c2e08aba1a
AutoFDO performance is sensitive to profile density, i.e., the amount of samples in the profile relative to the program size, because profiles with insufficient samples could be inaccurate due to statistical noise and thus hurt AutoFDO performance. A previous investigation showed that AutoFDO performed better on MySQL with increased amount of samples. Therefore, we implement a profile-density computation feature to give hints about profile density to users and the compiler. We define the density of a profile Prof as follows: - For each function A in the profile, density(A) = total_samples(A) / sizeof(A). - density(Prof) = min(density(A)) for all functions A that are warm (defined below). A function is considered warm if its total-samples is within top N percent of the profile. For implementation, we reuse the `ProfileSummaryBuilder::getHotCountThreshold(..)` as threshold which can be set by percent(`--profile-summary-cutoff-hot`) or by value(`--profile-summary-hot-count`). We also introduce `--hot-function-density-threshold` to set hot function density threshold and will give suggestion if profile density is below it which implies we should increase samples. This also applies for CS profile with all profiles merged into base. Reviewed By: hoy, wenlei Differential Revision: https://reviews.llvm.org/D113781
155 lines
2.1 KiB
Plaintext
155 lines
2.1 KiB
Plaintext
[main]
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8
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810-82f:15
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834-85c:15
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870-870:1544
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875-8a1:11
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875-8bf:1223
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875-8c3:185
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893-8bf:176
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8a7-8c3:13
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5
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82f->790:15
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870->540:1546
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8a1->810:15
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8bf->870:2022
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8c3->893:276
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[partition_pivot_first]
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10
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710-72d:238
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740-753:1
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740-75b:739
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740-75f:267
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740-761:1164
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743-753:12
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743-75b:2414
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743-761:793
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755-75b:103
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755-75f:115
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3
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753->770:13
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75b->743:3327
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75f->740:385
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[partition_pivot_first:4.2 @ swap]
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1
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764-76e:2904
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1
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76e->740:2999
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[partition_pivot_first:5 @ swap]
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2
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770-770:619
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77a-783:619
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0
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[partition_pivot_last]
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15
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650-66d:206
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650-675:182
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682-689:164
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686-689:193
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6b0-6b7:18
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6b0-6bf:2082
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6b0-6c8:1180
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6b0-6ca:683
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6b9-6bf:170
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6b9-6c8:92
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6b9-6ca:62
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6d0-6d3:2230
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6e3-6ea:712
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6e3-6ef:1518
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6ec-6ef:667
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8
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66d->686:206
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675->682:79
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689->6b9:359
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6b7->68b:18
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6bf->6d0:2307
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6c8->6b0:1300
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6ca->6ec:755
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6ea->6b0:724
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[partition_pivot_last:5 @ swap]
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3
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677-67d:292
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6d6-6df:3621
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6f2-700:3528
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1
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700->6b0:3619
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[partition_pivot_last:6 @ swap]
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2
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68b-68b:1124
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695-69e:1124
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0
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[quick_sort]
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4
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790-79c:1273
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7a6-7a6:1273
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7a8-7b8:941
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7bd-7ca:791
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4
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7a6->650:817
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7a6->710:489
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7b8->790:961
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7ca->790:805
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[quick_sort:2 @ partition_pivot_first]
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12
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710-72d:408
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740-753:208
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740-75b:463
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740-75f:262
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740-761:496
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743-753:386
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743-75b:1300
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743-761:451
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755-75b:283
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755-75f:144
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774-777:619
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787-788:619
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4
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753->770:619
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75b->743:2137
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75f->740:427
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788->7a8:646
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[quick_sort:2 @ partition_pivot_last]
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17
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650-66d:295
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650-675:517
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682-689:528
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686-689:307
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68f-692:1124
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6a2-6a2:1124
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6b0-6b7:806
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6b0-6bf:1093
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6b0-6c8:935
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6b0-6ca:351
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6b9-6bf:226
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6b9-6c8:273
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6b9-6ca:81
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6d0-6d3:1391
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6e3-6ea:500
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6e3-6ef:891
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6ec-6ef:452
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9
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66d->686:307
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675->682:340
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689->6b9:580
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6a2->7a8:1167
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6b7->68b:834
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6bf->6d0:1391
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6c8->6b0:1263
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6ca->6ec:452
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6ea->6b0:518
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[quick_sort:4 @ quick_sort]
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6
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790-792:831
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790-79c:331
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7a6-7a6:331
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7a8-7b8:441
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7bd-7ca:632
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7d7-7d7:2029
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6
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792->7d7:853
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7a6->650:248
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7a6->710:103
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7b8->790:462
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7ca->790:661
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7d7->7cf:2097
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