# A Python program to run speed and evaluate the performance of MPIR # routines. # # Copyright (c) 2009, Brian Gladman, Worcester, UK. # # This file is part of the MPIR Library. The MPIR Library is free # software; you can redistribute it and/or modify it under the terms # of the GNU Lesser General Public License version 2.1 as published # by the Free Software Foundation. # # The MPIR Library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. You should have # received a copy of the GNU Lesser General Public License along # with the MPIR Library; see the file COPYING.LIB. If not, write to # the Free Software Foundation, Inc., 51Franklin Street, Fifth Floor, # Boston, MA 02110-1301, USA. from __future__ import print_function import sys import os import shutil import string import copy import code import math import platform from subprocess import Popen, PIPE, STDOUT if sys.platform.startswith('win'): dir = '.\\x64\\release\\' else : dir = './' ll = [ '-c -s 10(10)1000 noop', '-c -s 10(10)1000 noop_wxs', '-c -s 10(10)1000 noop_wxys', '-c -s 10(10)1000 mpn_add_n', '-c -s 10(10)1000 mpn_sub_n', '-c -s 10(10)1000 mpn_addadd_n', '-c -s 10(10)1000 mpn_subadd_n', '-c -s 10(10)1000 mpn_addsub_n', '-c -s 10(10)1000 mpn_karaadd', '-c -s 10(10)1000 mpn_karasub', '-c -s 10(10)1000 mpn_addmul_1.3333', '-c -s 10(10)1000 mpn_submul_1.3333', '-c -s 10(10)1000 mpn_submul_2', '-c -s 10(10)1000 mpn_mul_1.3333', '-c -s 10(10)1000 mpn_mul_1_inplace.3333', '-c -s 10(10)1000 mpn_mul_2', '-c -s 10(10)1000 mpn_divrem_euclidean_qr_1.3333', '-c -s 10(10)1000 mpn_divrem_euclidean_qr_2', '-c -s 10(10)1000 mpn_divrem_euclidean_r_1.3333', '-c -s 10(10)1000 mpn_divrem_hensel_qr_1.3333', '-c -s 10(10)1000 mpn_divrem_hensel_qr_1_1.3333', '-c -s 10(10)1000 mpn_divrem_hensel_qr_1_2.3333', '-c -s 10(10)1000 mpn_divrem_hensel_r_1.3333', '-c -s 10(10)1000 mpn_rsh_divrem_hensel_qr_1.3333', '-c -s 10(10)1000 mpn_rsh_divrem_hensel_qr_1_1.3333', '-c -s 10(10)1000 mpn_rsh_divrem_hensel_qr_1_2.3333', '-c -s 10(10)1000 mpn_divrem_hensel_rsh_qr_1.3333', '-c -s 10(10)1000 mpn_divrem_1.3333', '-c -s 10(10)1000 mpn_divrem_1f.3333', '-c -s 10(10)1000 mpn_mod_1.3333', '-c -s 10(10)1000 mpn_mod_1_1', '-c -s 10(10)1000 mpn_mod_1_2', '-c -s 10(10)1000 mpn_mod_1_3', '-c -s 10(10)1000 mpn_mod_1_k.3', '-c -s 10(10)1000 mpn_preinv_divrem_1.3333', '-c -s 10(10)1000 mpn_preinv_divrem_1f.3333', '-c -s 10(10)1000 mpn_preinv_mod_1.3333', '-c -s 10(10)1000 mpn_add_err1_n', '-c -s 10(10)1000 mpn_sub_err1_n', '-c -s 10(10)1000 mpn_inv_divappr_q', '-c -s 10(10)1000 mpn_inv_div_qr', '-c -s 10(10)1000 mpn_dc_divappr_q', '-c -s 10(10)1000 mpn_dc_div_qr_n', '-c -s 10(10)1000 mpn_divrem_1_inv.3333', '-c -s 10(10)1000 mpn_divrem_1f_div.3333', '-c -s 10(10)1000 mpn_divrem_1f_inv.3333', '-c -s 10(10)1000 mpn_mod_1_div.3333', '-c -s 10(10)1000 mpn_mod_1_inv.3333', '-c -s 10(10)1000 mpn_divrem_2', '-c -s 10(10)1000 mpn_divrem_2_div', '-c -s 10(10)1000 mpn_divrem_2_inv', '-c -s 10(10)1000 mpn_divexact_1.3333', '-c -s 10(10)1000 mpn_divexact_by3', '-c -s 10(10)1000 mpn_divexact_byff', '-c -s 10(10)1000 mpn_divexact_byfobm1.3333', '-c -s 10(10)1000 mpn_modexact_1_odd.333', '-c -s 10(10)1000 mpn_modexact_1c_odd.333', '-c -s 10(10)1000 mpn_mod_34lsub1', '-c -s 10(10)1000 mpn_dc_tdiv_qr', '-c -s 10(10)1000 mpn_lshift.33', '-c -s 10(10)1000 mpn_rshift.33', '-c -s 10(10)1000 mpn_lshift1', '-c -s 10(10)1000 mpn_rshift1', '-c -s 10(10)1000 mpn_duuble', '-c -s 10(10)1000 mpn_half', '-c -s 10(10)1000 mpn_lshift2', '-c -s 10(10)1000 mpn_rshift2', '-c -s 10(10)1000 mpn_and_n', '-c -s 10(10)1000 mpn_andn_n', '-c -s 10(10)1000 mpn_nand_n', '-c -s 10(10)1000 mpn_ior_n', '-c -s 10(10)1000 mpn_iorn_n', '-c -s 10(10)1000 mpn_nior_n', '-c -s 10(10)1000 mpn_xor_n', '-c -s 10(10)1000 mpn_xnor_n', '-c -s 10(10)1000 mpn_com_n', '-c -s 10(10)1000 mpn_not', '-c -s 10(10)1000 mpn_popcount', '-c -s 10(10)1000 mpn_hamdist', '-c -s 10(10)1000 MPN_ZERO', '-c -s 10(10)1000 MPN_COPY', '-c -s 10(10)1000 MPN_COPY_INCR', '-c -s 10(10)1000 MPN_COPY_DECR', '-c -s 10(10)1000 count_leading_zeros', '-c -s 10(10)1000 gmp_allocate_free', '-c -s 10(10)1000 malloc_realloc_free', '-c -s 10(10)1000 gmp_allocate_reallocate_free', '-c -s 10(10)1000 malloc_free', '-c -s 10(10)1000 mpn_umul_ppmm', '-c -s 10(10)1000 mpz_add', '-c -s 10(10)1000 mpz_init_realloc_clear', '-c -s 10(10)1000 mpz_init_clear', '-c -s 10(10)1000 udiv_qrnnd', '-c -s 10(10)1000 udiv_qrnnd_c', '-c -s 10(10)1000 udiv_qrnnd_preinv1', '-c -s 10(10)1000 udiv_qrnnd_preinv2', '-c -s 10(10)1000 umul_ppmm', '-c -s 10(10)1000 mpn_popcount', '-c -s 10(10)1000 mpn_hamdist', ] lq = [ '-c -s 10(10)1000 mpn_dc_divrem_n', '-c -s 10(10)1000 mpn_dc_divrem_sb', '-c -s 10(10)1000 mpn_dc_tdiv_qr', '-c -s 10(10)1000 mpn_kara_mul_n', '-c -s 10(10)1000 mpn_kara_sqr_n', '-c -s 10(10)1000 mpn_mul_basecase', '-c -s 1000(500)10000 -t 10 mpn_mul_fft_full', '-c -s 10(10)1000 mpn_mul_n', '-c -s 10(10)1000 mpn_sqr_basecase', '-c -s 10(10)1000 mpn_sqr_n', '-c -s 50(10)1000 mpn_toom3_mul_n', '-c -s 50(10)1000 mpn_toom3_sqr_n', '-c -s 1(5)100 mpz_powm', ] # run an executable and return its error return value and any output def run_exe(exe, args, inp) : al = {'stdin' : PIPE, 'stdout' : PIPE, 'stderr' : STDOUT } if sys.platform.startswith('win') : al['creationflags'] = 0x08000000 p = Popen([exe] + args.split(' '), **al) res = p.communicate(inp.encode())[0].decode() ret = p.poll() return (ret, res) # output a matrix implemented as a dictionary def mout(m, n) : for r in range(n) : print('\n{0:3d}'.format(r), end='') for c in range(n) : print('{0:18.4f}'.format(m[(r,c)]) , end='') print # output a vector def vout(v) : print(' ' , end='') for c in v : print('{0:18.4f}'.format(c) , end='') print() # In-place LU matrix decomposition. The diagonal # elements of the upper triangular matrix U are # all 1 and are not stored. Pivoting is used and # the matrix is implemented as a dictionary. It # is only intended for use with small matrices. def LU_decompose(A, n) : p = [0] * n for k in range(n) : # find pivot p[k] = k max = math.fabs(A[(k,k)]) for j in range(k + 1, n) : if max < math.fabs(A[(j,k)]) : max = math.fabs(A[(j,k)]) p[k] = j # exchange rows if necessary if p[k] != k : for j in range(n) : A[(k,j)], A[(p[k],j)] = A[(p[k],j)], A[(k,j)] # exit if matrix is singular if A[(k,k)] == 0.0 : return None # set upper triangular elements for j in range(k + 1,n) : A[(k,j)] /= A[(k,k)] # update remaining part of original matrix for i in range(k + 1, n) : for j in range(k + 1, n) : A[(i,j)] -= A[(i,k)] * A[(k,j)] # return pivot array return p # Use the LU decomposition above to solve the matrix # equation A x = b for x given A and b def LU_solve(A, p, b) : n = len(p) x = [0] * n # calculate U x = L^-1 b for k in range(n) : if p[k] != k : b[k], b[p[k]] = b[p[k]], b[k] x[k] = b[k] for i in range(k) : x[k] -= x[i] * A[(k,i)] x[k] /= A[(k,k)] # back substitute for x = U^-1 (L^-1 b) for k in reversed(range(n)) : if p[k] != k : b[k], b[p[k]], b[p[k]], b[k] for i in range(k + 1, n) : x[k] -= x[i] * A[(k,i)] return x def lsq_solve(x, y, n) : m = {} # matrix as dictionary v = [] # vector as list # set up matrix and vectors for least squares for i in range(n) : v.append(sum(xx ** i * yy for xx, yy in zip(x, y))) for j in range(i, n) : m[(i,j)] = m[(j,i)] = sum(xx ** (i + j) for xx in x) # decompose the matrix into lower and upper triangular # matrices p = LU_decompose(m, n) if p != None : return LU_solve(m, p, v) else : return None def do_lsq(x, y, lsq_size) : # get least squares coefficients f = lsq_solve(x, y, lsq_size) # now find the standard deviation from the curve s = 0 for i in range(len(x)) : t = sum(f[j] * x[i] ** j for j in range(lsq_size)) s += (y[i] - t) ** 2 sd = 2 * math.sqrt(s / len(x)) # now remove 'outliers' - data points outside twice # the standard deviation sc = 0 for i in reversed(range(len(x))) : t = sum(f[j] * x[i] ** j for j in range(lsq_size)) if math.fabs(y[i] - t) > sd : del x[i] del y[i] sc += 1 # if we had to remove more than 10% of measurements # declare that the result is not stable if 10 * sc > len(x) : return None else : return f print('Machine:', platform.processor()) print('Running:', platform.platform()) print('SPEED CURVE (l: no of limbs) cycles: c[0] + c[1] * l + c[2] * l^2') print('ROUTINE ', end = '') print(' c[0] c[1] c[2]') lines = '' cnt = 0 lsq_size = 4 for args in ll + lq : cnt += 1 # run speed for each routine in the list above ret = run_exe(os.path.join(dir, 'speed'), args, '') # parse the output to produce limbs[] and times[] x = [] y = [] lines = ret[1].split('\n') for l in lines : if len(l) : s = l.split() try : t = [float(i) for i in s] except : continue x += [t[0]] y += [t[1]] # output the name of the routine nn = args.split(' ')[-1] print('{0:<30s}'.format(nn) , end='') if not len(x) : # print(ret[1].strip(), '(failed to parse output)') print('(failed to parse output)') continue q = 0 if args in ll else 1 rep = q while rep < 3 : rep += 1 f = do_lsq(x, y, lsq_size) if f != None : break else : print('not stable') continue if args in lq : print('{0[0]:11.1f} {0[1]:11.1f} {0[2]:11.1f}'.format(f)) else : print('{0[0]:11.1f} {0[1]:11.1f}'.format(f))