Difference between revisions of "PDR.PODM Distributed Memory"

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= Interesting Findings =
 
= Interesting Findings =
 +
 +
=== 15 MPI ranks depth = 3 ===
 +
 
[[File:PDR PODM Parallelism.png| 700px]]
 
[[File:PDR PODM Parallelism.png| 700px]]
 
[[File:PDR_PODM_Histogram_15.png| 700px]]
 
[[File:PDR_PODM_Histogram_15.png| 700px]]

Revision as of 16:18, 16 April 2020

Issues

  • No reuse of leaves refined by worker nodes. The picture below shows the issue. Two neighbour leaves (0,1) each refined as the main leaf (0 top, 1 bottom) but not refined as a neighor.

PDR PODM Leaves not refined.png

  • Current algorithm uses neighbour traversal to distribute cells to octree leaving some cells out in some cases. Such a case can happen when a cell is part of an octree leaf based on its circumcenter but

it does not have any neighbour in the same leaf.

PDR PODM Cells not distributed.png

  • During unpacking the incident cell for each vertex is not set correctly. Specifically, in the case that the initial incident cell is not part of the working unit (Leaf + LVL.1 Neighbours) and thus is not local,

it is set to the infinite cell. This causes PODM to crash randomly for some cases.

  • Another issue comes from the way global IDs are updated for each cell's neighbors' IDs. The code that updates the cell's connectivity using global IDs takes the neighbor's pointer, retrieves its global ID and

updates the neighborID field. However, when the neighbour is part of another work unit's leaf and is not local this pointer is NULL. In this case the neighborID field is wrongly reset to the infinite cell ID, which as result, deletes the connectivity information forever.

  • The function that unpacks the required leaves before refinement does not discard duplicate vertices. Duplicate vertices will always be present since each leaf is packed and sent individually, and as a result,

neighbouring leaves will include the shared vertices. Because duplicate vertices are not handled, multiple vertex objects are created that are in fact the same point geometrically. Thus, two cells that share a common vertex could have pointers to two different vertex objects and, as a result, each cell views a different state about the same vertex.

Fixes

PDR Fix.png

Work Unit After Refinement.png


Interesting Findings

15 MPI ranks depth = 3

PDR PODM Parallelism.png PDR PODM Histogram 15.png


polling_times[1] = 62.977124

polling_times[2] = 81.895647

polling_times[3] = 79.853833

polling_times[4] = 121.689584

polling_times[5] = 67.187857

polling_times[6] = 77.674953

polling_times[7] = 110.503308

polling_times[8] = 63.927371

polling_times[9] = 63.174693

polling_times[10] = 77.656739

polling_times[11] = 84.785231

polling_times[12] = 86.950730

polling_times[13] = 67.230592

polling_times[14] = 70.842911

int_to_pointer_times[1] = 413.575395

int_to_pointer_times[2] = 393.641700

int_to_pointer_times[3] = 436.249681

int_to_pointer_times[4] = 388.363599

int_to_pointer_times[5] = 454.809466

int_to_pointer_times[6] = 464.654607

int_to_pointer_times[7] = 451.311534

int_to_pointer_times[8] = 417.156188

int_to_pointer_times[9] = 458.370492

int_to_pointer_times[10] = 443.813062

int_to_pointer_times[11] = 466.672172

int_to_pointer_times[12] = 405.445182

int_to_pointer_times[13] = 466.928191

int_to_pointer_times[14] = 427.620845

clear_element_lists_times[1] = 0.000762

clear_element_lists_times[2] = 0.000687

clear_element_lists_times[3] = 0.000647

clear_element_lists_times[4] = 0.000621

clear_element_lists_times[5] = 0.000627

clear_element_lists_times[6] = 0.000670

clear_element_lists_times[7] = 0.000724

clear_element_lists_times[8] = 0.000560

clear_element_lists_times[9] = 0.000868

clear_element_lists_times[10] = 0.002675

clear_element_lists_times[11] = 0.000628

clear_element_lists_times[12] = 0.000851

clear_element_lists_times[13] = 0.001053

clear_element_lists_times[14] = 0.001273

polling_all_times[1] = 140.294989

polling_all_times[2] = 171.339134

polling_all_times[3] = 158.837717

polling_all_times[4] = 230.522151

polling_all_times[5] = 151.359351

polling_all_times[6] = 168.926307

polling_all_times[7] = 236.011997

polling_all_times[8] = 145.662843

polling_all_times[9] = 159.411167

polling_all_times[10] = 171.122498

polling_all_times[11] = 194.755214

polling_all_times[12] = 186.417060

polling_all_times[13] = 152.213076

polling_all_times[14] = 162.141306

wait_in_queue_times1[1] = 2027.081317

wait_in_queue_times1[2] = 2004.238402

wait_in_queue_times1[3] = 2078.419998

wait_in_queue_times1[4] = 2007.550366

wait_in_queue_times1[5] = 1986.886886

wait_in_queue_times1[6] = 1951.253771

wait_in_queue_times1[7] = 1942.383973

wait_in_queue_times1[8] = 2005.419375

wait_in_queue_times1[9] = 1997.842771

wait_in_queue_times1[10] = 1997.673105

wait_in_queue_times1[11] = 2000.956408

wait_in_queue_times1[12] = 1974.561631

wait_in_queue_times1[13] = 1979.973832

wait_in_queue_times1[14] = 2072.972642

wait_in_queue_times2[1] = 3162.564690

wait_in_queue_times2[2] = 3152.861625

wait_in_queue_times2[3] = 3103.681840

wait_in_queue_times2[4] = 3093.594562

wait_in_queue_times2[5] = 3094.828513

wait_in_queue_times2[6] = 3068.933678

wait_in_queue_times2[7] = 3007.889167

wait_in_queue_times2[8] = 3148.827740

wait_in_queue_times2[9] = 3080.482366

wait_in_queue_times2[10] = 3088.332840

wait_in_queue_times2[11] = 3042.559130

wait_in_queue_times2[12] = 3121.678383

wait_in_queue_times2[13] = 3079.916276

wait_in_queue_times2[14] = 3124.285036

mesher_elements[1] = 2288798

mesher_elements[2] = 2536028

mesher_elements[3] = 2709194

mesher_elements[4] = 4263686

mesher_elements[5] = 2157859

mesher_elements[6] = 3134463

mesher_elements[7] = 4804167

mesher_elements[8] = 1975299

mesher_elements[9] = 2373811

mesher_elements[10] = 2721700

mesher_elements[11] = 1998762

mesher_elements[12] = 2323134

mesher_elements[13] = 4775266

mesher_elements[14] = 2789597

meshing_times[1] = 36.443444

meshing_times[2] = 37.558417

meshing_times[3] = 45.448564

meshing_times[4] = 40.453400

meshing_times[5] = 43.791719

meshing_times[6] = 40.182756

meshing_times[7] = 44.701021

meshing_times[8] = 39.561876

meshing_times[9] = 47.492666

meshing_times[10] = 46.783555

meshing_times[11] = 40.445111

meshing_times[12] = 39.889953

meshing_times[13] = 47.444813

meshing_times[14] = 35.083527

leaf_distribution_time[1] = 72.064785

leaf_distribution_time[2] = 67.755446

leaf_distribution_time[3] = 78.394198

leaf_distribution_time[4] = 72.630350

leaf_distribution_time[5] = 77.081779

leaf_distribution_time[6] = 78.999842

leaf_distribution_time[7] = 77.059384

leaf_distribution_time[8] = 72.617468

leaf_distribution_time[9] = 69.875876

leaf_distribution_time[10] = 71.842582

leaf_distribution_time[11] = 73.979892

leaf_distribution_time[12] = 71.419246

leaf_distribution_time[13] = 72.170305

leaf_distribution_time[14] = 72.779034


total_num_mesher_elements = 43167663

initial_number_of_elements = 2315899

initial_io_time = 9.116445

initial_allocation_time = 0.004795

initial_meshing_time = 3.937135

initial_mesh_distribution_time = 2.665803

get_leaf_elements_time = 0.331813

pointer_to_int_time = 0.138809

pack_data_time = 0.064877

send_data_time = 0.191445

sub_total_time = 3831.977334

max_meshing_time = 47.492666

max_io_time = 9.596057

max_allocation_time = 9.961161

max_recv_time = 0.000000

max_polling_time = 121.689584

max_int_to_pointer_time = 466.928191

max_clear_element_lists_time = 0.002675

max_polling_all_time = 236.011997

max_wait_in_queue_time1 = 2078.419998

max_wait_in_queue_time2 = 3162.564690

elements_per_second = 7311014.065501

elements_per_second1 = 588219.279339

elements_per_second2 = 20765032.278210

master_idle_time = 3831.174962

surface_box_time = 9.046863

edt_time = 0.566338

octree_time = 0.122400


  • How many times migrated a leaf (main and neighbor) ?
  • How many times a leaf is scheduled ? (as main leaf)
  • Packed size for each transmission (MB)/time?
  • Meshing time each time it was main
  • Initial meshing time
  • Per process :
    • Meshing
    • Preprocessing (pack/unpack)
    • waiting time
  • Waiting time per process ?
  • Elements before and after each time it was used (main/neighbor) ?
leaf_id init elements num_migrations num_scheduled num_refined max_size_migration (KB) min_size_migration (KB) avg_size_migration (KB) max_time_migration (sec) min_time_migration (sec) avg_time_migration (sec)
0 5140 1 0 1
1 4921 1 0 1
2 4880 1 0 1
3 4714 1 0 1
4 5044 1 0 1
5 4668 1 0 1
6 4741 1 0 1
7 4470 1 1 0
8 4854 1 0 1
9 4733 1 0 1
10 4628 1 0 1
11 4719 1 0 1
12 4747 1 0 1
13 4692 1 0 1
14 4484 1 0 1
15 4439 1 0 1
16 4847 2 0 2 415 415 415.0 0.000405 0.000405 0.000405
17 4597 6 0 6 394 394 394.0 0.015056 0.000377 0.003564199999999999
18 4875 4 0 4 417 417 417.0 0.030162 0.000719 0.016067
19 4704 9 0 9 405 404 404.0 0.019694 0.000544 0.007640875
20 4611 2 0 2 395 395 395.0 0.000401 0.000401 0.000401
21 4515 10 0 10 387 387 387.0 0.023126 0.000373 0.0037203333333333324
22 4715 7 0 7 404 404 404.0 0.016818 0.000444 0.006300166666666667
23 4557 21 1 20 403 394 400.0 0.04192 0.000398 0.01055885
24 4570 10 0 10 392 392 392.0 0.030502 0.000373 0.00599111111111111
25 4666 13 0 13 400 400 400.0 0.047174 0.00068 0.01136525
26 4746 15 0 15 531 407 510.0 0.049097 0.00055 0.00914507142857143
27 4868 17 0 17 621 417 593.0 0.052032 0.000761 0.014502000000000001
28 4481 19 0 19 384 384 384.0 0.019985 0.000293 0.0026930555555555557
29 4509 26 0 26 387 387 387.0 0.034138 0.000437 0.006108079999999999
30 4590 34 4 30 10613 395 8870.0 1.144701 0.000322 0.13719339393939395
31 4752 39 4 35 20063 415 15899.0 2.210566 0.000639 0.17472076315789475
32 4904 1 0 1
33 4767 1 0 1
34 4667 1 0 1
35 4510 1 0 1
36 4929 1 0 1
37 4771 1 0 1
38 4754 1 0 1
39 4459 1 0 1
40 4686 1 0 1
41 4609 1 0 1
42 4489 1 0 1
43 4465 1 0 1
44 4641 1 0 1
45 4707 1 0 1
46 4539 1 0 1
47 4540 1 0 1
48 4813 3 0 3 412 412 412.0 0.000928 0.000405 0.0006665
49 4425 17 0 17 380 380 380.0 0.012858 0.000314 0.0024941250000000002
50 4620 11 0 11 396 396 396.0 0.022816 0.000463 0.006890599999999999
51 4525 32 0 32 388 388 388.0 0.061139 0.000387 0.00679132258064516
52 4762 4 0 4 408 408 408.0 0.046096 0.018953 0.031662
53 4583 19 0 19 393 393 393.0 0.018943 0.000514 0.006347722222222222
54 4628 14 0 14 397 397 397.0 0.041793 0.000596 0.012489384615384614
55 4521 38 1 37 394 389 393.0 0.040649 0.000447 0.009107486486486486
56 4533 33 0 33 388 388 388.0 0.029611 0.000359 0.004132593750000001
57 4476 46 0 46 384 384 384.0 0.038396 0.000364 0.006301111111111111
58 4603 54 4 50 9376 395 8107.0 0.805219 0.000388 0.08217690566037736
59 4798 63 5 58 22391 411 21441.0 2.142464 0.000345 0.19465403225806455
60 4490 36 0 36 385 385 385.0 0.023103 0.000337 0.005721457142857143
61 4512 45 0 45 387 387 387.0 0.038251 0.000424 0.00701115909090909
62 4582 60 6 54 9883 393 8875.0 1.112903 0.000483 0.10731481355932204
63 4699 67 7 60 21908 403 21305.0 1.537622 0.000399 0.1713687424242424
64 4818 1 0 1
65 4816 2 0 2 412 412 412.0 0.00045 0.00045 0.00045
66 4626 1 0 1
67 4727 2 0 2 405 405 405.0 0.000464 0.000464 0.000464
68 4623 1 0 1
69 4837 2 0 2 414 414 414.0 0.000473 0.000473 0.000473
70 4454 1 1 0
71 4429 2 0 2 381 381 381.0 0.000438 0.000438 0.000438
72 4896 1 0 1
73 5117 1 0 1
74 4669 1 0 1
75 4841 1 0 1
76 4804 1 0 1
77 4988 1 0 1
78 4587 1 1 0
79 4616 1 0 1
80 4746 15 0 15 407 407 407.0 0.018839 0.000579 0.010480928571428572
81 4600 17 0 17 394 394 394.0 0.033308 0.000398 0.0047910625
82 4909 22 0 22 627 429 571.0 0.051229 0.000809 0.014659999999999996
83 4768 25 0 25 607 408 570.0 0.054998 0.000675 0.013141125000000002
84 4596 31 0 31 394 394 394.0 0.018374 0.000358 0.005900033333333332
85 4484 33 0 33 385 385 385.0 0.040269 0.000366 0.00621278125
86 4879 45 4 41 21582 818 14618.0 0.856606 0.000981 0.086496
87 4722 54 6 48 19733 413 15960.0 1.694223 0.000403 0.22801369811320754
88 4637 12 0 12 397 397 397.0 0.0184 0.000352 0.006041727272727273
89 4901 6 0 6 419 419 419.0 0.024881 0.000641 0.0060704
90 4698 19 0 19 527 515 522.0 0.032079 0.000546 0.012114944444444442
91 4873 9 0 9 418 418 418.0 0.011423 0.000847 0.004115875
92 4534 24 0 24 388 388 388.0 0.021887 0.00037 0.005878608695652173
93 4708 11 0 11 403 403 403.0 0.015462 0.000409 0.0031311999999999998
94 4609 42 5 37 10745 520 10079.0 0.66151 0.001178 0.13872612195121953
95 4558 22 0 22 408 391 405.0 0.031273 0.000437 0.007883619047619047
96 4594 1 0 1
97 4676 3 0 3 401 401 401.0 0.000473 0.000463 0.000468
98 4465 1 0 1
99 4467 3 0 3 384 384 384.0 0.00045 0.000442 0.000446
100 4726 1 0 1
101 4658 2 0 2 399 399 399.0 0.000494 0.000494 0.000494
102 4508 1 0 1
103 4525 2 0 2 388 388 388.0 0.00049 0.00049 0.00049
104 4718 2 0 2 405 405 405.0 0.000476 0.000476 0.000476
105 4967 2 0 2 425 425 425.0 0.00051 0.00051 0.00051
106 4481 2 0 2 385 385 385.0 0.000452 0.000452 0.000452
107 4571 2 0 2 392 392 392.0 0.000471 0.000471 0.000471
108 4786 1 0 1
109 4823 1 0 1
110 4574 1 1 0
111 4680 1 0 1
112 4574 51 0 51 392 392 392.0 0.047324 0.000392 0.005151880000000001
113 4489 52 0 52 385 385 385.0 0.060409 0.000312 0.005352901960784314
114 4736 72 4 68 23594 414 17669.0 1.006988 0.000343 0.08559649295774648
115 4735 83 7 76 22078 406 18987.0 2.747555 0.000391 0.22780202439024386
116 4481 47 0 47 385 385 385.0 0.03056 0.000375 0.005288021739130433
117 4531 46 0 46 389 389 389.0 0.043212 0.000331 0.006133044444444443
118 4789 69 7 62 20996 418 17145.0 1.757034 0.000356 0.1959893823529412
119 4608 81 6 75 21187 396 18847.0 2.456932 0.000378 0.22400851249999998
120 4518 36 0 36 387 387 387.0 0.028003 0.000351 0.004179828571428572
121 4695 17 0 17 402 402 402.0 0.036991 0.000394 0.005922875
122 4632 63 5 58 8865 463 8604.0 0.732422 0.000591 0.10695459677419356
123 4671 33 0 33 401 400 401.0 0.026674 0.000354 0.007890125000000001
124 4484 33 0 33 385 385 385.0 0.036508 0.000326 0.006485437499999999
125 4649 16 0 16 399 399 399.0 0.025154 0.00034 0.006072800000000001
126 4461 60 5 55 6350 5843 6265.0 0.527465 0.008833 0.08390650847457627
127 4697 34 0 34 403 403 403.0 0.033889 0.00043 0.006612545454545454
128 5001 5 0 5 429 429 429.0 0.017186 0.000428 0.00848025
129 4783 14 0 14 499 491 494.0 0.071658 0.000541 0.015102230769230767
130 4770 5 0 5 408 408 408.0 0.036432 0.000566 0.012671
131 4780 12 0 12 467 412 438.0 0.03341 0.000402 0.012121272727272725
132 4577 10 0 10 392 392 392.0 0.020947 0.000367 0.006680222222222223
133 4618 31 3 28 5218 4818 4942.0 0.351238 0.006392 0.0750992
134 4663 10 0 10 399 399 399.0 0.037043 0.000432 0.009523222222222221
135 4555 28 1 27 3236 470 1796.0 0.107946 0.000541 0.023689148148148147
136 4842 24 0 24 833 421 538.0 0.08271 0.000418 0.013151782608695652
137 4958 27 0 27 840 433 655.0 0.063228 0.000467 0.014496038461538463
138 4799 22 0 22 813 412 730.0 0.055436 0.000558 0.007774952380952382
139 4911 26 0 26 842 421 739.0 0.123103 0.000465 0.025620559999999997
140 4801 55 1 54 35078 764 10374.0 2.521878 0.000641 0.10560070370370372
141 4818 62 2 60 35230 814 21732.0 1.25825 0.00093 0.14713504918032783
142 4742 53 4 49 33400 415 27391.0 1.310808 0.000573 0.2354048461538462
143 4829 61 4 57 35186 421 26418.0 3.787715 0.000825 0.30994716666666655
144 4887 2 0 2 418 418 418.0 0.014348 0.014348 0.014348
145 4587 8 0 8 393 393 393.0 0.040848 0.000721 0.010354142857142857
146 5103 1 0 1
147 4833 3 0 3 414 414 414.0 0.016732 0.000898 0.008815
148 4692 4 0 4 402 402 402.0 0.033889 0.000556 0.016683
149 4557 16 1 15 391 391 391.0 0.018683 0.000472 0.004319266666666668
150 4964 1 0 1
151 4674 6 0 6 400 400 400.0 0.028551 0.000983 0.013714399999999998
152 4799 16 0 16 493 411 482.0 0.037937 0.000534 0.0101036
153 4674 21 0 21 678 661 674.0 0.09922 0.000717 0.018367699999999994
154 4845 7 0 7 415 415 415.0 0.00591 0.00042 0.002009
155 4861 11 0 11 417 417 417.0 0.032705 0.000533 0.0148554
156 4616 37 2 35 10955 396 9495.0 0.84543 0.000558 0.14547416666666665
157 4896 47 4 43 29802 814 28902.0 1.4604 0.005043 0.23156036956521733
158 4867 16 0 16 418 417 418.0 0.036092 0.000502 0.011479066666666666
159 4743 24 0 24 514 415 507.0 0.052268 0.000587 0.017611521739130432
160 4728 20 0 20 406 406 406.0 0.020009 0.000408 0.008198631578947367
161 4539 53 3 50 1305 410 954.0 0.092867 0.000431 0.013970576923076926
162 4650 22 0 22 398 398 398.0 0.027525 0.000387 0.00920314285714286
163 4462 51 2 49 1281 384 1026.0 0.173192 0.000357 0.012442220000000002
164 4741 25 0 25 407 407 407.0 0.027902 0.000388 0.006532625
165 4557 61 3 58 3903 486 1465.0 0.084045 0.000469 0.012070450000000003
166 4749 28 0 28 408 408 408.0 0.036397 0.000423 0.005662185185185185
167 4547 62 6 56 5675 393 4951.0 0.556404 0.000379 0.06199437704918033
168 4811 90 5 85 33750 417 18049.0 2.345832 0.000446 0.1369888876404494
169 4866 97 2 95 35510 426 18884.0 2.747791 0.000411 0.22452585416666662
170 4733 89 3 86 34065 406 28104.0 1.726143 0.00034 0.19082899999999994
171 5014 95 7 88 35596 430 23707.0 3.052361 0.000409 0.22742587234042558
172 4820 96 1 95 34465 421 7042.0 0.218778 0.000472 0.03614748421052631
173 4922 101 2 99 35520 430 19368.0 3.092187 0.000365 0.15891331
174 4901 96 5 91 35540 420 26870.0 4.254684 0.000395 0.25204304210526324
175 5132 101 4 97 35517 439 27090.0 2.22364 0.000619 0.18806341
176 4672 13 0 13 400 400 400.0 0.029952 0.000399 0.009411
177 4585 35 0 35 394 393 394.0 0.032544 0.00043 0.0068634117647058835
178 4895 4 0 4 419 419 419.0 0.020005 0.000999 0.009127999999999999
179 4585 14 0 14 393 393 393.0 0.036209 0.000378 0.00787546153846154
180 4775 17 0 17 410 410 410.0 0.028318 0.000435 0.00724625
181 4460 42 3 39 555 387 538.0 0.052798 0.000598 0.012619195121951218
182 4883 6 0 6 418 418 418.0 0.032337 0.00095 0.010706400000000001
183 4655 19 0 19 398 398 398.0 0.029974 0.000575 0.009053555555555558
184 4798 67 3 64 20596 411 18456.0 1.975423 0.000372 0.24117915151515149
185 4741 77 6 71 35275 803 34594.0 2.824109 0.001208 0.24719185526315796
186 4787 31 0 31 517 410 493.0 0.039667 0.000379 0.0081323
187 4709 43 0 43 817 412 803.0 0.075649 0.000401 0.019051404761904762
188 4792 71 5 66 25521 804 23645.0 1.902453 0.001311 0.2771063428571429
189 4848 78 7 71 35180 33582 34843.0 1.660215 0.052097 0.22248788311688308
190 4672 35 0 35 539 410 519.0 0.048332 0.000468 0.014152470588235292
191 4668 44 0 44 813 787 805.0 0.079299 0.00091 0.01713302325581395
192 4925 32 0 32 842 815 827.0 0.066662 0.001037 0.017131677419354837
193 4824 35 0 35 838 422 565.0 0.070966 0.000469 0.012869823529411765
194 4929 33 0 33 838 431 545.0 0.0761 0.000461 0.010275656250000003
195 5073 37 0 37 857 435 680.0 0.080225 0.000457 0.013417944444444446
196 5103 68 2 66 35249 33674 34415.0 2.730947 0.049441 0.22863904477611938
197 4888 77 4 73 35319 816 14284.0 1.240214 0.001182 0.10229803947368421
198 4840 71 2 69 35261 808 11567.0 1.443657 0.000858 0.09159465714285712
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499 4581 32 0 32 395 393 395.0 0.030057 0.000359 0.005512225806451612
500 4679 32 0 32 709 407 666.0 0.08843 0.000411 0.01497041935483871
501 4697 29 0 29 583 404 567.0 0.041782 0.000377 0.008812607142857143
502 4859 17 0 17 416 416 416.0 0.02546 0.000419 0.004625125
503 4940 15 0 15 423 423 423.0 0.029683 0.000334 0.006086142857142858
504 4587 41 4 37 2074 1818 2041.0 0.08827 0.002139 0.0208681
505 4644 20 0 20 398 398 398.0 0.016099 0.000404 0.003742052631578947
506 4663 22 0 22 400 400 400.0 0.038095 0.000426 0.008067095238095238
507 4810 11 0 11 412 412 412.0 0.022261 0.000391 0.0048446999999999995
508 4595 20 0 20 405 401 404.0 0.031597 0.000323 0.008233894736842103
509 4723 9 0 9 404 404 404.0 0.036299 0.000321 0.011594375
510 4803 10 0 10 411 411 411.0 0.029019 0.000336 0.005391999999999999
511 5033 4 0 4 430 430 430.0 0.002389 0.000346 0.0010416666666666667
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