CUBench

The Definitive Open-Source GPU Benchmarking Utility

A comprehensive CUDA-based tool for evaluating GPU performance across a variety of kernel configurations, memory access patterns and occupancy scenarios.

Features

• Measures execution time over multiple trials for statistical significance
• Tests a range of thread block sizes and grid configurations
• Reports per-kernel occupancy and register utilisation
• Customisable benchmark parameters via BenchmarkConfig
• Outputs human-readable summaries to the console

Requirements

• CUDA Toolkit (version 11.0 or later)
• NVCC compiler
• C++17-compatible standard library
• 1 or more CUDA-capable GPUs

Building

1. Ensure the CUDA environment variables are set (e.g. CUDA_HOME).

2. Compile with NVCC:

   nvcc -std=c++17 main.cu -o cubench -lcusparse -lcufft --extended-lambda -rdc=true

3. (Optional) Add optimisation flags:

   nvcc -O3 -std=c++17 main.cu -o cubench -lcusparse -lcufft --extended-lambda -rdc=true

Usage

Run the benchmark executable. By default, it uses device 0 and the settings in BenchmarkConfig:

./cubench

Sample output:

=== Rasterisation Benchmark ===
Triangles: 10000
Resolution: 1920x1080
Time: 158.24 ms (6.32 FPS)
Triangles/sec: 0.06 M
Pixels/sec: 13.10 M

Custom Configuration

Modify the BenchmarkConfig struct in main.cu to tweak:

• Number of trials per test
• Input sizes for occupancy and memory benchmarks
• Minimum and maximum block sizes

Recompile after changes.

Notes

I have a lot of other projects i need to create and maintain. expect delayed bugfixes / features / responses.

Planned features

NUMA & NVLink Bandwidth
GPU power efficiency (GFLOPs/W under light and heavy sustained loads to measure clock drop-off)

Licence

This project is licensed under the Apache License 2.0. See LICENSE for details.

Author

Stevenson Parker

Created: 24 July 2025