Open skirpichev opened 1 year ago
I tried three tests.
$ py311 -m timeit -r10 -s 'from gmpy2 import fac as f, mpz as z;from pickle import dumps as d' -s 'a=f(z(1000))' 'd(a)'
50000 loops, best of 10: 4.92 usec per loop
$ py311 -m timeit -r10 -s 'from math import factorial as f;from pickle import dumps as d' -s 'a=f(1000)' 'd(a)'
200000 loops, best of 10: 1.3 usec per loop
$ py311 -m timeit -r10 -s 'from gmpy2 import fac as f, mpz as z, to_binary as d' -s 'a=f(z(1000))' 'd(a)'
100000 loops, best of 10: 3.23 usec per loop
The third test directly calls gmpy2.to_binary
which creates the pickled data. to_binary
calls mpz_export
for all the data manipulation.
The Python code inside gmpy2 is ancient. I would accept removing it since we no longer need to support old versions of Python and mpz
is now a real type and not a factory function.
I don't know if it is worth trying to improve on mpz_export/mpz_import
? The code in mpz_pylong.c
may help.
BTW, the mpz.to_bytes() (or to_binary) is systematically slower than int.to_bytes(). Here is a simple benchmark:
Code:
# a.py
from gmpy2 import mpz, to_binary
import sys
import random
import time
import platform
import matplotlib.pyplot as plt
int_time = []
mpz_time = []
#bin_time = []
times = 15
nbits = int(float(sys.argv[1]))
random.seed(1)
r = sorted(random.sample(range(1, nbits), 500))
for k in r:
ns = [random.randint(2**k//2, 2**k) for _ in range(times)]
nl = [(n, (n.bit_length() + 7)//8 + 2) for n in ns]
start = time.perf_counter_ns()
for n, l in nl:
n.to_bytes(l)
int_time.append((time.perf_counter_ns() - start) / times)
nl = [(mpz(n), l) for n, l in nl]
start = time.perf_counter_ns()
for n, l in nl:
n.to_bytes(l)
mpz_time.append((time.perf_counter_ns() - start) / times)
# start = time.perf_counter_ns()
# for n, l in nl:
# to_binary(n)
# bin_time.append((time.perf_counter_ns() - start) / times)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r, int_time, label='int.to_bytes()')
ax.plot(r, mpz_time, label='mpz.to_bytes()')
#ax.plot(r, bin_time, label='to_binary(mpz)')
ax.set_yscale('log')
ax.set_xlabel('bits')
ax.set_ylabel('time (ns)')
ax.legend()
plt.title('Benchmark for to_bytes with ' + str(nbits) + ' bits.')
plt.show()
fig.savefig('to_bytes-'+str(nbits) + '.png')
UPD:
$ python
Python 3.11.3+ (heads/3.11:9fbb614c4e, Apr 29 2023, 14:18:05) [GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import gmpy2, sys
>>> gmpy2.mp_limbsize()
64
>>> sys.int_info
sys.int_info(bits_per_digit=30, sizeof_digit=4, default_max_str_digits=4300, str_digits_check_threshold=640)
An example (~4-5x difference):
I wonder if using the pure python function for pickling support could be a significant part of this speed loss. (Inspired by https://github.com/mpmath/mpmath/pull/667)