System Benchmark Tool

A unified, containerized benchmarking suite designed for Linux servers. This tool performs stress tests and performance measurements for CPU, GPU (CUDA), Disk I/O, and Network speed in a safe, reproducible environment.

🚀 Features

• CPU Benchmark: Performs dense float32 Matrix Multiplication to calculate GFLOPS.
• GPU Benchmark (Smart):
  • Auto-detects NVIDIA GPUs.
  • Safety First: Checks available VRAM before running. If the GPU is busy (e.g., training a model), it scales down the workload to prevent Out-Of-Memory (OOM) crashes.
  • Measures Compute TFLOPS (approx).
• Disk Benchmark:
  • Uses fio for industry-standard accuracy.
  • Uses --direct=1 to bypass OS RAM caching for true disk speed.
  • Tests Sequential Read and Write (1GB).
• Network Benchmark:
  • Upload/Download/Ping latency using speedtest-cli.
• Dual Logging: Results are printed to the console and saved to log file benchmark_report.log.

📋 Prerequisites

1. Docker installed. Check with docker --version.
2. NVIDIA Container Toolkit installed (Required for GPU tests to work inside Docker).
3. Internet Access (Required for the Network Speedtest).

📂 Project Structure

sys-bench/  
├── docker-compose.yml    # Docker orchestration file  
├── Dockerfile            # Docker image  
├── run.sh                # Entry point script (wraps Python execution)  
├── requirements.txt      # Python dependencies  
└── src/                  # Source code  
    ├── __init__.py       # Versioning
    ├── main.py  
    └── benchmarks/       # Individual test modules

⚡ How to Run

Method 1: Pulling from Docker Hub (Recommended)

The fastest way to run the tool without cloning the source code is to pull from our docker hub. If you want to run any previous version, you can specify the tag (e.g., 1.0.0). The latest tag will always point to the most recent build version.

docker run --rm --gpus all -v $(pwd):/data vatsal1306/sys-bench:latest

Method 2: Using Docker Compose

Recommended if you have cloned the repository and want to modify the code. It handles volume mounting and GPU resource flags automatically.

1. Build and Run Docker:

docker-compose up --build

2. View Results:
   • Live output will appear in your terminal.
   • A report file will be generated in your current directory: ./benchmark_report.log

Method 3: Manual Docker Run

If you prefer to build the image manually:

1. Build the Image:

docker build -t sys-bench .

2. Run the Container:
   • -v $(pwd):/data: Maps your current directory to the container to save logs and test disk speed.
   • --gpus all: Passes NVIDIA GPUs to the container.

docker run --rm --gpus all -v $(pwd):/data sys-bench

📊 Interpreting Results

Metric	Component	Description	Context
GFLOPS	CPU	Giga Floating Point Operations Per Second.	Higher is Better. Measures raw math speed.
TFLOPS	GPU	Tera Floating Point Operations Per Second.	Higher is Better. Measures CUDA compute throughput. Note: Score depends on available VRAM.
MB/s	Disk	Megabytes per Second.	Higher is Better. Speed of writing/reading from physical disk.
Mbps	Network	Megabits per Second.	Higher is Better. Internet connection bandwidth.

⚠️ Troubleshooting

1. GPU not detected

• Ensure you have the NVIDIA drivers installed on the host.
• Ensure nvidia-container-toolkit is installed.
• Verify the container runtime includes --gpus all.

2. Disk Benchmark Fails or Permissions Error

• The tool tries to write a temporary file to the mounted volume (/data inside container, which is $(pwd) on host).
• Ensure your current directory is writable by the user running Docker.
• If using Docker Compose, you may need to map the user ID by uncommenting the user: line in docker-compose.yml.

3. Network Test Fails

• Check if the server has outbound internet access.
• Some corporate firewalls could block speedtest endpoints.

📈 Future Enhancements

• Add support for AMD GPUs and Metal (macOS).

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📩 Contact

For questions or support, please open an issue on GitHub or contact the maintainer at vatsal1399@gmail.com