def computeOnMasterNode():
global R, serializedData
# Create an algorithm to compute QR decomposition on the master node
algorithm = qr.Distributed(step2Master)
for i in range(nBlocks):
# Deserialize partial results from step 1
dataArch = OutputDataArchive(serializedData[i])
dataForStep2FromStep1 = DataCollection()
dataForStep2FromStep1.deserialize(dataArch)
algorithm.input.add(qr.inputOfStep2FromStep1, i, dataForStep2FromStep1)
# Compute QR decomposition
pres = algorithm.compute()
inputForStep3FromStep2 = pres.getCollection(qr.outputOfStep2ForStep3)
for i in range(nBlocks):
# Serialize partial results to transfer to local nodes for step 3
dataArch = InputDataArchive()
inputForStep3FromStep2[i].serialize(dataArch)
length = dataArch.getSizeOfArchive()
serializedData[i] = np.empty(length, dtype=np.uint8)
dataArch.copyArchiveToArray(serializedData[i])
# Result class from qr
res = algorithm.getResult()
R = res.get(qr.matrixR)
def computeOnMasterNode():
global serializedData, Sigma, V
# Create an algorithm to compute SVD on the master node
algorithm = svd.Distributed(step2Master)
for i in range(nBlocks):
# Deserialize partial results from step 1
dataArch = OutputDataArchive(serializedData[i])
dataForStep2FromStep1 = DataCollection()
dataForStep2FromStep1.deserialize(dataArch)
algorithm.input.add(svd.inputOfStep2FromStep1, i,
dataForStep2FromStep1)
# Compute SVD
# DistributedPartialResult class from svd
pres = algorithm.compute()
inputForStep3FromStep2 = pres.getCollection(svd.outputOfStep2ForStep3)
for i in range(nBlocks):
# Serialize partial results to transfer to local nodes for step 3
dataArch = InputDataArchive()
inputForStep3FromStep2[i].serialize(dataArch)
length = dataArch.getSizeOfArchive()
serializedData[i] = np.empty(length, dtype=np.uint8)
dataArch.copyArchiveToArray(serializedData[i])
# DistributedPartialResult class from svd
res = algorithm.getResult()
Sigma = res.get(svd.singularValues)
V = res.get(svd.rightSingularMatrix)
