我有一个程序需要不断地 - 在设定的时间间隔内，在一定的时间限制内 - 弹出/更新不同对象列表的列表。
def update_dwindow_tables(cpos):
# remove old tables
kill_ids = [(f,l) for f,l in dwindow_tables if l == cpos]
for kid in kill_ids:
dwindow_tables.pop(kid)
# add new tables / append with new rows
for f,i,l in window_ids: # len == 100
k = (f+cpos,f+i+l+cpos)
try:
table = dwindow_tables[k]
except KeyError:
table = list()
dwindow_tables[k] = table
table += [[f,i,l,s,ss,fcalc,None,None,list()] \
for fcalc in FEATURE_INFO \ # len == 4
for s in SYM_RANGE \ # len == 6
for ss in FEATURE_INFO[fcalc]] # len == 5
当总体内存使用率为“低”且我运行时，我得到可接受/预期的结果：
def test_with_noallocs(passes=25):
cpos = 1
dwindow_tables.clear() # start fresh
subprocess.Popen(['free']) # show current memory
for ii in range(passes):
t = timed(update_dwindow_tables,1,cpos) # simple timer
print(str(ii),'::',str(t))
cpos += 1
>>> test_with_noallocs()
total used free shared buffers cached
Mem: 7939812 3235144 4704668 11040 179312 2340784
-/+ buffers/cache: 715048 7224764
Swap: 6026236 4 6026232
0 :: 0.07612323760986328
1 :: 0.018736600875854492
2 :: 0.08361983299255371
3 :: 0.01877737045288086
4 :: 0.021776199340820312
5 :: 0.10514140129089355
6 :: 0.01875615119934082
7 :: 0.12712812423706055
8 :: 0.020363807678222656
9 :: 0.14745235443115234
10 :: 0.020594120025634766
11 :: 0.020571231842041016
12 :: 0.17327523231506348
13 :: 0.020349502563476562
14 :: 0.022448301315307617
15 :: 0.019313335418701172
16 :: 0.2052304744720459
17 :: 0.018848657608032227
18 :: 0.020733118057250977
19 :: 0.022453784942626953
20 :: 0.02028656005859375
21 :: 0.2401261329650879
22 :: 0.019221067428588867
23 :: 0.021938323974609375
24 :: 0.022317171096801758
随着整体内存使用量的增长，我遇到了一些问题：
def test_with_allocs(passes=8,memjumps=7):
mhold = []
cpos = 1
dwindow_tables.clear() # start fresh
for i in range(memjumps):
subprocess.Popen(['free']) # show current memory
for ii in range(passes):
t = timed(update_dwindow_tables,1,cpos) # simple timer
print(str(ii),'::',str(t))
cpos += 1
mhold.append([0] * 62500000) # alloc ~ 500MB
print('-'*80)
...
>>> test_with_allocs()
total used free shared buffers cached
Mem: 7939812 3291616 4648196 11040 179384 2340792
-/+ buffers/cache: 771440 7168372
Swap: 6026236 4 6026232
0 :: 0.02623128890991211
1 :: 0.07842659950256348
2 :: 0.01869797706604004
3 :: 0.02117013931274414
4 :: 0.09816169738769531
5 :: 0.017796039581298828
6 :: 0.11702108383178711
7 :: 0.020305156707763672
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 3786144 4153668 11040 179384 2340792
-/+ buffers/cache: 1265968 6673844
Swap: 6026236 4 6026232
0 :: 2.1014063358306885 <--- YIKES!
1 :: 0.02068185806274414
2 :: 0.0225675106048584
3 :: 0.7974197864532471
4 :: 0.02149367332458496
5 :: 0.019992589950561523
6 :: 0.021541118621826172
7 :: 0.8673183917999268
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 4294676 3645136 11040 179384 2340792
-/+ buffers/cache: 1774500 6165312
Swap: 6026236 4 6026232
0 :: 1.3272449970245361 <--- YIKES!
1 :: 0.020661592483520508
2 :: 0.023934364318847656
3 :: 1.5896172523498535 <--- YIKES!
4 :: 0.02139139175415039
5 :: 0.020427465438842773
6 :: 0.020937681198120117
7 :: 0.022132158279418945
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 4802688 3137124 11040 179384 2340792
-/+ buffers/cache: 2282512 5657300
Swap: 6026236 4 6026232
0 :: 3.582160711288452 <--- YIKES!
1 :: 0.0217897891998291
2 :: 0.021826744079589844
3 :: 0.0232698917388916
4 :: 0.022684097290039062
5 :: 0.02332758903503418
6 :: 0.026173830032348633
7 :: 2.3785290718078613 <--- YIKES!
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 5307360 2632452 11040 179392 2340800
-/+ buffers/cache: 2787168 5152644
Swap: 6026236 4 6026232
0 :: 1.2985937595367432 <--- YIKES!
1 :: 0.02199387550354004
2 :: 0.027379512786865234
3 :: 0.02257084846496582
4 :: 0.026216983795166016
5 :: 0.024593830108642578
6 :: 3.0734689235687256 <--- YIKES!
7 :: 0.023942232131958008
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 5798440 2141372 11040 179392 2340800
-/+ buffers/cache: 3278248 4661564
Swap: 6026236 4 6026232
0 :: 1.329371690750122 <--- YIKES!
1 :: 0.02473139762878418
2 :: 0.023987293243408203
3 :: 0.0245821475982666
4 :: 0.02610492706298828
5 :: 3.6783151626586914 <--- YIKES!
6 :: 0.023839950561523438
7 :: 0.023787736892700195
--------------------------------------------------------------------------------
total used free shared buffers cached
Mem: 7939812 6287984 1651828 11040 179400 2340800
-/+ buffers/cache: 3767784 4172028
Swap: 6026236 4 6026232
0 :: 1.3231782913208008 <--- YIKES!
1 :: 0.02602362632751465
2 :: 0.027123451232910156
3 :: 0.02720165252685547
4 :: 0.02463078498840332
5 :: 4.431311845779419 <--- YIKES!
6 :: 0.0224149227142334
7 :: 0.025854110717773438
--------------------------------------------------------------------------------
我认为这与Python的堆分配器如何工作有关？我所能找到的只是一些旧帖子，以及文档中的几页，给出了概述，但没有更具体的内容。也许有人可以解释这一点，以及是否有一个简单的解决方法（或者我只是遗漏了一些显而易见的东西），然后才开始勉强通过消息来源。
答案 0 :(得分：0)
事实证明这是垃圾收集器，事后看来这显然是愚蠢的。
obmalloc.c中的堆分配（原始层和对象层）在上面的test_with_allocs条件下基本上是constat时间。