Performance Testing Procedures
Performance Testing Procedures: High-Density Compute Node (HDCN-9000 Series)
This document details the rigorous performance testing procedures, baseline hardware specifications, and operational characteristics of the High-Density Compute Node, model HDCN-9000 series. This configuration is specifically engineered for high-throughput, latency-sensitive workloads, making thorough pre-deployment validation crucial.
1. Hardware Specifications
The HDCN-9000 series represents a significant advancement in rack-density computing, balancing power efficiency with raw computational throughput. All specifications listed below pertain to the validated reference configuration used for the established performance baseline.
1.1 Core Processing Units (CPUs)
The system utilizes dual-socket processing architecture, leveraging the latest generation of high-core-count microprocessors designed for server environments.
Parameter | Specification | Notes |
---|---|---|
Processor Model | Intel Xeon Scalable 4th Gen (Sapphire Rapids) - Platinum Series | Optimized for AVX-512 and AMX acceleration. |
Quantity | 2 | Dual-socket motherboard configuration. |
Core Count (Per CPU) | 60 Physical Cores | Total 120 physical cores. |
Thread Count (Per CPU) | 120 Logical Threads (Hyper-Threading Enabled) | 240 Logical Threads total. |
Base Clock Frequency | 2.4 GHz | Guaranteed minimum operating frequency under sustained load. |
Max Turbo Frequency (Single Core) | Up to 3.8 GHz | Dependent on thermal envelope and workload profile. |
L3 Cache (Per CPU) | 112.5 MB (Total 225 MB) | Inclusive cache architecture. |
TDP (Thermal Design Power) | 350W (Per CPU) | Requires robust cooling infrastructure (See Section 5). |
Instruction Sets Supported | AVX-512, VNNI, BMI2, AES-NI, AMX | Critical for AI/ML and high-performance computing (HPC) workloads. |
1.2 System Memory (RAM) Configuration
Memory capacity and speed are primary bottlenecks in many high-performance workloads. The HDCN-9000 is equipped with maximum supported high-density, high-speed DDR5 modules.
Parameter | Specification | Notes |
---|---|---|
Memory Type | DDR5 ECC RDIMM | Error-Correcting Code Registered DIMMs. |
Total Capacity | 2048 GB (2 TB) | Configured for maximum density. |
Module Size | 128 GB per DIMM | Using 16 x 128GB modules. |
Channel Configuration | 8 Channels per CPU (16 Total) | Fully populated across all available memory channels for maximum bandwidth. |
Memory Speed | 4800 MHz (MT/s) | Achieved using XMP profiles validated by the OEM. |
Memory Bandwidth (Theoretical Peak) | ~768 GB/s | Calculated based on 16 channels @ 4800 MT/s. |
Link to Memory Subsystem Optimization details advanced memory configuration techniques.
1.3 Storage Subsystem
The storage architecture prioritizes low latency and high IOPS for metadata operations and rapid dataset access. NVMe Gen 4 is the standard.
Component | Specification | Use Case |
---|---|---|
Boot Drive (OS/Hypervisor) | 2 x 480GB SATA SSD (RAID 1) | Redundancy for host operating system. |
Primary Data Storage (Scratch Space) | 8 x 3.84 TB NVMe U.2 PCIe 4.0 SSDs | Configured in a high-performance software RAID 0 array for maximum throughput. |
Storage Controller | Broadcom MegaRAID SAS 9580-8i (Pass-through mode for NVMe) | Primarily handles drive management and reporting; I/O bypasses the RAID controller for best latency. |
Total Usable High-Speed Storage | Approximately 30.7 TB (Raw) | Performance heavily dependent on RAID stripe size. |
1.4 Network Interface Controllers (NICs)
High-speed, low-latency networking is mandatory for cluster communications and data ingestion.
Interface | Specification | Role |
---|---|---|
Primary Interconnect (Data Plane) | 2 x 200 GbE Mellanox ConnectX-6 | RDMA (RoCEv2) capable for cluster messaging and storage access. |
Management Network (OOB) | 1 x 10 GbE RJ-45 | Dedicated for IPMI/BMC access and remote management. |
PCIe Interface Used | PCIe 5.0 x16 (Per NIC) | Ensures zero saturation of the NIC bandwidth. |
Link to PCIe Lane Allocation Strategy describes how lanes are distributed among CPUs and peripherals.
1.5 Motherboard and Chassis
The system is housed in a 2U rackmount chassis designed for high airflow environments.
- **Form Factor:** 2U Rackmount
- **Chipset:** Dual Socket C741 Server Chipset
- **PCIe Slots:** 8 x PCIe 5.0 slots (Configured with 4 x16 slots populated)
- **Power Supplies:** 2 x 2400W Redundant (N+1 configuration) Platinum Rated PSUs.
- **Cooling:** High-static pressure, front-to-back airflow optimization with liquid-assist options available for extreme overclocking scenarios (not standard testing). Link to Thermal Management Systems
2. Performance Characteristics
Performance testing involved a standardized suite of industry benchmarks designed to stress different subsystems independently and concurrently. All tests were executed after a minimum 48-hour burn-in period to ensure thermal stabilization and memory training completion.
2.1 CPU Benchmarking: Compute Intensity
Tests focused on floating-point operations (FP64) and integer throughput, crucial for scientific simulations and complex data processing.
- 2.1.1 Linpack Benchmark (HPL)
Linpack measures the speed at which the system can solve a dense system of linear equations, directly correlating to theoretical peak FLOPS.
- **Configuration:** 120 physical cores active, AVX-512 enabled, Block Size (NB) set to 256.
- **Result:** 9.85 TFLOPS (Double Precision, FP64)
- **Efficiency:** Achieved 78% of theoretical peak FLOPS, indicating excellent CPU-to-CPU interconnect latency and effective memory bandwidth utilization.
- 2.1.2 SPEC CPU 2017 Integer Rate
This benchmark tests sustained integer performance across various real-world application proxies.
- **Result (Rate):** 12,550 (Multi-threaded integer score)
- **Analysis:** The high score confirms the efficiency of the 60-core CPUs in handling branching logic and complex instruction sets typical in database query processing. Link to SPEC Benchmark Interpretation
2.2 Memory Subsystem Performance
Memory bandwidth is critical for data-intensive applications that frequently traverse the CPU cache hierarchy.
- 2.2.1 STREAM Benchmark (Double Precision Copy)
Measures sustained memory bandwidth utilization.
- **Configuration:** All 16 DIMMs active, running at 4800 MHz.
- **Result (Peak Bandwidth):** 712.4 GB/s
- **Analysis:** This result is approximately 92.7% of the theoretical peak bandwidth (768 GB/s), demonstrating minimal overhead from the dual-socket topology and effective memory controller tuning. Link to Memory Bandwidth Scaling provides comparison points for lower channel counts.
2.3 Storage IOPS and Latency
Measured using FIO (Flexible I/O Tester) targeting the 8-drive NVMe array in RAID 0 configuration.
Workload Type | Block Size | Queue Depth (QD) | IOPS (Read/Write) | Average Latency (µs) |
---|---|---|---|---|
Sequential Read | 128 KB | 128 | 16.8 Million / 15.1 Million | 45 µs |
Random Read (4K) | 4 KB | 256 | 1.2 Million / 950,000 | 210 µs |
Random Write (4K) | 4 KB | 256 | 880,000 / 720,000 | 285 µs |
The latency figures under high queue depth validate the use of PCIe 5.0 direct-attached NVMe drives, bypassing substantial controller overhead.
2.4 Network Throughput Testing
Testing utilized Iperf3 across the 200 GbE interconnects, running simultaneously to measure aggregate system throughput.
- **Test:** Bi-directional TCP throughput aggregation (2 streams per NIC).
- **Result:** 395 Gbps aggregate throughput (CPU utilization < 15%).
- **RDMA Test:** Using an optimized MPI benchmark between two identical HDCN-9000 nodes, the achieved effective bandwidth was 19.5 GB/s (156 Gbps) with an average one-way latency of 1.1 microseconds. This confirms the low latency profile necessary for distributed memory access patterns. Link to RDMA Performance Metrics
3. Recommended Use Cases
The HDCN-9000 configuration, characterized by its massive core count, high memory capacity, and extremely fast interconnects, is optimized for workloads that are either compute-bound or require rapid shuffling of large in-memory datasets.
3.1 High-Performance Computing (HPC) Simulation
Workloads involving complex fluid dynamics (CFD), molecular modeling, and finite element analysis (FEA) benefit directly from the 120 physical cores and the low-latency RDMA fabric.
- **Key Requirement Met:** High FP64 throughput (TFLOPS) and low inter-node communication latency.
3.2 Large-Scale Relational and In-Memory Databases
For OLTP systems requiring sub-millisecond transaction times or in-memory analytical platforms (e.g., SAP HANA), the 2TB of high-speed DDR5 memory is the primary advantage.
- **Key Requirement Met:** Massive memory footprint to hold entire working sets and high IOPS for persistent logging/metadata. Link to Database Server Tuning Guides
3.3 Artificial Intelligence and Machine Learning Training (Medium Scale)
While GPU density is often paramount for deep learning inference, the HDCN-9000 excels in the pre-processing, feature engineering, and training phases of *medium-sized* models that benefit from CPU acceleration (e.g., graph neural networks or specific NLP models utilizing AMX/AVX-512).
- **Key Requirement Met:** AMX acceleration for efficient integer matrix multiplication during model preparation phases.
3.4 Complex Data Warehousing and ETL
Extract, Transform, Load (ETL) pipelines that involve heavy transformation logic benefit from the high core count, allowing parallel processing of data streams before final storage.
- **Key Requirement Met:** High sustained integer throughput and excellent I/O capabilities to feed the processing cores efficiently.
4. Comparison with Similar Configurations
To contextualize the HDCN-9000’s performance profile, we compare it against two common alternative server configurations: a High-Frequency Workstation Node (HFWN) and a GPU-Accelerated Inference Node (GAIN-200).
4.1 Comparative Configuration Overview
Feature | HDCN-9000 (Reference) | HFWN-5000 (High Frequency) | GAIN-200 (GPU Focused) |
---|---|---|---|
CPU Cores (Total) | 120 Physical | 48 Physical (Higher Clock) | 32 Physical (Lower TDP) |
Max RAM Capacity | 2048 GB (DDR5-4800) | 1024 GB (DDR5-5600) | 512 GB (DDR5-4800) |
Peak FP64 TFLOPS (CPU Only) | 9.85 TFLOPS | 6.1 TFLOPS | 2.5 TFLOPS |
Network Interconnect | 2x 200 GbE (RDMA) | 2x 100 GbE (TCP/IP) | 4x 100 GbE (RoCEv2) |
Primary Storage Bus | PCIe 5.0 NVMe | PCIe 4.0 U.2 SSDs | PCIe 5.0 NVMe (Shared w/ GPU) |
Link to Server Tiering Strategy discusses when to select a node based on these performance profiles.
4.2 Performance Trade-off Analysis
The fundamental trade-off is between raw core density/memory capacity (HDCN-9000) versus specialized accelerator performance (GAIN-200) or absolute single-thread clock speed (HFWN-5000).
Workload Type | HDCN-9000 | HFWN-5000 | GAIN-200 |
---|---|---|---|
Pure HPC (FP64, Multi-threaded) | 100% | 62% | 45% (CPU contribution only) |
In-Memory Transaction Processing | 100% (Capacity/Bandwidth) | 85% (Slightly faster clock helps) | 60% (Limited RAM) |
Compiler Optimization Time (Integer) | 95% | 100% (Slight clock advantage) | 75% |
Network Saturation Testing (Aggregate) | 100% (395 Gbps) | 50% (197 Gbps) | 100% (Requires careful teaming) |
- Conclusion on Comparison:** The HDCN-9000 is the superior choice when the workload scales effectively across 100+ cores and requires access to over 1TB of fast memory simultaneously. The GAIN-200 configuration, while not detailed here, would dramatically outperform the HDCN-9000 in deep learning inference tasks due to dedicated GPU resources (e.g., H100/B200). Link to GPU vs. CPU Acceleration
5. Maintenance Considerations
Deploying a high-density, high-power system like the HDCN-9000 requires specialized attention to power delivery, cooling, and firmware management to maintain the validated performance baseline.
5.1 Power Requirements and Consumption
The system’s TDP rating, combined with the high-speed networking and storage, mandates a robust power infrastructure.
- **Peak Measured Power Draw (Stress Test):** 3150 Watts (Measured at the PDU input under 100% load across all subsystems).
- **Recommended PSU Configuration:** Dual 2400W Platinum PSUs (N+1 redundancy). This provides 4800W capacity, allowing for 1650W headroom for transient spikes and future expansion (e.g., adding a PCIe accelerator card).
- **PDU Requirements:** Must be rated for high-density 30A circuits (or equivalent 208V/240V input) to safely operate two such nodes per standard 1U rack space without tripping breakers. Link to Data Center Power Planning
5.2 Thermal Management and Airflow
The 700W total CPU TDP is the primary thermal challenge.
- **Required Airflow:** Minimum 120 CFM (Cubic Feet per Minute) per node, delivered at 28°C ambient inlet temperature.
- **Cooling Strategy:** Must utilize high-static pressure fans. Standard low-velocity cooling schemes common in storage arrays are insufficient. Hot aisle containment is strongly recommended to maintain inlet temperatures below 30°C under peak operational load.
- **Monitoring:** IPMI sensors must be polled every 60 seconds to track CPU package temperatures. Any sustained temperature exceeding 92°C under load necessitates immediate thermal investigation (e.g., thermal paste degradation or fan failure). Link to Server Cooling Standards
5.3 Firmware and BIOS Management
Maintaining the validated performance requires strict control over firmware versions, as microcode updates can significantly alter performance characteristics, particularly in areas like power delivery and cache management.
- **BIOS Settings Critical for Performance:**
* **Memory Frequency:** Must be explicitly set to 4800 MHz (or higher if stability allows), not left on "Auto." * **Power Limits (PL1/PL2):** Must be configured to "Package Power Limits Retain" or set to maximum defined by the CPU datasheet (e.g., ICCMax tuning). Disabling aggressive power capping is mandatory for performance testing validation. * **NUMA Balancing:** Must be configured to "Prefer Socket" or disabled entirely if the application is explicitly NUMA-aware. Link to BIOS Performance Tuning
- **Firmware Checklist:**
1. BIOS/UEFI: Must be the latest stable version (e.g., v3.10.x). 2. BMC/IPMI: Latest version to ensure accurate sensor reporting. 3. HBA/RAID Controller: Latest vendor firmware (e.g., Broadcom 07.00.xx.xxx). 4. NIC Firmware: Latest version supporting RoCEv2 optimization features. Link to Network Driver Best Practices
5.4 Capacity Planning and Scaling
The density of the HDCN-9000 necessitates careful consideration when scaling out.
- **Network Saturation:** When deploying more than four HDCN-9000 nodes, the 200 GbE interconnects will become the primary bottleneck for all-to-all communication patterns. In such scenarios, migration to 400 GbE infrastructure or utilizing Infiniband fabrics is required. Link to Interconnect Scaling Limits
- **Rack Density vs. Performance:** Due to the high power draw, administrators must limit the number of HDCN-9000 units per rack cabinet to prevent exceeding the rack's thermal dissipation limits (typically 10-15 kW per rack). Link to Rack Density Planning
The procedures outlined in this document ensure that the HDCN-9000 configuration delivers its documented performance characteristics consistently across diverse testing environments. Strict adherence to the hardware specifications and maintenance considerations is paramount for mission-critical deployments. Link to System Validation Protocols
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.* ⚠️