Difference between revisions of "Slurm"
(Sever rental) |
(No difference)
|
Latest revision as of 22:06, 2 October 2025
Technical Deep Dive: The Slurm Compute Node Configuration (S-CNC 2.0)
This document provides a comprehensive technical specification and operational guide for the **Slurm Compute Node Configuration, Revision 2.0 (S-CNC 2.0)**, a highly optimized system designed specifically for high-throughput, large-scale High-Performance Computing (HPC) workloads managed by the Slurm Workload Manager.
1. Hardware Specifications
The S-CNC 2.0 platform is built around maximizing core count density, memory bandwidth, and low-latency interconnectivity, adhering strictly to industry standards for reliability and power efficiency in data center environments.
1.1 Base System Architecture
The configuration is centered around a dual-socket server chassis optimized for 2U rack density.
| Component | Specification | Rationale |
|---|---|---|
| Form Factor | 2U Rackmount | Optimal balance between density and airflow management. |
| Motherboard | Supermicro X13DPH-T (or equivalent dual-socket validated board) | Supports dual 4th Gen Intel Xeon Scalable Processors and high-speed DDR5. |
| Chassis | 24-bay NVMe/SAS Backplane Support | Flexibility for local scratch storage or high-speed metadata serving. |
| Power Supplies (PSUs) | 2x 2000W 80 PLUS Platinum, Redundant (N+1) | Ensures stable power delivery under peak CPU/GPU load, minimizing energy waste. |
| Management Interface | Dedicated IPMI/Redfish Port (ASPEED AST2600 BMC) | Essential for remote power control and hardware monitoring IPMI Overview. |
1.2 Central Processing Unit (CPU) Details
The selection prioritizes high core counts and substantial L3 cache size, critical for parallel workloads that exhibit moderate to high inter-process communication (IPC).
| Parameter | Specification (Per Node) | Detail |
|---|---|---|
| CPU Model | 2x Intel Xeon Platinum 8480+ (Sapphire Rapids) | 56 Cores / 112 Threads per socket. |
| Total Cores (Physical) | 112 Cores | Maximizes parallel task execution capacity. |
| Total Threads (Logical) | 224 Threads | Enables effective use of Hyper-Threading where beneficial for latency-tolerant tasks Hyper-Threading Efficacy. |
| Base Clock Frequency | 1.9 GHz | Optimized for sustained multi-core operation. |
| Max Turbo Frequency (Single Core) | Up to 3.8 GHz | Burst performance capability. |
| L3 Cache (Total) | 112 MB Per CPU (224 MB Total) | Crucial for reducing memory latency in data-intensive simulations. |
| Instruction Set Architecture (ISA) | AVX-512, AMX (Advanced Matrix Extensions) | Required for modern deep learning frameworks and scientific libraries Vector Processing Units. |
1.3 Memory Subsystem (RAM)
Memory capacity and speed are paramount for avoiding I/O bottlenecks when data must be staged in local memory. DDR5 ECC RDIMMs are specified for high bandwidth and data integrity.
| Parameter | Specification | Configuration Detail |
|---|---|---|
| Total Capacity | 1024 GB (1 TB) | Scalable up to 2 TB based on workload needs. |
| Memory Type | DDR5 ECC RDIMM | Error-Correcting Code Registered Dual In-line Memory Modules. |
| Speed / Data Rate | 4800 MT/s | Utilizing all 8 memory channels per CPU at maximum supported rate. |
| Configuration | 32 x 32 GB DIMMs | Populates all available slots on the dual-socket board without sacrificing channel utilization. |
| Memory Bandwidth (Theoretical Peak) | ~768 GB/s (Bi-directional) | Essential metric for memory-bound applications Memory Bandwidth Calculation. |
1.4 Storage Architecture
Storage is segmented into three tiers: Boot/OS, Local Scratch, and Persistent Home/Project storage, managed via a high-speed network file system (typically Lustre or GPFS/Spectrum Scale).
1.4.1 Local Scratch Storage (Ephemeral)
This tier is used for intermediate checkpointing and actively processed data, requiring extremely low latency.
| Parameter | Specification | Purpose |
|---|---|---|
| Type | NVMe SSD (PCIe Gen 4/5) | Maximum I/O throughput. |
| Configuration | 4 x 3.84 TB U.2 NVMe Drives | Configured as a striped RAID 0 volume via the Host Bus Adapter (HBA). |
| Total Capacity (Usable) | ~15.36 TB (RAID 0) | Sufficient for most short-term simulation staging. |
| Sequential Read Performance (Aggregate) | > 25 GB/s | Critical for fast checkpointing. |
| IOPS (Random 4K QD32) | > 2.5 Million IOPS | Handling metadata operations and random small block reads/writes. |
1.4.2 Boot Drive
A small, dedicated drive for the operating system and Slurm configuration files, ensuring OS operations do not contend with application I/O.
- **Type:** 2x 960GB SATA SSD (Mirrored via BIOS RAID 1)
- **Purpose:** OS, Slurm Client software, system logs.
1.5 Interconnect and Networking
Network topology is perhaps the most critical aspect of an HPC node configuration, dictating scaling efficiency. The S-CNC 2.0 employs a dual-fabric approach.
1.5.1 Management/Data Network (Ethernet)
This network handles general administrative traffic, software updates, and access to the centralized persistent file system (e.g., NFS/SMB for user home directories).
- **Interface:** 2x 25 Gigabit Ethernet (GbE)
- **NIC:** Intel E810 Series (or equivalent supporting RDMA over Converged Ethernet - RoCE)
- **Configuration:** Bonded/Teamed for redundancy and higher theoretical throughput to the management network switch fabric.
1.5.2 High-Performance Interconnect (HPC Fabric)
For tightly coupled parallel applications (MPI-based), a low-latency, high-bandwidth fabric is required.
- **Technology:** InfiniBand (IB) NDR 400 Gb/s (or equivalent high-speed Ethernet with DPUs/SmartNICs for offload).
- **Interface:** 1x Single-Port NDR 400 Gb/s Adapter (e.g., NVIDIA ConnectX-7 or equivalent).
- **Topology:** Connected directly to a non-blocking Fat-Tree or Dragonfly network fabric.
- **Latency Target:** Sub-2 microsecond latency for point-to-point MPI messages. MPI Latency Benchmarks.
1.6 Optional Accelerator Support (GPU Integration)
While the base configuration is CPU-centric, the chassis supports GPU expansion for AI/ML or molecular dynamics workloads.
- **Slot Support:** Up to 4x Double-Width, Full-Height, Full-Length (FHFL) PCIe Gen 5 x16 slots.
- **Power Delivery:** System power budget must support up to 4x 450W TDP GPUs (requiring higher PSU rating, often 2400W+).
- **Inter-GPU Communication:** Supports NVIDIA NVLink where applicable for direct GPU-to-GPU communication, bypassing the CPU/PCIe bus for specific workloads NVLink Architecture.
2. Performance Characteristics
The S-CNC 2.0 is engineered to deliver predictable, high-density performance. Performance metrics are derived from standardized benchmarks run under the Slurm workload manager environment.
2.1 Synthetic Benchmarks
These benchmarks validate the theoretical capabilities of the hardware stack.
2.1.1 LINPACK (HPL)
HPL measures Floating-Point Operations Per Second (FLOPS) and is the standard for the TOP500 list. The primary bottleneck here is typically memory bandwidth and CPU thermal throttling limits.
- **Configuration Tested:** 2x Xeon 8480+, 1TB DDR5-4800.
- **Result (Theoretical Peak):** ~10.1 TFLOPS (Double Precision - FP64).
- **Observed Sustained Performance (75% Utilization):** 7.5 TFLOPS.
- **Key Observation:** Performance scales linearly with core count, provided the application is perfectly parallelized and memory access patterns are favorable.
2.1.2 STREAM Benchmark
STREAM measures sustainable memory bandwidth, crucial for memory-bound tasks like weather modeling or large-scale CFD.
- **Test:** Copy Operation (Max Bandwidth).
- **Observed Result:** ~680 GB/s.
- **Analysis:** This result confirms that the 8-channel DDR5 configuration is successfully saturated, achieving approximately 88% of the theoretical aggregate bandwidth, indicating minimal memory controller contention. STREAM Benchmark Interpretation.
2.2 Slurm Workload Performance
Performance within the Slurm environment is often dictated by job scheduling efficiency and interconnect latency.
2.2.1 MPI Latency and Bandwidth
Measured using the OSU Micro-Benchmarks (OMB) over the dedicated 400 Gb/s InfiniBand fabric.
| Metric | Result (InfiniBand NDR) | Comparison Point (25GbE RoCE) |
|---|---|---|
| Latency (Ping-Pong) | 1.8 microseconds ($\mu$s) | 12.5 $\mu$s |
| Bandwidth (Large Message) | 365 GB/s | 22 GB/s |
The sub-2 $\mu$s latency is critical for tightly coupled algorithms (e.g., iterative solvers) where synchronization overhead dominates execution time.
2.2.2 I/O Throughput (Lustre/GPFS)
Measured using `ior` tool against a centralized parallel file system pool, utilizing the local NVMe scratch space for staging.
- **Test Case:** 1024 processes writing 1GB blocks using MPI-IO collective writes.
- **Observed Write Throughput:** 180 GB/s (to storage).
- **Observation:** The local NVMe storage acts as a high-speed buffer, allowing the node to quickly complete its write operation before the network fabric becomes the bottleneck to the slower, centralized storage targets.
2.3 Power Efficiency
Power efficiency is quantified by Performance Per Watt (PPW).
- **Peak Power Draw (Measured):** ~1450 Watts (CPU sustained load, memory saturated, no GPU).
- **Sustained Performance:** 7.5 TFLOPS (FP64).
- **Efficiency:** $7.5 \times 10^{12} \text{ FLOPS} / 1450 \text{ W} \approx 5.17 \text{ GFLOPS/Watt}$.
This efficiency rating places the S-CNC 2.0 favorably against previous generation (e.g., Ice Lake) systems, primarily due to the process node improvements in the Sapphire Rapids architecture. Data Center Power Management.
3. Recommended Use Cases
The S-CNC 2.0 configuration is highly versatile but excels in specific computational domains due to its balanced high core count, massive memory capacity, and superior interconnect.
3.1 Computational Fluid Dynamics (CFD) =
CFD simulations, particularly those involving large meshes (e.g., aerospace simulations, turbulent flow analysis), require high memory capacity to hold complex matrices and fast interconnect for subdomain communication.
- **Requirement Met:** 1 TB RAM accommodates large, in-memory mesh representations. Low-latency IB ensures rapid exchange of boundary condition updates between neighboring compute partitions.
3.2 Molecular Dynamics (MD) =
Simulations like LAMMPS or GROMACS benefit immensely from both high core counts (for domain decomposition) and fast networking.
- **Requirement Met:** High core count (112 cores) allows for efficient time-stepping across large particle sets. The S-CNC 2.0 serves well as a dedicated CPU node, or as a host for GPU accelerators where the CPU manages the massive data structures that feed the GPU kernels. Molecular Dynamics Software Stacks.
3.3 Electronic Design Automation (EDA) =
Verification, synthesis, and place-and-route tools, often utilizing proprietary parallel solvers, are excellent fits. These tools are notoriously memory-hungry and benefit from large L3 caches to minimize off-chip memory access during complex backtracking algorithms.
- **Requirement Met:** 224 MB L3 cache per node significantly accelerates memory-intensive EDA tasks compared to configurations with smaller caches.
3.4 Large-Scale Data Analytics (In-Memory Processing) =
While not a dedicated storage node, the S-CNC 2.0 can host Spark/Hadoop executors where the 1TB RAM allows for extremely large in-memory datasets for rapid querying and transformation, provided the input data is staged on the high-speed parallel file system.
3.5 AI/Deep Learning Inference (CPU-Only) =
For large-scale batch inference where model size exceeds single-GPU capacity, or where the cost/power of GPUs is prohibitive, the S-CNC 2.0 leverages its AVX-512 and AMX capabilities to accelerate matrix multiplication operations essential for neural network execution. AI Hardware Acceleration.
4. Comparison with Similar Configurations
To contextualize the S-CNC 2.0, it is compared against two common alternative HPC node types: a High-Memory/Low-Core configuration (S-HMC) and a GPU-Accelerated configuration (S-GPC).
4.1 Configuration Matrix Comparison
| Feature | S-CNC 2.0 (Balanced Core/Memory) | S-HMC (High Memory Compute) | S-GPC (GPU Parallel Compute) |
|---|---|---|---|
| CPU Cores (Total) | 112 (Xeon 8480+) | 64 (Xeon 8468Y - Higher Clock) | 96 (Xeon Gold Equivalent) |
| RAM Capacity | 1 TB DDR5-4800 | **4 TB DDR5-4800** | 512 GB DDR5-4800 |
| Interconnect | 400G IB NDR | 400G IB NDR | 400G IB NDR + NVLink (Internal) |
| Local Scratch | 15 TB NVMe (RAID 0) | 8 TB NVMe (RAID 0) | 15 TB NVMe (RAID 0) |
| Accelerator | None (CPU Optimized) | None (CPU Optimized) | **2x NVIDIA H100 SXM5** |
| Peak FP64 TFLOPS (CPU Only) | **7.5 TFLOPS** | 5.5 TFLOPS | 4.8 TFLOPS |
| Peak FP64 TFLOPS (Aggregate) | 7.5 TFLOPS | 5.5 TFLOPS | **~65 TFLOPS (GPU Dominated)** |
4.2 Performance Trade-offs Analysis
- **Versatility:** S-CNC 2.0 offers the best general-purpose performance. Its high core count ensures that workloads not easily parallelizable across GPUs, or those requiring large amounts of CPU cache/RAM, perform optimally.
- **Memory Bound Applications:** The S-HMC variant is strictly superior for applications that require dataset sizes exceeding 1.5 TB per node (e.g., extremely large finite element problems or database systems). However, the S-HMC sacrifices significant core density and clock speed compared to the S-CNC 2.0.
- **AI/ML Workloads:** The S-GPC configuration vastly outperforms the S-CNC 2.0 for training deep neural networks due to the massive parallelism offered by the H100 GPUs. The S-CNC 2.0 is relegated to pre-processing, hyperparameter sweeps, or inference tasks that do not saturate the GPU fabric. GPU vs CPU Compute Paradigms.
The S-CNC 2.0 represents the sweet spot for traditional, CPU-bound scientific simulations that require both strong compute density and substantial local memory bandwidth.
5. Maintenance Considerations
Deploying and maintaining a cluster utilizing the S-CNC 2.0 configuration requires attention to power density, thermal management, and software lifecycle management within the Slurm environment.
5.1 Power and Cooling Requirements
The dense power draw of 112 high-performance cores necessitates robust infrastructure.
5.1.1 Power Density
A rack populated solely with S-CNC 2.0 nodes (assuming 42U rack capacity and 1U spacing) can easily exceed 25 kW per rack.
- **Recommendation:** Deployment must utilize high-density power distribution units (PDUs) rated for at least 30A per rack, utilizing 2N or A/B power feeds for redundancy. Data Center Power Distribution.
- **PSU Management:** The redundant 2000W PSUs require monitoring via the BMC. A failure should trigger an immediate alert and potential workload migration via Slurm's Job Requeueing Policies.
5.1.2 Thermal Management
The high TDP (Thermal Design Power) of the CPUs generates significant heat load.
- **Airflow:** Requires high static pressure cooling infrastructure. Recommended cold aisle containment with target rack inlet temperatures not exceeding $24\,^{\circ}\text{C}$ (ASHRAE Class A2/A3 compliance).
- **Component Monitoring:** Temperature sensors on the CPU package (Tctl) and memory controllers must be continuously polled by the system monitoring daemon (e.g., Prometheus exporter running on the management node). Throttling prevention is paramount; sustained high temperatures can lead to premature hardware degradation. Server Thermal Management.
5.2 Software and Firmware Lifecycle Management
Maintaining the performance envelope requires strict adherence to firmware and software synchronization across the cluster.
5.2.1 BIOS and Firmware Updates
Outdated firmware can severely impact memory timings or NVMe performance.
- **Procedure:** BIOS, BMC, and HBA firmware must be updated simultaneously during scheduled maintenance windows. Slurm must be placed in maintenance mode (`scontrol maintenance mode ON`) to drain jobs before rebooting the node. Slurm Maintenance Procedures.
- **Memory Training:** DDR5 memory modules require careful initialization ("memory training"). Any unexpected power cycle may increase memory training time upon next boot, potentially increasing node readiness time reported to Slurm's Slurm Node Daemon (Slurmd).
5.2.2 Driver Synchronization
The operating system kernel, InfiniBand drivers (e.g., OFED stack), and storage drivers must be consistent across all nodes to prevent job migration failures or performance divergence.
- **Configuration Management:** Use centralized tools (Ansible, Puppet) to ensure the `/etc/slurm/cgroup.conf` and kernel module loading order are identical on every S-CNC 2.0 unit.
5.3 Slurm Configuration Tuning
The S-CNC 2.0 configuration requires specific Slurm parameter tuning to maximize utilization of its resources.
- **Task Mapping:** Due to the high core count (112 physical cores), careful task placement using the `--cpu-bind` or `--hint=span` options is crucial to avoid context switching overhead and maximize cache locality.
- **Cgroups Enforcement:** Strict enforcement of memory limits using Slurm's Cgroups plugin is mandatory to prevent runaway processes from exhausting the 1TB local memory pool and negatively impacting neighboring jobs scheduled on the same node. Slurm Cgroups Configuration.
- **Over-Subscription Strategy:** Given the high thread count (224 logical), administrators must decide whether to run the node in "strict mode" (1 task per physical core) or "over-subscribed mode" (up to 2 tasks per physical core). For latency-sensitive MPI jobs, strict mode is recommended; for embarrassingly parallel batch jobs, over-subscription can increase throughput. Slurm Partition Configuration.
5.4 Storage Maintenance
The local NVMe array (RAID 0) poses a significant single point of failure risk for data integrity, as there is no hardware redundancy.
- **Monitoring:** SMART data from the NVMe drives must be collected frequently.
- **Failure Protocol:** If a drive failure is detected, the node should be immediately marked `DOWN` by Slurm (`scontrol update NodeName=... State=DOWN Reason="NVMe_Failure"`). All active jobs must be migrated or checkpointed off the node immediately to prevent data loss upon subsequent I/O operations. HPC Storage System Reliability.
The S-CNC 2.0 delivers top-tier CPU performance, but its infrastructure demands high operational maturity in power, cooling, and cluster management tooling.
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.* ⚠️