The sources for various benchmarks are available at the download page. Some Python benchmarks are explained in more detail at the quickstart page.
This benchmark runs a modified version of the escape time algorithm for drawing a Mandelbrot set. It was chosen simply because it contains a large number of multiplications, additions and subtractions.
The contestants are libmpdec, DecNumber and IntelRDFPMathLib.
These are the data types of the three libraries. The arbitrary precision data types and the IEEE types are compatible:
libmpdec
DecNumber
IntelRDFPMathLib
arbitrary precision
mpd
decNumber
-
IEEE 16 digits
-
decDouble
bid64
IEEE 34 digits
-
DecQuad
bid128
mpd
decDouble
decQuad
decNumber
bid64
bid128
16 digits
4.94s
6.10s
-
7.70s
2.40s
-
34 digits
6.78s
-
11.40s
13.10s
-
8.07s
9 digits
4.68s
-
-
5.69s
-
-
19 digits
4.84s
-
-
9.44s
-
-
38 digits
6.67s
-
-
16.02s
-
-
3.16GHz Core 2 Duo, 10000000 iterations
mpd
decDouble
decQuad
decNumber
bid64
bid128
16 digits
4.57s
3.74s
-
5.45s
2.37s
-
34 digits
7.88s
-
7.89s
9.09s
-
9.15s
9 digits
3.47s
-
-
3.74s
-
-
19 digits
5.32s
-
-
6.55s
-
-
38 digits
9.79s
-
-
10.84s
-
-
Athlon 3700+, 3000000 iterations
This benchmark compares the Python modules cdecimal, decimal.py and and Java’s BigDecimal class. Python’s native binary float type and gmpy’s binary mpf type are included for a better overview.
The benchmark runs 10000 iterations of calculating pi to various precisions.
The telco benchmark was devised by Mike Cowlishaw as a way of measuring decimal performance in a real world telecom application.
decimal
cdecimal
telco
172.19s
5.68s
3.16GHz Core 2 Duo
This benchmark compares libmpdec, apfloat and mpfr. Both libmpdec and apfloat use a power of ten base, so it is no surprise that mpfr leads every benchmark with respect to pure calculation time [3] . However, when the result of a large calculation needs to be converted to decimal, libmpdec and apfloat are faster. To show this, the multiplication benchmarks have been split into calculation + conversion time. All other benchmarks are calculation time only.
func/prec/iter
libmpdec
apfloat
mpfr
mul/5000/1
0.00s + 0.00s
0.00s + 0.00s
0.00s + 0.00s
mul/100000/1
0.02s + 0.00s
0.01s + 0.01s
0.01s + 0.02s
mul/1000000/1
0.14s + 0.03s
0.19s + 0.03s
0.04s + 0.75s
mul/10000000/1
2.32s + 0.26s
2.96s + 0.35s
0.64s + 17.44s
mul/100000000/1
20.59s + 2.65s
32.59s + 3.40s
8.56s + 328.59s
div/5000/1
0.01s
0.00s
0.00s
div/100000/1
0.04s
0.05s
0.02s
div/1000000/1
0.62s
0.51s
0.16s
div/10000000/1
7.59s
7.91s
2.85s
div/100000000/1
75.98s
88.64s
49.14s
sqrt/9/100000
0.18s
-
0.01s
sqrt/19/100000
0.26s
-
0.01s
sqrt/38/100000
0.36s
0.72s
0.02s
sqrt/1000000/1
1.59s [4]
0.82s
0.13s
sqrt/10000000/1
22.50s
12.67s
2.31s
invroot/1000000/1
0.62s
0.64s
0.11s
invroot/10000000/1
8.44s
9.79s
1.54s
exp/9/100000
0.73s
-
0.45s
exp/19/100000
1.21s
-
0.65s
exp/38/100000
2.16s
70.96s [5]
1.71s
exp/5000/1
1.04s
0.62s
0.01s
ln/9/100000
1.27s
-
0.25s
ln/19/100000
2.00s
-
0.28s
ln/38/100000
3.30s
20.90s [5]
0.48s
ln/5000/1
2.25s
0.20s
0.01s
3.16GHz Core 2 Duo
This benchmark compares the Python modules cdecimal and gmpy. Python’s native integer type is included for a better overview. The benchmark calculates the factorial of huge numbers. The timings are split into pure calculation time and conversion time to decimal string.
Python integers
cdecimal
cdecimal-nt [6]
gmpy
100000!
1.65s + 78.66s
0.48s + 0.01s
0.45s + 0.01s
0.18s + 0.18s
1000000!
78.54s + >2h
7.84s + 0.07s
7.56s + 0.07s
2.76s + 6.52s
10000000!
-
99.17s + 0.87s
96.08s + 0.87s
46.57s + 165.80s
3.16GHz Core 2 Duo
Python integers
cdecimal
cdecimal-nt [6]
gmpy
100000!
2.72s + 119.81s
1.73s + 0.01s
1.71s + 0.01s
0.36s + 0.34s
1000000!
103.85s + >2h
29.99s + 0.12s
29.69s + 0.12s
5.47s + 11.30s
10000000!
-
381.89s + 1.48s
380.51s + 1.49s
86.46s + 291.41s
Athlon 3700+