Performance drop in NumPy matrix-vector multiplication

I think that I got finally the correct answer and explanation to why:

1. This problem is fixed in the Python version 3.8.0a2 (Current Pre-release Testing Version)
2. The problem exists in Python v 3.7.2 (Latest Release) on Windows and macOS.

I wrote a little bit more longer program to test both my Widows and macOS computers. Looks like NumPy in version 3.7 started to run matmul function in all four logical processors on my computers. I don't see this in 3.8.02a:

$ python3.8 numpy_matmul.py       $ python3.7 numpy_matmul.py     

Python version  : 3.8.0a2         Python version  : 3.7.2         
  build:('v3.8.0a2:23f4589b4b',    build:('v3.7.2:9a3ffc0492',
        Feb 25 2019 10:59:08')          'Dec 24 2018 02:44:43')
  compiler:                        compiler:
     Clang 6.0 (clang-600.0.57)   Clang 6.0 (clang-600.0.57) 

Tested by Python code only :      Tested by Python code only :  
 90 time = 0.1132 cpu = 0.1100     90 time = 0.1535 cpu = 0.1236
 91 time = 0.1133 cpu = 0.1130     91 time = 0.1264 cpu = 0.1263
 92 time = 0.1079 cpu = 0.1077     92 time = 0.1089 cpu = 0.1087
 93 time = 0.1146 cpu = 0.1145     93 time = 0.1226 cpu = 0.1224
 94 time = 0.1176 cpu = 0.1174     94 time = 0.1273 cpu = 0.1271
 95 time = 0.1216 cpu = 0.1215     95 time = 0.1372 cpu = 0.1371
 96 time = 0.1115 cpu = 0.1114     96 time = 0.2854 cpu = 0.8933
 97 time = 0.1231 cpu = 0.1229     97 time = 0.2887 cpu = 0.9033
 98 time = 0.1174 cpu = 0.1173     98 time = 0.2836 cpu = 0.8963
 99 time = 0.1330 cpu = 0.1301     99 time = 0.3100 cpu = 0.9108
100 time = 0.1130 cpu = 0.1128    100 time = 0.3149 cpu = 0.9087

Tested with timeit.repeat :       Tested with timeit.repeat :   
 90 time = 0.1060 cpu = 0.1066     90 time = 0.1238 cpu = 0.3264
 91 time = 0.1091 cpu = 0.1097     91 time = 0.1233 cpu = 0.1240
 92 time = 0.1021 cpu = 0.1027     92 time = 0.1138 cpu = 0.1128
 93 time = 0.1149 cpu = 0.1156     93 time = 0.1324 cpu = 0.1327
 94 time = 0.1135 cpu = 0.1139     94 time = 0.1319 cpu = 0.1326
 95 time = 0.1170 cpu = 0.1177     95 time = 0.1325 cpu = 0.1331
 96 time = 0.1069 cpu = 0.1076     96 time = 0.2879 cpu = 0.8886
 97 time = 0.1192 cpu = 0.1198     97 time = 0.2867 cpu = 0.8986
 98 time = 0.1151 cpu = 0.1155     98 time = 0.3034 cpu = 0.8854
 99 time = 0.1200 cpu = 0.1207     99 time = 0.2867 cpu = 0.8966
100 time = 0.1146 cpu = 0.1153    100 time = 0.2901 cpu = 0.9018

Here is numpy_matmul.py:

import time
import timeit
import numpy as np
import platform


def correct_cpu(cpu_time):
    pv1, pv2, _ = platform.python_version_tuple()
    pcv = platform.python_compiler()
    if pv1 == '3' and '5' <= pv2 <= '8' and pcv =='Clang 6.0 (clang-600.0.57)':
        cpu_time /= 2.0
    return cpu_time


def test(func, n, name):
    print('\nTested %s :' % name)
    for i in range(90, 101):
        t = time.perf_counter()
        c = time.process_time()
        tm = func(i, n)
        t = time.perf_counter() - t
        c = correct_cpu(time.process_time() - c)
        st = t if tm <= 0.0 else tm
        print('%3d time = %.4f cpu = %.4f' % (i, st, c))
        if abs(t-st)/st > 0.02:
            print('    time!= %.4f' % t)


def test1(i, n):
    a, b = np.random.rand(i, i), np.random.rand(i)
    for _ in range(n):
        np.matmul(a, b)
    return 0.0


def test2(i, n):
    s = 'import numpy as np;' + \
        'a, b = np.random.rand({0},{0}), np.random.rand({0})'
    s = s.format(i)
    r = 'np.matmul(a, b)'
    t = timeit.repeat(stmt=r, setup=s, number=n)
    return sum(t)


def test3(i, n):
    s = 'import numpy as np;' + \
        'a, b = np.random.rand({0},{0}), np.random.rand({0})'
    s = s.format(i)
    r = 'np.matmul(a, b)'
    return timeit.timeit(stmt=r, setup=s, number=n)


print('Python version  :', platform.python_version())
print('       build    :', platform.python_build())
print('       compiler :', platform.python_compiler())
num = 10000
test(test1, 5 * num, 'by Python code only')
test(test2, num, 'with timeit.repeat')
test(test3, 5 * num, 'with timeit.timeit')