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Table 1 Performance of the coarse-grained parallelization and the self-adaptive scheme proposed

From: Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

P Method Mean iter ±std Mean evals ±std Mean time ±std(s) Speedup
  10-eSS 80 ±30 199214 ±44836 5378±1070 -
  CeSS (τ=700s) 109 ±49 188131 ±82834 6487±3226 0.83
B1 CeSS (τ=1400s) 122 ±41 175331 ±98255 5018±1477 1.07
  saCeSS(non-adaptive) 92 ±35 143145 ±61828 3759±976 1.43
  saCeSS 62 ±21 92122 ±35058 2753±955 1.95
  10-eSS 450 ±167 1504503 ±541257 1914±714 -
  CeSS (τ=400s) 452 ±278 1637125 ±1016688 2459±2705 0.78
B2 CeSS (τ=800s) 508 ±205 1802917 ±690613 1911±1103 1.00
  saCeSS(non-adaptive) 440 ±192 1528793 ±647677 1918±833 1.00
  saCeSS 846 ±982 1247699 ±1222378 1694±1677 1.13
  10-eSS 10062 ±2528 66915128 ±15623835 511166±135988 -
  CeSS(τ=50000s) 7288 ±5551 52592578 ±35513874 332721±245829 1.53
B3 saCeSS(non-adaptive) 4323 ±3251 32604331 ±23357322 251305±209082 2.03
  saCeSS 4113 ±3130 27647470 ±21488783 229888±238970 2.22
  10-eSS 99 ±121 2230089 ±2068300 750±692 -
  CeSS (τ=100s) 140 ±386 1665954 ±2921838 817±1909 0.92
B4 CeSS (τ=200s) 119 ±87 1649723 ±1024833 518±428 1.45
  saCeSS(non-adaptive) 39 ±30 1163458 ±927751 402±303 1.86
  saCeSS 35 ±24 1017956 ±728328 343±240 2.18
  10-eSS 16 ±4 69448 ±14570 901±197 -
  CeSS (τ=200s) 11 ±4 108481 ±36190 1481±634 0.61
B5 CeSS (τ=400s) 14 ±3 94963 ±20172 996±264 0.90
  saCeSS(non-adaptive) 10 ±2 49622 ±9530 637±131 1.42
  saCeSS 10 ±3 51076 ±12696 658±174 1.37
  10-eSS 4659 ±3742 9783720 ±8755231 8217±7536 -
  CeSS (τ=1000s) 5919 ±5079 10475485 ±8978383 8109±7441 1.01
B6 CeSS (τ=2000s) 6108 ±6850 10778260 ±12157617 7878±9400 1.04
  saCeSS(non-adaptive) 2501 ±1517 4394243 ±2689489 3638±2302 2.26
  saCeSS 1500 ±1265 2594741 ±2214235 2177±1933 3.77
  1. Execution time and speedup results using 10 processors. Stopping criteria: VTR B1=1.3753×104, VTR B2=2.50×102, VTR B3=3.7×10−1, VTR B4=55, VTR B5=4.2×103, VTR B6=1.0833×105