Compute Node Allocation Policy
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Compute Node Allocation Policy
This document details the technical specifications, performance characteristics, recommended use cases, comparisons, and maintenance considerations for the "Compute Node Allocation Policy" server configuration. This configuration is designed for high-density compute workloads, prioritizing processing power and memory capacity over extensive local storage. It is a cornerstone of our Cloud Infrastructure and is frequently deployed in High Performance Computing (HPC) environments.
1. Hardware Specifications
The Compute Node Allocation Policy configuration uses a standardized hardware platform to ensure consistency and ease of management across the fleet. The following specifications represent the current generation (v3.0) hardware.
| Component | Specification | Details |
|---|---|---|
| CPU | Dual Intel Xeon Platinum 8480+ | 56 cores / 112 threads per CPU, Base Clock: 2.0 GHz, Max Turbo Frequency: 3.8 GHz, Total L3 Cache: 105 MB per CPU, CPU Architecture details available on the internal wiki. |
| RAM | 512 GB DDR5 ECC Registered | 8 x 64 GB DIMMs, Speed: 4800 MHz, Memory Channel Configuration is 8-channel per CPU. Utilizes RDIMM Technology for enhanced reliability. |
| Storage (Boot) | 1 TB NVMe PCIe Gen4 SSD | Used for operating system and boot loader. SSD RAID Configuration is not employed; single drive for simplicity. |
| Storage (Local) | None (Optional 2TB NVMe PCIe Gen4 SSD for temporary workspace) | This configuration deliberately minimizes local storage. Reliance on Network File System (NFS) and Object Storage is expected. |
| Network Interface | Dual 200 Gbps Ethernet | Mellanox ConnectX-7, RDMA capable, Network Bonding configured for redundancy and increased bandwidth. Supports RoCEv2 Protocol. |
| Interconnect | NVLink 4.0 | Facilitates high-bandwidth, low-latency communication between CPUs. NVLink Topology is direct CPU-to-CPU. |
| Power Supply | 2 x 1600W 80+ Titanium | Redundant power supplies for high availability. Power Distribution Unit (PDU) details are documented separately. |
| Chassis | 2U Rackmount | Standard 19-inch rackmount form factor. Chassis Cooling System details are below. |
| Remote Management | IPMI 2.0 with dedicated NIC | Enables out-of-band management and remote access. IPMI Security Considerations are documented. |
| Motherboard | Supermicro X13DEI-N6 | Custom-designed motherboard optimized for dual CPU configuration and high memory capacity. Motherboard Schematic available internally. |
2. Performance Characteristics
This configuration excels in compute-intensive tasks. We’ve conducted extensive benchmarking to quantify its performance. All benchmarks were performed in a controlled environment with consistent cooling and power conditions. Results are compared against our previous generation (v2.0) Compute Node and a comparable competitor's offering (Competitor X).
| Benchmark | Compute Node Allocation Policy (v3.0) | Previous Generation (v2.0) | Competitor X | Units |
|---|---|---|---|---|
| LINPACK (HPL) | 5.2 PFLOPS | 3.8 PFLOPS | 4.9 PFLOPS | Floating-point operations per second |
| STREAM Triad | 2.8 TB/s | 1.9 TB/s | 2.5 TB/s | Gigabytes per second |
| SPEC CPU 2017 Rate (Average) | 245 | 180 | 230 | Relative Score |
| SPEC CPU 2017 Int (Average) | 260 | 195 | 240 | Relative Score |
| HPCG | 1.15 PFLOPS | 0.8 PFLOPS | 1.0 PFLOPS | Floating-point operations per second |
| LAMMPS Molecular Dynamics | 150 ns/day | 90 ns/day | 130 ns/day | Nanoseconds per day (Large System) |
- Real-World Performance:**
- **Machine Learning Training:** We observed a 35-40% reduction in training time for large language models (LLMs) compared to the previous generation, primarily due to the increased core count and memory bandwidth. LLM Training Infrastructure relies heavily on this configuration.
- **Scientific Simulations (CFD):** Computational Fluid Dynamics (CFD) simulations showed a 25-30% speedup, attributed to the improved floating-point performance and NVLink interconnect. CFD Simulation Workflow is optimized for this hardware.
- **Financial Modeling:** Complex financial modeling tasks experienced a 20-25% performance improvement, benefiting from the increased core count and memory capacity. Financial Modeling Applications are frequently deployed here.
- **Genomics:** Genome sequencing and analysis saw a 30% increase in throughput, thanks to the enhanced memory bandwidth and processing power. Genomics Data Pipeline utilizes this configuration.
3. Recommended Use Cases
The Compute Node Allocation Policy configuration is ideal for workloads that demand significant processing power and memory capacity. Specific use cases include:
- **High-Performance Computing (HPC):** Scientific simulations, weather forecasting, climate modeling, and other computationally intensive research tasks. HPC Cluster Management is critical for these deployments.
- **Machine Learning (ML) and Artificial Intelligence (AI):** Training and inference of large models, deep learning, and data analytics. AI Infrastructure Best Practices should be followed.
- **Big Data Analytics:** Processing and analyzing massive datasets, data mining, and business intelligence. Big Data Analytics Stack is commonly used in conjunction with this configuration.
- **Financial Modeling:** Quantitative finance, risk management, and algorithmic trading. Financial Modeling Security Protocols are essential.
- **Genomics and Bioinformatics:** Genome sequencing, protein folding, and drug discovery. Bioinformatics Data Storage requirements are considered.
- **Rendering and Visualization:** High-resolution rendering, video processing, and scientific visualization. Rendering Farm Configuration utilizes this node type.
- **Virtual Desktop Infrastructure (VDI) for demanding applications:** Supporting resource-intensive applications like CAD/CAM and simulations within virtual environments. VDI Security Considerations apply.
4. Comparison with Similar Configurations
The Compute Node Allocation Policy configuration sits in a specific niche within our server offerings. Here's a comparison with two other common configurations:
| Configuration | CPU | RAM | Storage | Network | Primary Focus | Cost (Approx.) |
|---|---|---|---|---|---|---|
| Compute Node Allocation Policy (v3.0) | Dual Intel Xeon Platinum 8480+ | 512 GB DDR5 | 1 TB NVMe (Boot), Optional 2TB NVMe | Dual 200 Gbps Ethernet | High-Density Compute | $18,000 |
| Storage Optimized Server | Dual Intel Xeon Gold 6338 | 256 GB DDR4 | 16 x 4TB SAS HDD, RAID 6 | Dual 10 Gbps Ethernet | Large-Capacity Storage | $12,000 |
| Balanced Server | Dual Intel Xeon Silver 4310 | 128 GB DDR4 | 4 x 1TB NVMe SSD, RAID 1 | Dual 25 Gbps Ethernet | General-Purpose Workload | $8,000 |
- Key Differentiators:**
- **Compute Node Allocation Policy:** Prioritizes raw processing power, memory bandwidth, and high-speed interconnects. Sacrifices local storage for reliance on network storage.
- **Storage Optimized Server:** Focuses on large-capacity storage and data redundancy. Lower processing power and memory capacity. Ideal for archival and data warehousing.
- **Balanced Server:** Offers a compromise between compute, storage, and networking. Suitable for a wide range of general-purpose workloads.
5. Maintenance Considerations
Maintaining the Compute Node Allocation Policy configuration requires careful attention to cooling, power, and component lifespan.
- **Cooling:** These nodes generate significant heat. Proper airflow within the rack is crucial. Data Center Cooling Systems must be adequate. Liquid cooling is recommended for high-density deployments. Monitor CPU temperatures regularly using Server Monitoring Tools. Regular cleaning of heatsinks and fans is essential.
- **Power Requirements:** Each node requires approximately 800 Watts at full load. Ensure sufficient power capacity in the rack and at the PDU level. Power Usage Effectiveness (PUE) should be monitored to optimize energy efficiency.
- **Remote Management:** Utilize IPMI for remote monitoring, power cycling, and firmware updates. Secure IPMI access with strong passwords and IPMI Access Control Lists.
- **Firmware Updates:** Regularly update CPU, motherboard, and network card firmware to address security vulnerabilities and improve performance. Firmware Update Procedures are documented internally.
- **Component Lifespan:** SSDs have a limited write endurance. Monitor SSD health using SSD Monitoring Software. RAM modules can experience bit errors over time. Run regular memory tests. CPUs are generally very reliable, but should be monitored for temperature and performance degradation.
- **Network Configuration:** Maintain accurate network configuration and monitor network performance. Network Troubleshooting Guide is available.
- **Operating System:** Recommended OS is Red Hat Enterprise Linux (RHEL) or CentOS Stream. Operating System Hardening Guide should be followed.
- **Physical Security:** Ensure physical security of the servers to prevent unauthorized access. Data Center Security Protocols apply.
- **Regular Backups:** Although local storage is minimal, essential system configuration data should be backed up regularly. Backup and Recovery Procedures are documented.
- **Environmental Monitoring:** Continuous monitoring of temperature, humidity, and airflow within the server room is crucial for preventative maintenance. Environmental Monitoring Systems are deployed throughout the data center.
This document will be updated periodically to reflect changes in hardware specifications, performance characteristics, and best practices. Refer to the internal wiki for the latest version. For detailed troubleshooting guides and FAQs, please consult the Internal Support Portal. ```
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.* ⚠️