NumPy

Performance

Recommendation

The recommended generator for general use is PCG64. It is statistically high quality, full-featured, and fast on most platforms, but somewhat slow when compiled for 32-bit processes.

Philox is fairly slow, but its statistical properties have very high quality, and it is easy to get 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 transformation rather than the Ziggurat generator. 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 is used the same 32-bit generator as RandomState but produces random values using Generator.

MT19937

PCG64

Philox

SFC64

RandomState

32-bit Unsigned Ints

3.2

2.7

4.9

2.7

3.2

64-bit Unsigned Ints

5.6

3.7

6.3

2.9

5.7

Uniforms

7.3

4.1

8.1

3.1

7.3

Normals

13.1

10.2

13.5

7.8

34.6

Exponentials

7.9

5.4

8.5

4.1

40.3

Gammas

34.8

28.0

34.7

25.1

58.1

Binomials

25.0

21.4

26.1

19.5

25.2

Laplaces

45.1

40.7

45.5

38.1

45.6

Poissons

67.6

52.4

69.2

46.4

78.1

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

Philox

SFC64

32-bit Unsigned Ints

101

121

67

121

64-bit Unsigned Ints

102

156

91

199

Uniforms

100

179

90

235

Normals

263

338

257

443

Exponentials

507

752

474

985

Gammas

167

207

167

231

Binomials

101

118

96

129

Laplaces

101

112

100

120

Poissons

116

149

113

168

Overall

144

192

132

225

Note

All timings were taken using Linux on an i5-3570 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

Philox

SFC64

32-bit Unsigned Int

100

119.8

67.7

120.2

64-bit Unsigned Int

100

152.9

90.8

213.3

Uniforms

100

179.0

87.0

232.0

Normals

100

128.5

99.2

167.8

Exponentials

100

148.3

93.0

189.3

Overall

100

144.3

86.8

180.0

64-bit Windows

The relative performance on 64-bit Linux and 64-bit Windows is broadly similar.

Distribution

MT19937

PCG64

Philox

SFC64

32-bit Unsigned Int

100

129.1

35.0

135.0

64-bit Unsigned Int

100

146.9

35.7

176.5

Uniforms

100

165.0

37.0

192.0

Normals

100

128.5

48.5

158.0

Exponentials

100

151.6

39.0

172.8

Overall

100

143.6

38.7

165.7

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

Philox

SFC64

32-bit Unsigned Int

100

30.5

21.1

77.9

64-bit Unsigned Int

100

26.3

19.2

97.0

Uniforms

100

28.0

23.0

106.0

Normals

100

40.1

31.3

112.6

Exponentials

100

33.7

26.3

109.8

Overall

100

31.4

23.8

99.8

Note

Linux timings used Ubuntu 18.04 and GCC 7.4. Windows timings were made on Windows 10 using Microsoft C/C++ Optimizing Compiler Version 19 (Visual Studio 2015). All timings were produced on an i5-3570 processor.