Command Line Completion

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Overview

The "Command Line Completion" (CLC) server configuration is a specialized system designed for computationally intensive tasks primarily driven by command-line tools and scripting. It prioritizes CPU performance, high RAM capacity, and fast storage I/O, while accepting slightly reduced graphical processing power. This configuration excels in areas like data analysis, software compilation, scientific computing, and automated testing. This document details the hardware specifications, performance characteristics, recommended use cases, comparisons, and maintenance considerations for the CLC configuration.

1. Hardware Specifications

This configuration is built around a balance of high core count, memory bandwidth, and NVMe storage performance. All components are chosen for reliability and sustained performance under heavy load.

Component Specification Details
CPU AMD EPYC 7763 (64-Core) 2.45 GHz base clock, 3.5 GHz boost clock, 128 threads, 256MB L3 Cache, TDP 280W. Utilizes the Zen 3 architecture. CPU Architecture
Motherboard Supermicro H12SSL-NT Supports dual AMD EPYC 7003 Series processors, 16 x DIMM slots, PCIe 4.0 support, dual 10GbE LAN ports, IPMI 2.0 remote management. Server Motherboards
RAM 256GB DDR4-3200 ECC Registered 8 x 32GB modules. Registered ECC memory ensures data integrity and stability. Speed is optimized for AMD EPYC processors. DDR4 Memory
Storage (OS) 500GB NVMe PCIe 4.0 SSD Samsung 980 Pro. For fast boot times and responsive operating system performance. NVMe SSDs
Storage (Data) 4 x 4TB NVMe PCIe 4.0 SSD (RAID 0) Western Digital SN850. Configured in RAID 0 for maximum throughput. Total usable capacity: 16TB. Data redundancy is *not* a priority in this configuration, favoring performance. RAID Configurations
GPU NVIDIA T400 (Low Profile) 768 CUDA Cores, 16GB GDDR6 Memory. Primarily for display and basic graphics tasks; not a primary focus. GPU Technology
Power Supply 1200W 80+ Platinum Redundant power supply for increased reliability. Provides ample headroom for all components. Power Supplies
Network Interface Dual 10GbE Intel X710-DA4. Provides high-bandwidth network connectivity. Supports Link Aggregation for improved throughput and redundancy. Networking Technologies
Cooling High-Performance Air Cooling Noctua NH-U14S TR4-SP3. Designed for AMD EPYC processors. Multiple fans for optimal heat dissipation. Server Cooling
Chassis Supermicro 4U Rackmount Chassis Provides ample space for components and excellent airflow. Server Chassis

Operating System: Ubuntu Server 22.04 LTS (64-bit) is the standard OS. Custom kernel builds may be employed for further optimization. Linux Distributions


2. Performance Characteristics

The CLC configuration is designed for maximum performance in CPU-bound and I/O-bound workloads. Benchmarks are used to quantify its capabilities.

CPU Performance:

  • SPECint®2017 Rate Base 2.0: 280 (estimated, based on EPYC 7763 results) - This benchmark measures integer processing performance.
  • SPECfp®2017 Rate Base 2.0: 150 (estimated, based on EPYC 7763 results) - This benchmark measures floating-point processing performance.
  • Compilation Time (Linux Kernel): Approximately 25 minutes - A representative workload for software development.

Storage Performance:

  • Sequential Read Speed (RAID 0): 12 GB/s (measured using fio) - Demonstrates the high throughput of the NVMe RAID 0 array.
  • Sequential Write Speed (RAID 0): 10 GB/s (measured using fio) - High write speeds are crucial for data logging and processing.
  • Random Read IOPS (4KB): 800,000 IOPS (measured using fio) - Indicates excellent performance for small file access.
  • Random Write IOPS (4KB): 700,000 IOPS (measured using fio)

Network Performance:

  • 10GbE Throughput: 9.4 Gbps (measured using iperf3) - Demonstrates the high network bandwidth.

Real-World Performance Examples:

  • Bioinformatics Data Analysis (Genome Sequencing): CLC configuration reduces processing time for typical genome alignment tasks by approximately 40% compared to a comparable system with traditional SATA SSDs. Bioinformatics Applications
  • High-Frequency Trading (Backtesting): Allows for rapid backtesting of trading strategies with historical data.
  • Machine Learning Model Training (Small to Medium Datasets): Provides sufficient processing power and memory for training machine learning models on datasets up to 1TB in size. Machine Learning Workloads
  • Large-Scale Software Compilation (e.g., Chromium, LLVM): Significantly reduces compilation times, improving developer productivity.

3. Recommended Use Cases

The CLC configuration is best suited for the following a


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.* ⚠️

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