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Add support for CPU-only benchmarking.

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Fixes #95.

CPU-only mode is enabled by setting the `is_cpu_only` property while
defining a benchmark, e.g. `NVBENCH_BENCH(foo).set_is_cpu_only(true)`.

An optional `nvbench::exec_tag::no_gpu` hint can also be passed to
`state.exec` to avoid instantiating GPU benchmarking backends. Note that
a CUDA compiler and CUDA runtime are always required, even if all benchmarks
in a translation unit are CPU-only.

Similarly, a new `nvbench::exec_tag::gpu` hint can be used to avoid
compiling CPU-only backends for GPU benchmarks.
This commit is contained in:
Allison Piper
2025-04-04 20:03:49 +00:00
parent 1efed5f8e1
commit a6df59a9b5
16 changed files with 780 additions and 89 deletions
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9
README.md
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@@ -25,6 +25,9 @@ features:
* 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](docs/benchmarks.md#cpu-only-benchmarks)
* Measures the host-side execution time of a non-GPU benchmark.
* Not suitable for microbenchmarking.
# Supported Compilers and Tools
@@ -65,6 +68,7 @@ This repository provides a number of [examples](examples/) that demonstrate
various NVBench features and usecases:
- [Runtime and compile-time parameter sweeps](examples/axes.cu)
- [CPU-only benchmarking](examples/cpu_only.cu)
- [Enums and compile-time-constant-integral parameter axes](examples/enums.cu)
- [Reporting item/sec and byte/sec throughput statistics](examples/throughput.cu)
- [Skipping benchmark configurations](examples/skip.cu)
@@ -171,6 +175,7 @@ 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,
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.