Additional speed benchmarks¶
All methods are benchmarked using their default parameters, with the following exceptions:
basex(var) and daun(var) mean “variable regularization”, that is changing the regularization parameter for each transformed image.
direct_C and direct_Python correspond to the “direct” method using its C (Cython) and Python backends respectively.
linbasex and rbasex show whole-image (n × n) transforms, while all other methods show half-image (n rows, (n + 1)/2 columns) transforms.
rbasex(None) means no output-image creation (only the transformed radial distributions).
Intel i7-9700 (Linux)¶
- CPU:
Intel Core i7-9700 (8 cores, 8 threads; 3.0 GHz base, 4.7 GHz max)
- RAM:
32 GB DDR4-2666
- OS:
- Libraries:
NumPy 1.18.1
SciPy 1.4.1
MKL 2020
Results¶
Intel i7-6700 (Linux)¶
- CPU:
Intel Core i7-6700 (4 cores, 8 threads; 3.4 GHz base, 4.0 GHz max)
- RAM:
32 GB DDR4-2133
- OS:
- Libraries:
NumPy 1.18.1
SciPy 1.4.1
MKL 2019 Update 5
Results¶
AMD Ryzen 5 5600G (Linux)¶
- CPU:
AMD Ryzen 5 5600G (6 cores, 12 threads; 3.9 GHz base, 4.4 GHz max)
- RAM:
32 GB DDR4-3200
- OS:
- Libraries:
NumPy 1.24.2
SciPy 1.10.1
OpenBLAS 0.3.21
Results¶
AMD Ryzen 5 5600G (Windows)¶
- CPU:
AMD Ryzen 5 5600G (6 cores, 12 threads; 3.9 GHz base, 4.4 GHz max)
- RAM:
32 GB DDR4-3200
- OS:
- Libraries:
NumPy 1.26.0
SciPy 1.11.2
OpenBLAS 0.3.23
Results¶
Raspberry Pi 4B (Linux)¶
- CPU:
Broadcom BCM2711 (4 cores; 1.5 GHz)
- RAM:
4 GB LPDDR4-3200
- OS:
- Libraries:
NumPy 1.16.2
SciPy 1.1.0
Reference BLAS 3.8.0