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Implemented parallel processing of each runs

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This commit is contained in:
Sudipta Basak 2024-03-26 08:40:14 +00:00
parent e98de5bf9f
commit 6bd277f54b
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2 changed files with 8 additions and 16 deletions
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12
ConnectivityTest/MaliciousNodesVsNetDegree/prog.py
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@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
import sys
from datetime import datetime
import os
from joblib import Parallel, delayed
def plotData(conf):
@ -37,8 +38,6 @@ def getValidatorCountPerColumn(numberOfCols, numOfValidators, chiC):
return validatorCountPerColumn
def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
print(f"Running: {run_i + 1} / {runs}")
isParted = False
partCount = 0
isPartedCount = 0
@ -49,7 +48,6 @@ def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
partCount += 1
if isParted: isPartedCount += 1
sys.stdout.write("\033[F")
return isPartedCount, partCount
@ -65,11 +63,9 @@ def study():
for mal in mals:
isPartedCount = partCount = 0
validatorCountPerColumn = getValidatorCountPerColumn(numberOfColumns, numberOfValidators, custody)
for _run in range(runs):
_isPartedCount, _partCount = runOnce(_run, runs, deg, validatorCountPerColumn, mal)
isPartedCount += _isPartedCount
partCount += _partCount
results = Parallel(-1)(delayed(runOnce)(_run, runs, deg, validatorCountPerColumn, mal) for _run in range(runs))
isPartedCount = sum([res[0] for res in results])
partCount = sum([res[1] for res in results])
partPercentages.append(isPartedCount * 100 / runs)
avgDisconnectedCols.append(partCount / runs)
print(f"Malicious Nodes: {mal}%, Partition Percentage: {partPercentages[-1]}, Avg. Partitions: {avgDisconnectedCols[-1]}")

12
ConnectivityTest/MaliciousNodesVsNumberOfNodes/prog.py
View File

@ -5,6 +5,7 @@ import sys
import numpy as np
from datetime import datetime
import os
from joblib import Parallel, delayed
def plotData(conf):
@ -38,8 +39,6 @@ def getValidatorCountPerColumn(numberOfCols, numOfValidators, chiC):
return validatorCountPerColumn
def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
print(f"Running: {run_i + 1} / {runs}")
isParted = False
partCount = 0
isPartedCount = 0
@ -50,7 +49,6 @@ def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
partCount += 1
if isParted: isPartedCount += 1
sys.stdout.write("\033[F")
return isPartedCount, partCount
@ -66,11 +64,9 @@ def study():
for mal in mals:
isPartedCount = partCount = 0
validatorCountPerColumn = getValidatorCountPerColumn(numberOfColumns, nv, custody)
for _run in range(runs):
_isPartedCount, _partCount = runOnce(_run, runs, deg, validatorCountPerColumn, mal)
isPartedCount += _isPartedCount
partCount += _partCount
results = Parallel(-1)(delayed(runOnce)(_run, runs, deg, validatorCountPerColumn, mal) for _run in range(runs))
isPartedCount = sum([res[0] for res in results])
partCount = sum([res[1] for res in results])
partPercentages.append(isPartedCount * 100 / runs)
avgDisconnectedCols.append(partCount / runs)
print(f"Malicious Nodes: {mal}%, Partition Percentage: {partPercentages[-1]}, Avg. Partitions: {avgDisconnectedCols[-1]}")