The Hardware Sampling (hws) library can be used to track hardware performance like clock frequency, memory usage, temperatures, or power draw. It currently supports CPUs as well as GPUs from NVIDIA, AMD, and Intel.
General dependencies:
- a C++20 capable compiler supporting
std::format
(tested with GCC 14.1.0) - Pybind11 > v2.13.1 if Python bindings are enabled (automatically build during the CMake configuration if it couldn't be found using the respective
find_package
call)
Dependencies based on the hardware to sample:
- if a CPU should be targeted: at least one of
turbostat
(may require root privileges),lscpu
, orfree
and thesubprocess.h
library (automatically build during the CMake configuration if it couldn't be found using the respectivefind_package
call) - if an NVIDIA GPU should be targeted: NVIDIA's Management Library
NVML
- if an AMD GPU should be targeted: AMD's ROCm SMI library
rocm_smi_lib
- if an Intel GPU should be targeted: Intel's
Level Zero library
To download the hardware sampling use:
git clone git@github.com:SC-SGS/hardware_sampling.git
cd hardware_sampling
Building the library can be done using the normal CMake approach:
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release [optional_options] ..
cmake --build . -j
The [optional_options]
can be one or multiple of:
HWS_ENABLE_ERROR_CHECKS=ON|OFF
(default:OFF
): enable sanity checks during hardware sampling, may be problematic with smaller sample intervalsHWS_SAMPLING_INTERVAL=100ms
(default:100ms
): set the sampling interval in millisecondsHWS_ENABLE_PYTHON_BINDINGS=ON|OFF
(default:ON
): enable Python bindings
The library supports the install
target:
cmake --install . --prefix "/home/myuser/installdir"
Afterward, the necessary exports should be performed:
export CMAKE_PREFIX_PATH=${CMAKE_INSTALL_PREFIX}/share/hardware_sampling/cmake:${CMAKE_PREFIX_PATH}
export LD_LIBRARY_PATH=${CMAKE_INSTALL_PREFIX}/lib:${LD_LIBRARY_PATH}
export CPLUS_INCLUDE_PATH=${CMAKE_INSTALL_PREFIX}/include:${CPLUS_INCLUDE_PATH}
export PYTHONPATH=${CMAKE_INSTALL_PREFIX}/lib:${PYTHONPATH}
import HardwareSampling
import numpy as np
import matplotlib.pyplot as plt
import datetime
sampler = HardwareSampling.CpuHardwareSampler()
# could also be, e.g.,
# sampler = HardwareSampling.GpuNvidiaHardwareSampler()
sampler.start()
sampler.add_event("init")
A = np.random.rand(2**14, 2**14)
B = np.random.rand(2**14, 2**14)
sampler.add_event("matmul")
C = A @ B
sampler.stop()
sampler.dump_yaml("track.yaml")
# plot the results
time_points = sampler.time_points()
relative_time_points = [(t - time_points[0]) / datetime.timedelta(milliseconds=1) for t in time_points]
plt.plot(relative_time_points, sampler.clock_samples().get_average_frequency(), label="average")
plt.plot(relative_time_points, sampler.clock_samples().get_average_non_idle_frequency(), label="average non-idle")
axes = plt.gcf().axes[0]
x_bounds = axes.get_xlim()
for event in sampler.get_events()[1:-1]:
tp = (event.time_point - time_points[0]) / datetime.timedelta(milliseconds=1)
axes.axvline(x=tp, color='r')
axes.annotate(text=event.name, xy=(((tp - x_bounds[0]) / (x_bounds[1] - x_bounds[0])), 1.025), xycoords='axes fraction', rotation=270)
plt.xlabel("runtime [ms]")
plt.ylabel("clock frequency [MHz]")
plt.legend()
plt.show()
The hws library is distributed under the MIT license.