Benchmarks for Intel Distribution for Python.
# Since you want to benchmark numpy/scipy, manually install the one you want
# instead of letting pip install one
# cython is needed to build native extensions in ibench_native
conda install scipy cython scikit-learn
pip install -v --upgrade .If icc and cython are available during the build, they will be used
to build native benchmarks. To specify a different compiler, specify one
in the environment variable CXX.
# basic command
python -m ibench run -b all --size large --runs 3 --file all.out- To run one or multiple benchmarks, pass the
-b BENCHMARKS...option. Benchmarks can be specified individually, or in predefined groups (e.g.nativecontains all native benchmarks) - To specify the problem size, use the
--sizeoption. This selects from a list of predefined problem sizes.
ibench supports both JSON (default) and CSV output with collection of
environment information. When using CSV format, ibench will prefix each
line of environment information with a comment character (default @).
Use the following arguments to the run subparser to configure its output:
-f,--format FORMAT- use the specified format (jsonorcsv)--no-get-env-info- don't collect environment or machine info--env-info-prefix- prefix character to use before environment info
- To run python benchmarks:
python -m ibench run -b linalg --size large --runs 3 --file linalg.out - To run native benchmarks*:
python -m ibench run -b native --size large --runs 3 --file native.out
* Currently, native benchmarks are only available for det, dot, lu, and inv.
- To run python benchmarks:
python -m ibench run -b sklearn --size large --runs 3 --file sklearn.out - For comparable python and native benchmarks, see scikit-learn_bench.
- To run python benchmarks:
python -m ibench run -b fft --size large --runs 3 --file fft.out - For comparable python and native benchmarks, see fft_benchmark.
- To run python benchmarks:
python -m ibench run -b rng --size large --runs 3 --file rng.out - For comparable python and native benchmarks, see optimizations_bench.
- To run python Black-Scholes benchmark:
python -m ibench run -b blacksch --size large --runs 3 --file blacksch.out - For comparable python and native benchmarks, see BlackScholes_bench.
- See optimizations_bench.
python -m ibench --help
python -m ibench run --help