
- **Power Monitoring:** Integration with the cluster monitoring system (e.g., Prometheus/Grafana) is essential. Slurm administrators should implement power capping policies via ACPI or vendor-specific management tools if the facility power budget is constrained, though this may throttle performance.
5.2 Thermal Management and Cooling
The 350W TDP CPUs generate substantial heat. Standard air cooling may be insufficient for sustained peak operation.
- **Airflow Requirements:** Minimum required static pressure from front-to-back airflow must exceed 1.5 inches of water column (iwc). Hot aisle containment is strongly recommended for racks housing more than four S24-SLRM units.
- **Liquid Cooling Consideration:** For future upgrades or highly dense deployments, the chassis must support direct-to-chip liquid cooling (DLC) loops for the CPUs, especially if higher TDP processors are substituted. The current 8500 series CPUs are generally manageable with high-end air cooling if ambient temperatures are strictly controlled below 22°C.
5.3 Software Integrity and Slurm Daemon Health
The reliability of the `slurmd` daemon running on this node is critical, as its failure prevents any job allocation or execution on this hardware.
- **Slurmd Monitoring:** Health checks must be configured to monitor the `slurmd` process state, memory usage, and network connectivity to the Slurm Controller (Slurmctld). A failure should trigger an immediate node state change in Slurm:
* `scontrol update NodeName=hpc-s24-01 State=DRAIN` * Followed by an alert to the operations team.
- **Firmware and Drivers:** Regular updates to the BIOS, BMC (Baseboard Management Controller), and the InfiniBand Drivers (e.g., OFED stack) are mandatory. Outdated drivers often lead to unexpected RDMA connection drops, causing MPI jobs spanning multiple S24-SLRM nodes to fail silently or terminate prematurely under Slurm's execution monitoring.
5.4 Storage Maintenance
The local NVMe array requires specific attention due to its high activity profile.
- **Wear Leveling:** Monitoring the drive endurance metrics (e.g., SMART data for TBW/DWPD usage) is necessary. If an NVMe drive shows excessive wear, it must be replaced proactively before failure, ideally scheduled during low-utilization periods enforced by Slurm’s QoS settings.
- **Filesystem Integrity:** Regular checks on the local XFS or ZFS filesystem integrity are required, potentially scheduled during maintenance windows defined in the Slurm configuration (`$SlurmConfDir/cgroup.conf`).
Conclusion
The HPC-S24-SLRM configuration represents a state-of-the-art, high-density compute node optimized for general-purpose scientific computing managed robustly by Slurm Workload Manager. Its balance of 128 high-performance cores, 2TB of fast DDR5 memory, and superior InfiniBand interconnectivity ensures high throughput for large, complex simulations that benefit from massive parallelism but may not require dedicated GPU acceleration. Proper environmental controls and rigorous software upkeep, especially concerning Slurm integration, are essential for maximizing the Return on Investment (ROI) of this powerful hardware asset.
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 |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️