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Barycentre with our intrinsics implementation

9.5.4.1 : Barycentre with numpy functions

Now, let's write the barycentreNumpyPython.py file :

We need also to import several packages : 
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import sys
import numpy as np
import astericshpc
The function to evaluate performances is built the same way such as the C++ one : 
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def getTimeFunctionSize(nbRepetition, nbElement):
	tabX = np.asarray(np.random.random(nbElement), dtype=np.float32)
	tabY = np.asarray(np.random.random(nbElement), dtype=np.float32)
	tabA = np.asarray(np.random.random(nbElement), dtype=np.float32)

	timeBegin = astericshpc.rdtsc()
	for i in range(0, nbRepetition):
		gx = (tabX*tabA).mean()
		gy = (tabY*tabA).mean()
	
	timeEnd = astericshpc.rdtsc()
	elapsedTime = float(timeEnd - timeBegin)/float(nbRepetition)
	elapsedTimePerElement = elapsedTime/float(nbElement)
	print("nbElement =",nbElement,", elapsedTimePerElement =",elapsedTimePerElement,"cy/el",", elapsedTime =",elapsedTime,"cy")
	print(str(nbElement) + "\t" + str(elapsedTimePerElement) + "\t" + str(elapsedTime),file=sys.stderr)
Then, we have a function to make all the points with a list of sizes : 
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def makeElapsedTimeValue(listSize, nbRepetition):
	for val in listSize:
		getTimeFunctionSize(nbRepetition, val)
Finally, we call the performances tests only if this script is executed as a main file and not if it is included by an other file : 
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if __name__ == "__main__":
	listSize = [1000,
			1600,
			2000,
			2400,
			2664,
			3000,
			4000,
			5000,
			10000]
	makeElapsedTimeValue(listSize, 1000000)
The full barycentreNumpyPython.py file : 
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'''
	Auteur : Pierre Aubert
	Mail : aubertp7@gmail.com
	Licence : CeCILL-C
'''

import sys
import numpy as np
import astericshpc

def getTimeFunctionSize(nbRepetition, nbElement):
	tabX = np.asarray(np.random.random(nbElement), dtype=np.float32)
	tabY = np.asarray(np.random.random(nbElement), dtype=np.float32)
	tabA = np.asarray(np.random.random(nbElement), dtype=np.float32)

	timeBegin = astericshpc.rdtsc()
	for i in range(0, nbRepetition):
		gx = (tabX*tabA).mean()
		gy = (tabY*tabA).mean()
	
	timeEnd = astericshpc.rdtsc()
	elapsedTime = float(timeEnd - timeBegin)/float(nbRepetition)
	elapsedTimePerElement = elapsedTime/float(nbElement)
	print("nbElement =",nbElement,", elapsedTimePerElement =",elapsedTimePerElement,"cy/el",", elapsedTime =",elapsedTime,"cy")
	print(str(nbElement) + "\t" + str(elapsedTimePerElement) + "\t" + str(elapsedTime),file=sys.stderr)

def makeElapsedTimeValue(listSize, nbRepetition):
	for val in listSize:
		getTimeFunctionSize(nbRepetition, val)

if __name__ == "__main__":
	listSize = [1000,
			1600,
			2000,
			2400,
			2664,
			3000,
			4000,
			5000,
			10000]
	makeElapsedTimeValue(listSize, 1000000)
You can download it here.
Previous
Peformances tests		 Parent
Peformances tests		 Outline Next
Barycentre with our intrinsics implementation