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Showing content from https://github.com/NVIDIA/nvbench below:

NVIDIA/nvbench: CUDA Kernel Benchmarking Library

This project is a work-in-progress. Everything is subject to change.

NVBench is a C++17 library designed to simplify CUDA kernel benchmarking. It features:

• Parameter sweeps: a powerful and flexible "axis" system explores a kernel's configuration space. Parameters may be dynamic numbers/strings or static types.
• Runtime customization: A rich command-line interface allows redefinition of parameter axes, CUDA device selection, locking GPU clocks (Volta+), changing output formats, and more.
• Throughput calculations: Compute and report:
  • Item throughput (elements/second)
  • Global memory bandwidth usage (bytes/second and per-device %-of-peak-bw)
• Multiple output formats: Currently supports markdown (default) and CSV output.
• Manual timer mode: (optional) Explicitly start/stop timing in a benchmark implementation.
• Multiple measurement types:
  • Cold Measurements:
    • Each sample runs the benchmark once with a clean device L2 cache.
    • GPU and CPU times are reported.
  • Batch Measurements:
    • Executes the benchmark multiple times back-to-back and records total time.
    • Reports the average execution time (total time / number of executions).
  • CPU-only Measurements
    • Measures the host-side execution time of a non-GPU benchmark.
    • Not suitable for microbenchmarking.

Check out this talk for an overview of the challenges inherent to CUDA kernel benchmarking and how NVBench solves them for you!

• CMake > 3.30.4
• CUDA Toolkit + nvcc: 12.0 and above
• g++: 7 -> 14
• clang++: 14 -> 19
• Headers are tested with C++17 -> C++20.

A basic kernel benchmark can be created with just a few lines of CUDA C++:

void my_benchmark(nvbench::state& state) {
  state.exec([](nvbench::launch& launch) {
    my_kernel<<<num_blocks, 256, 0, launch.get_stream()>>>();
  });
}
NVBENCH_BENCH(my_benchmark);

See Benchmarks for information on customizing benchmarks and implementing parameter sweeps.

Each benchmark executable produced by NVBench provides a rich set of command-line options for configuring benchmark execution at runtime. See the CLI overview and CLI axis specification for more information.

This repository provides a number of examples that demonstrate various NVBench features and usecases:

• Runtime and compile-time parameter sweeps
• CPU-only benchmarking
• Enums and compile-time-constant-integral parameter axes
• Reporting item/sec and byte/sec throughput statistics
• Skipping benchmark configurations
• Benchmarking on a specific stream
• Adding / hiding columns (summaries) in markdown output
• Benchmarks that sync CUDA devices: nvbench::exec_tag::sync
• Manual timing: nvbench::exec_tag::timer

To build the examples:

mkdir -p build
cd build
cmake -DNVBench_ENABLE_EXAMPLES=ON -DCMAKE_CUDA_ARCHITECTURES=70 .. && make

Be sure to set CMAKE_CUDA_ARCHITECTURE based on the GPU you are running on.

Examples are built by default into build/bin and are prefixed with nvbench.example.

# Devices

## [0] `Quadro GV100`
* SM Version: 700 (PTX Version: 700)
* Number of SMs: 80
* SM Default Clock Rate: 1627 MHz
* Global Memory: 32163 MiB Free / 32508 MiB Total
* Global Memory Bus Peak: 870 GiB/sec (4096-bit DDR @850MHz)
* Max Shared Memory: 96 KiB/SM, 48 KiB/Block
* L2 Cache Size: 6144 KiB
* Maximum Active Blocks: 32/SM
* Maximum Active Threads: 2048/SM, 1024/Block
* Available Registers: 65536/SM, 65536/Block
* ECC Enabled: No

# Log

Run:  throughput_bench [Device=0]
Warn: Current measurement timed out (15.00s) while over noise threshold (1.26% > 0.50%)
Pass: Cold: 0.262392ms GPU, 0.267860ms CPU, 7.19s total GPU, 27393x
Pass: Batch: 0.261963ms GPU, 7.18s total GPU, 27394x

# Benchmark Results

## throughput_bench

### [0] Quadro GV100

| NumElements |  DataSize  | Samples |  CPU Time  | Noise |  GPU Time  | Noise | Elem/s  | GlobalMem BW  | BWPeak | Batch GPU  | Batch  |
|-------------|------------|---------|------------|-------|------------|-------|---------|---------------|--------|------------|--------|
|    16777216 | 64.000 MiB |  27393x | 267.860 us | 1.25% | 262.392 us | 1.26% | 63.940G | 476.387 GiB/s | 58.77% | 261.963 us | 27394x |

To get started using NVBench with your own kernels, consider trying out the NVBench Demo Project.

nvbench_demo provides a simple CMake project that uses NVBench to build an example benchmark. It's a great way to experiment with the library without a lot of investment.

Contributions are welcome!

For current issues, see the issue board. Issues labeled with are good for first time contributors.

To build nvbench tests:

mkdir -p build
cd build
cmake -DNVBench_ENABLE_TESTING=ON .. && make

Tests are built by default into build/bin and prefixed with nvbench.test.

To run all tests:

or

NVBench is released under the Apache 2.0 License with LLVM exceptions. See LICENSE.

NVBench will measure the CPU and CUDA GPU execution time of a single host-side critical region per benchmark. It is intended for regression testing and parameter tuning of individual kernels. For in-depth analysis of end-to-end performance of multiple applications, the NVIDIA Nsight tools are more appropriate.

NVBench is focused on evaluating the performance of CUDA kernels. It also provides CPU-only benchmarking facilities intended for non-trivial CPU workloads, but is not optimized for CPU microbenchmarks. This may change in the future, but for now, consider using Google Benchmark for high resolution CPU benchmarks.

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