Skip to content

README.md

Latest commit

 

History

History
52 lines (35 loc) · 1.77 KB

README.md

File metadata and controls

52 lines (35 loc) · 1.77 KB
Raw

mkl_umath ASV Benchmarks

Performance benchmarks for mkl_umath using Airspeed Velocity (ASV).

The npbench/ suite uses kernels from npbench to measure end-to-end impact of MKL ufunc acceleration in realistic workloads.

Coverage

File Ufuncs Dtypes Sizes/Presets
micro/bench_micro.py 24 unary (exp, log, sin, cos, sqrt, cbrt, etc.) + arctan2, power float32, float64 10k, 100k, 1M
npbench/bench_softmax.py exp, max, sum float32 M (32x8x256x256), L (64x16x448x448)
npbench/bench_arc_distance.py sin, cos, arctan2, sqrt float64 M (1M), L (10M)
npbench/bench_go_fast.py tanh float64 M (6k x 6k), L (20k x 20k)
npbench/bench_mandelbrot.py abs, multiply, add complex128 M (250/500), L (833/1000)

Running Benchmarks

Prerequisites:

pip install asv psutil

Run benchmarks against the current commit:

asv run --python=same --quick HEAD^!

Compare two commits:

asv continuous --python=same HEAD~1 HEAD

View results in a browser:

asv publish
asv preview

Threading

Set MKL_NUM_THREADS to control the thread count used by MKL:

MKL_NUM_THREADS=8 asv run --python=same --quick HEAD^!

If MKL_NUM_THREADS is not set, __init__.py applies a default: 4 threads when the machine has 4 or more physical cores, or 1 (single-threaded) otherwise. This keeps results comparable across CI machines in the shared pool regardless of their total core count. Physical cores are detected via psutil.cpu_count(logical=False) (hyperthreads excluded per MKL recommendation).