Performance

Recommendation

The recommended generator for general use is PCG64 or its upgraded variant PCG64DXSM for heavily-parallel use cases. They are statistically high quality, full-featured, and fast on most platforms, but somewhat slow when compiled for 32-bit processes. See Upgrading PCG64 with PCG64DXSM for details on when heavy parallelism would indicate using PCG64DXSM.

Philox is fairly slow, but its statistical properties have very high quality, and it is easy to get an assuredly-independent stream by using unique keys. If that is the style you wish to use for parallel streams, or you are porting from another system that uses that style, then Philox is your choice.

SFC64 is statistically high quality and very fast. However, it lacks jumpability. If you are not using that capability and want lots of speed, even on 32-bit processes, this is your choice.

MT19937 fails some statistical tests and is not especially fast compared to modern PRNGs. For these reasons, we mostly do not recommend using it on its own, only through the legacy RandomState for reproducing old results. That said, it has a very long history as a default in many systems.

Timings

The timings below are the time in ns to produce 1 random value from a specific distribution. The original MT19937 generator is much slower since it requires 2 32-bit values to equal the output of the faster generators.

Integer performance has a similar ordering.

The pattern is similar for other, more complex generators. The normal performance of the legacy RandomState generator is much lower than the other since it uses the Box-Muller transform rather than the Ziggurat method. The performance gap for Exponentials is also large due to the cost of computing the log function to invert the CDF. The column labeled MT19973 uses the same 32-bit generator as RandomState but produces random variates using Generator.

	MT19937	PCG64	PCG64DXSM	Philox	SFC64	RandomState
32-bit Unsigned Ints	3.3	1.9	2.0	3.3	1.8	3.1
64-bit Unsigned Ints	5.6	3.2	2.9	4.9	2.5	5.5
Uniforms	5.9	3.1	2.9	5.0	2.6	6.0
Normals	13.9	10.8	10.5	12.0	8.3	56.8
Exponentials	9.1	6.0	5.8	8.1	5.4	63.9
Gammas	37.2	30.8	28.9	34.0	27.5	77.0
Binomials	21.3	17.4	17.6	19.3	15.6	21.4
Laplaces	73.2	72.3	76.1	73.0	72.3	82.5
Poissons	111.7	103.4	100.5	109.4	90.7	115.2

The next table presents the performance in percentage relative to values generated by the legacy generator, RandomState(MT19937()). The overall performance was computed using a geometric mean.

	MT19937	PCG64	PCG64DXSM	Philox	SFC64
32-bit Unsigned Ints	96	162	160	96	175
64-bit Unsigned Ints	97	171	188	113	218
Uniforms	102	192	206	121	233
Normals	409	526	541	471	684
Exponentials	701	1071	1101	784	1179
Gammas	207	250	266	227	281
Binomials	100	123	122	111	138
Laplaces	113	114	108	113	114
Poissons	103	111	115	105	127
Overall	159	219	225	174	251

Note

All timings were taken using Linux on an AMD Ryzen 9 3900X processor.

Performance on different operating systems

Performance differs across platforms due to compiler and hardware availability (e.g., register width) differences. The default bit generator has been chosen to perform well on 64-bit platforms. Performance on 32-bit operating systems is very different.

The values reported are normalized relative to the speed of MT19937 in each table. A value of 100 indicates that the performance matches the MT19937. Higher values indicate improved performance. These values cannot be compared across tables.

64-bit Linux

Distribution	MT19937	PCG64	PCG64DXSM	Philox	SFC64
32-bit Unsigned Ints	100	168	166	100	182
64-bit Unsigned Ints	100	176	193	116	224
Uniforms	100	188	202	118	228
Normals	100	128	132	115	167
Exponentials	100	152	157	111	168
Overall	100	161	168	112	192

64-bit Windows

The relative performance on 64-bit Linux and 64-bit Windows is broadly similar with the notable exception of the Philox generator.

Distribution	MT19937	PCG64	PCG64DXSM	Philox	SFC64
32-bit Unsigned Ints	100	155	131	29	150
64-bit Unsigned Ints	100	157	143	25	154
Uniforms	100	151	144	24	155
Normals	100	129	128	37	150
Exponentials	100	150	145	28	159
Overall	100	148	138	28	154

32-bit Windows

The performance of 64-bit generators on 32-bit Windows is much lower than on 64-bit operating systems due to register width. MT19937, the generator that has been in NumPy since 2005, operates on 32-bit integers.

Distribution	MT19937	PCG64	PCG64DXSM	Philox	SFC64
32-bit Unsigned Ints	100	24	34	14	57
64-bit Unsigned Ints	100	21	32	14	74
Uniforms	100	21	34	16	73
Normals	100	36	57	28	101
Exponentials	100	28	44	20	88
Overall	100	25	39	18	77

Note

Linux timings used Ubuntu 20.04 and GCC 9.3.0. Windows timings were made on Windows 10 using Microsoft C/C++ Optimizing Compiler Version 19 (Visual Studio 2019). All timings were produced on an AMD Ryzen 9 3900X processor.