Overview
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).
Supported Compilers and Tools
- CMake > 2.23.1
- CUDA Toolkit + nvcc: 11.1 -> 12.4
- g++: 7 -> 12
- clang++: 9 -> 18
- cl.exe: 2019 -> 2022 (19.29, 29.39)
- Headers are tested with C++17 -> C++20.
Getting Started
Minimal Benchmark
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.
Command Line Interface
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.
Examples
This repository provides a number of examples that demonstrate
various NVBench features and usecases:
Building Examples
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
.
Example output from `nvbench.example.throughput`
```
# 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 |
```
Demo Project
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.
Contributing
Contributions are welcome!
For current issues, see the issue board. Issues labeled with
are good for first time contributors.
Tests
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:
make test
or
ctest
License
NVBench is released under the Apache 2.0 License with LLVM exceptions.
See LICENSE.
Scope and Related Projects
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 and is not
optimized for CPU microbenchmarks. This may change in the future, but for now,
consider using Google Benchmark for high resolution CPU benchmarks.