mkl_fft ASV Benchmarks

Performance benchmarks for mkl_fft using Airspeed Velocity (ASV).

Coverage

File	API	Transforms	Dtypes	Sizes/Shapes
bench_fft1d.py	mkl_fft	fft, ifft, rfft, irfft	float32, float64, complex64, complex128	power-of-two and non-power-of-two
bench_fftnd.py	mkl_fft	fft2, ifft2, rfft2, irfft2, fftn, ifftn, rfftn, irfftn	float32, float64, complex64, complex128	square and non-square/non-cubic
bench_interfaces.py	mkl_fft.interfaces.{numpy_fft, scipy_fft}	All exported functions; selected by a module parameter. Hermitian 2-D/N-D (hfft2, hfftn) are scipy-only.	float32, float64, complex64, complex128	power-of-two and cubic
bench_memory.py	mkl_fft	Peak RSS for 1-D, 2-D, and 3-D transforms	float32, float64, complex128	power-of-two

Threading

Set MKL_NUM_THREADS in the environment before running ASV 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 are excluded per MKL recommendation.

Notes on Measurement

DFTI descriptor warmup

MKL creates a DFTI descriptor on the first FFT call for a given (size, dtype, strides) combination and reuses it on subsequent calls. To avoid charging that one-time cost to the first measured iteration, each benchmark's setup performs an explicit warmup call after preparing the input array. ASV's default warmup_time (0.1s) already amortizes this for sub-millisecond transforms, but the explicit warmup makes the intent visible.

Running Benchmarks

Prerequisites:

pip install ".[benchmark]"

Run benchmarks against the current environment:

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