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Latest revision as of 18:18, 2 October 2025
Hardware Procurement Guidelines: High-Density Compute Platform (Model HPC-DCP-2024)
This document outlines the mandatory technical specifications, performance expectations, recommended deployment scenarios, comparative analysis, and long-term maintenance considerations for the High-Density Compute Platform, Model HPC-DCP-2024. This configuration is standardized for mission-critical, latency-sensitive enterprise workloads requiring significant parallel processing capabilities and high-speed local data access.
1. Hardware Specifications
The HPC-DCP-2024 is a 2U rackmount system designed for maximum core density and I/O throughput within a confined physical footprint. All components must adhere strictly to the specifications listed below to ensure compatibility with our established server lifecycle management protocols and warranty agreements.
1.1 Chassis and Platform
The base platform utilizes a proprietary motherboard designed for dual-socket operation with optimized power delivery networks (PDN) and high-speed interconnect fabric support.
| Component | Specification | Notes |
|---|---|---|
| Form Factor | 2U Rackmount (800mm depth recommended) | Optimized for high-density racks. |
| Motherboard | Dual Socket, Custom 4th Gen Server Platform | Support for 128 DIMM slots total. |
| Power Supplies (PSUs) | 2x 2200W (Platinum Efficiency, 80 Plus Platinum certified) | Redundant (N+1 configuration required). |
| Cooling Solution | High-Static Pressure Fan Array (6x Hot-Swappable Fans) | Optimized for 45°C ambient intake temperature. |
| Chassis Management | Integrated BMC/IPMI 2.0 (Redfish compliant) | Remote management interface is mandatory. |
| Expansion Slots | 6x PCIe Gen 5 x16 slots (full height/half length) | 2 dedicated slots for Network Interface Cards (NICs). |
1.2 Central Processing Units (CPUs)
The system mandates the use of dual-socket configurations utilizing the latest generation enterprise processors optimized for high core count and large L3 cache structures.
| Parameter | Minimum Requirement | Preferred Configuration (Model A) |
|---|---|---|
| Processor Family | Server Processor, 4th Generation Architecture | Latest Generation Enterprise Xeon Scalable or AMD EPYC equivalent (e.g., Genoa/Bergamo generation) |
| Core Count (Per Socket) | 64 Cores | 96+ Cores per socket |
| Thread Count (Total System) | 256 Threads (Minimum) | 384+ Threads (Preferred) |
| Base Clock Frequency | 2.8 GHz | 3.0 GHz sustained boost capability |
| L3 Cache (Total) | 384 MB | 512 MB+ |
| TDP (Thermal Design Power) | Max 350W per CPU | Thermal management must account for peak power draw. |
The choice of CPU directly impacts the memory subsystem bandwidth, as the memory controllers are integrated. Ensure CPU SKU selection maximizes the number of available PCIe lanes (minimum 128 lanes aggregate).
1.3 Memory Configuration
Memory capacity and speed are critical for data-intensive applications. The configuration prioritizes capacity and maximum channel utilization over raw DIMM speed in some scenarios.
| Parameter | Specification | Rationale |
|---|---|---|
| Type | DDR5 ECC RDIMM | Required for data integrity and bandwidth. |
| Speed (Data Rate) | Minimum 4800 MT/s; Preferred 5200 MT/s or higher | Must match the maximum supported speed by the selected CPU. |
| Total Capacity | 2 TB (Minimum Mandatory Deployment) | Required for in-memory database caching. |
| Configuration | 16 DIMMs per CPU (32 Total) | Ensures optimal interleaving across 8 memory channels per socket. |
| Maximum Capacity | 8 TB (Using 256GB DIMMs) | Future-proofing for virtualization density. |
| Memory Organization | Dual-Rank or Quad-Rank DIMMs preferred | Higher rank density aids performance in specific server workloads. |
2. Performance Characteristics
Performance validation for the HPC-DCP-2024 revolves around sustained throughput, low-latency interconnectivity, and I/O saturation resistance. Benchmark results provided below are based on standard validation suites executed under controlled thermal and power conditions (22°C ambient, 90% PSU utilization).
2.1 Synthetic Benchmarks
These metrics define the theoretical maximum processing capability of the configured hardware.
| Benchmark | Metric | Result (Units) | Target Threshold |
|---|---|---|---|
| SPECrate 2017 Integer | Peak Throughput Score | 18,500+ | 18,000 |
| SPECfp 2017 Floating Point | Peak Throughput Score | 22,000+ | 21,500 |
| Memory Bandwidth (L3 to DRAM) | Read/Write Aggregate | 7.5 TB/s Aggregate | 7.0 TB/s |
| PCIe Gen 5 Throughput | Bi-directional (All Lanes Active) | 128 GB/s (System Total) | 120 GB/s |
2.2 Storage Subsystem Performance
The configuration mandates NVMe-based storage utilizing the U.2 form factor for hot-swappability and high IOPs capability. The storage architecture must leverage the CPU's integrated PCIe lanes directly, bypassing the need for a dedicated SAS/SATA controller where possible.
The primary boot volume is a mirrored pair of low-latency NVMe drives, while the high-performance data pool utilizes a distributed RAID configuration (e.g., RAID 10 or erasure coding equivalent).
| Operation | Drive Configuration | Sequential Read (GB/s) | Random Read IOPS (4K QD32) | Latency (Microseconds) |
|---|---|---|---|---|
| Primary Data Pool (RAID 10 Equivalent) | 10x Drives | 45.0 | 12,500,000 | < 150 µs |
| Boot/OS Pool (Mirrored Pair) | 2x Drives | 10.0 | 1,500,000 | < 75 µs |
Sustained write performance must be validated against the endurance rating of the underlying NAND flash memory. Refer to NVMe endurance standards for acceptable write amplification factors (WAF).
2.3 Network Latency and Throughput
Network connectivity is specified as dual 200GbE ports utilizing Remote Direct Memory Access (RDMA) capabilities (RoCEv2 mandatory) for cluster communication.
The measured round-trip time (RTT) between two adjacent HPC-DCP-2024 nodes, measured via the InfiniBand/RDMA fabric, must not exceed $1.5$ microseconds ($\mu s$) under a 70% load profile. This low latency is crucial for distributed computing frameworks like MPI implementations.
3. Recommended Use Cases
The HPC-DCP-2024 platform's high core count, massive memory capacity, and significant I/O bandwidth make it exceptionally well-suited for specific, resource-intensive enterprise workloads. Deploying this configuration for generalized web serving or low-concurrency tasks represents a significant underutilization of capital investment.
3.1 High-Performance Computing (HPC)
This is the primary intended deployment environment.
- **Computational Fluid Dynamics (CFD):** The architecture supports the necessary floating-point throughput and fast inter-node communication required for complex meshing and simulation convergence.
- **Molecular Dynamics (MD) Simulations:** High core counts and large L3 caches facilitate the rapid calculation of inter-atomic forces across large datasets.
- **Weather Modeling and Climate Simulation:** Requires sustained performance across massive parallel tasks, benefiting directly from the high SPECfp scores.
3.2 Data Analytics and In-Memory Databases
The 2TB minimum RAM capacity positions this platform perfectly for in-memory data processing.
- **Large-Scale In-Memory Databases (IMDB):** Suitable for running multi-terabyte SAP HANA or similar columnar databases where latency is measured in single-digit milliseconds. The fast NVMe access serves as a rapid backup/restore target.
- **Real-Time Fraud Detection:** Low-latency processing of transaction streams requires the near-instantaneous lookup capabilities provided by the high-speed memory subsystem.
3.3 Machine Learning (ML) Training (CPU-Centric)
While GPU acceleration is often preferred for deep learning training, this platform excels in specific ML paradigms:
- **Gradient Boosting Machines (GBM) and Random Forests:** Algorithms like XGBoost and LightGBM are highly parallelizable across CPU cores and benefit significantly from the large DRAM capacity for holding feature vectors.
- **Data Preprocessing Pipelines:** The high aggregate I/O bandwidth (7.5 TB/s memory bandwidth + 45 GB/s storage bandwidth) allows for extremely rapid transformation and loading of training datasets, avoiding I/O bottlenecks common in GPU clusters.
3.4 Virtualization Density (High-Concurrency VDI)
For environments requiring high concurrency and predictable performance isolation, the platform offers excellent density.
- Deploying Virtual Desktop Infrastructure (VDI) where users are highly interactive (e.g., financial modeling workstations) benefits from the large number of dedicated physical cores available for VM allocation, minimizing context switching overhead.
4. Comparison with Similar Configurations
To justify the procurement cost of the HPC-DCP-2024, it must be benchmarked against standard enterprise workhorse configurations (e.g., 1U dense storage servers or dedicated GPU accelerators).
The following table compares the HPC-DCP-2024 (High-Density Compute) against two alternative standard server models: the Storage Density Server (SDS-1U) and the GPU Accelerator Node (GAN-4x).
| Feature | HPC-DCP-2024 (2U Compute) | SDS-1U (Standard Storage) | GAN-4x (GPU Compute) |
|---|---|---|---|
| Form Factor | 2U | 1U | 4U |
| Max CPU Cores (Total) | 192 (Dual 96-Core) | 64 (Dual 32-Core) | 128 (Dual 64-Core) |
| Max DRAM Capacity | 8 TB | 4 TB | 2 TB |
| Primary Storage Capacity (Raw) | ~46 TB (U.2 NVMe) | ~360 TB (SATA/SAS SSDs) | ~19 TB (Boot/Scratch) |
| Peak FP Performance (TFLOPS - CPU Only) | ~12.0 TFLOPS (FP64 sustained) | ~3.5 TFLOPS (FP64 sustained) | ~4.5 TFLOPS (FP64 sustained) |
| PCIe Bandwidth (Total) | 128 GB/s (Gen 5) | 64 GB/s (Gen 4) | 256 GB/s (Gen 5, dedicated to GPUs) |
| Ideal Workload | CPU-Bound Simulation, In-Memory DB | Bulk Storage, Logging, Backup Targets | Deep Learning Training, HPC (GPU optimized) |
4.1 Analysis of Comparison
The HPC-DCP-2024 occupies a distinct middle ground: it sacrifices the raw raw storage density of the SDS-1U but significantly outperforms it in computational density (cores/TB RAM per rack unit). Compared to the GAN-4x, the HPC-DCP-2024 offers substantially more host memory and higher CPU core counts, crucial for workloads that are memory-bound rather than strictly floating-point-bound (which is the domain of the GPU node).
For organizations standardizing on Software-Defined Storage layers, the HPC-DCP-2024 provides the necessary high-speed network interfaces and compute muscle to drive the storage fabric without being the primary storage target itself.
5. Maintenance Considerations
The high-density nature of the HPC-DCP-2024 introduces specific requirements for infrastructure management, particularly concerning power delivery, thermal dissipation, and component replacement procedures. Deviation from these guidelines voids the support contract.
5.1 Power Requirements
The system's peak power consumption, when all CPUs are under 100% load and all drives/NICs are saturated, can momentarily exceed 3.5 kW.
- **Rack Power Density:** Racks housing more than four HPC-DCP-2024 units must be rated for a minimum of 15 kW per rack unit. Rack Power Distribution Units (PDUs) must support 40A/208V circuits (or equivalent 3-phase connections).
- **PSU Redundancy:** The dual 2200W PSUs must be connected to independent power feeds (A-side and B-side) to ensure continuous operation during a single facility power failure.
5.2 Thermal Management and Airflow
The primary risk factor for this platform is thermal throttling due to inadequate cooling, which directly degrades the sustained performance metrics detailed in Section 2.
- **Ambient Temperature:** Intake air temperature must be maintained at or below $22^{\circ}C$ ($71.6^{\circ}F$) for optimal long-term operation. Operation above $28^{\circ}C$ is strictly prohibited without explicit engineering override.
- **Airflow Pattern:** Strict adherence to a front-to-back (cold aisle to hot aisle) airflow pattern is mandatory. Blanking panels must be installed in all unused rack spaces to prevent hot air recirculation into the server intake.
- **Fan Monitoring:** The BMC must be configured to report fan speed telemetry every 60 seconds. Any single fan reporting below 75% nominal RPM must trigger a P1 severity alert in the Data Center Infrastructure Management (DCIM) system.
5.3 Component Replacement Procedures
Due to the high component density, specific handling procedures are required, particularly for storage media and memory modules.
- 5.3.1 Hot-Swappable Components
The following components are designed for hot-swap replacement without system shutdown: 1. Power Supply Units (PSUs) 2. System Cooling Fans 3. NVMe SSDs (provided the underlying RAID/storage layer supports drive failure and rebuild without host interruption).
When replacing a PSU, the remaining active PSU must be confirmed to be operating at nominal voltage and carrying the full load for at least 30 minutes before the failed unit is removed.
- 5.3.2 Cold-Swap Components
CPUs and DIMMs require a full system shutdown and grounding procedures before replacement.
- **CPU Replacement:** Requires thermal paste application compliant with the manufacturer's specification (e.g., Arctic Silver 5 or equivalent high-conductivity paste) applied in a pea-sized dot pattern. Heatsink torque settings must be verified using a calibrated torque wrench set to the manufacturer's specification (typically between 1.5 Nm and 2.0 Nm). Refer to CPU Heatsink Installation Guide for detailed torque sequencing.
- **Memory Module Replacement:** All memory slots are populated identically. When replacing a DIMM, the replacement must match the capacity, rank configuration, and speed of the original module to maintain memory interleaving optimization. Failure to match specifications will result in performance degradation or system instability.
5.4 Firmware and Driver Management
To maintain the performance guarantees outlined in Section 2, the system firmware must be kept current.
- **BIOS/UEFI Level:** Must be maintained within one major revision of the current validated baseline (currently UEFI v3.10.x). Updates must be staged via the BMC interface only.
- **NIC Firmware:** Network Interface Card (NIC) firmware must match the version certified by the network vendor for RDMA operations. Outdated NIC firmware is the leading cause of unexpected cluster disconnects. Consult the Network Hardware Compatibility List before any firmware update.
The standard operational procedure mandates quarterly reviews of the Server Hardware Configuration Database to ensure all deployed units conform to the HPC-DCP-2024 baseline specification.
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.* ⚠️