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Baseline_Computing_Deconvolve_Engine.dblk
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<?xml version='1.0' encoding='UTF-8'?>
<!-- $Id: -->
<DesignBlock>
<comment>Computing</comment>
<inputs>
<input>
<comment>Number of continuum major cycles</comment>
<name>Number_Continuum_Major_Cycles</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDev></StdDev>
<min>5.0</min>
<max>20.0</max>
<PeakLikelihood>10.0</PeakLikelihood>
<str></str>
<StdDevPercent></StdDevPercent>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of spectral line major cycles</comment>
<name>Number_Spectral_Major_Cycles</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDev></StdDev>
<min>5.0</min>
<max>20.0</max>
<PeakLikelihood>10.0</PeakLikelihood>
<str></str>
<StdDevPercent></StdDevPercent>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of Taylor terms in frequency expansion</comment>
<name>Number_Taylor_Terms</name>
<LowerLimit>1</LowerLimit>
<DefaultValue>
<StdDev></StdDev>
<min></min>
<max></max>
<PeakLikelihood>1.0</PeakLikelihood>
<str></str>
<StdDevPercent></StdDevPercent>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Pipeline type e.g. Calibration, Continuum, Spectral_Line, Transient, All</comment>
<name>Pipeline_Type</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDev></StdDev>
<min></min>
<max></max>
<PeakLikelihood></PeakLikelihood>
<str>Continuum</str>
<StdDevPercent></StdDevPercent>
<DistributionType>String</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues>
<value>
<value>Calibration</value>
</value>
<value>
<value>Continuum</value>
</value>
<value>
<value>Spectral_Line</value>
</value>
<value>
<value>Transient</value>
</value>
<value>
<value>All</value>
</value>
<value>
<value>Ingest</value>
</value>
</PermittedValues>
<type>string</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Processing must complete in this fraction of real time</comment>
<name>Duty_Cycle</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDevPercent></StdDevPercent>
<min></min>
<max></max>
<PeakLikelihood>0.3</PeakLikelihood>
<str></str>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>float</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of scales for deconvolution</comment>
<name>Number_Scales</name>
<LowerLimit>1</LowerLimit>
<DefaultValue>
<str></str>
<min></min>
<max></max>
<StdDevPercent></StdDevPercent>
<PeakLikelihood>5</PeakLikelihood>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of channels for continuum imaging</comment>
<name>Number_Continuum_Channels</name>
<LowerLimit>1</LowerLimit>
<DefaultValue>
<str></str>
<min></min>
<max></max>
<StdDevPercent></StdDevPercent>
<PeakLikelihood>1</PeakLikelihood>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of channels for spectral imaging</comment>
<name>Number_Spectral_Channels</name>
<LowerLimit>1</LowerLimit>
<DefaultValue>
<str></str>
<min></min>
<max></max>
<StdDevPercent></StdDevPercent>
<PeakLikelihood>32768</PeakLikelihood>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of pixels for each l,m axis</comment>
<name>Number_Pixels</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDev></StdDev>
<min></min>
<max></max>
<PeakLikelihood>4096</PeakLikelihood>
<str></str>
<StdDevPercent></StdDevPercent>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Number of iterations </comment>
<name>Number_Iterations</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDevPercent></StdDevPercent>
<min>1.0</min>
<max></max>
<PeakLikelihood>10000.0</PeakLikelihood>
<str></str>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
<input>
<comment>Width of PSF patch used in deconvolution</comment>
<name>PSF_Width</name>
<LowerLimit></LowerLimit>
<DefaultValue>
<StdDevPercent></StdDevPercent>
<min></min>
<max></max>
<PeakLikelihood>512.0</PeakLikelihood>
<str></str>
<StdDev></StdDev>
<DistributionType>FixedValue</DistributionType>
<unit></unit>
</DefaultValue>
<PermittedValues/>
<type>integer</type>
<unit></unit>
<UpperLimit></UpperLimit>
</input>
</inputs>
<name>Baseline_Computing_Deconvolve_Engine</name>
<reference>computing.xhtml</reference>
<author>Tim Cornwell</author>
<specifications/>
<outputs/>
<implementation>
from math import pow
from math import pi
from math import sqrt
from math import log
blockname='Deconvolve_Engine'
######################################################################################################
#
# Get relevant parameters from processor
#
Cleaning_Time=ListOfChildren['processor']['specifications']['Cleaning_Time']
if message['PSF_Width'] < 2:
PSF_Width=message['Number_Pixels']
else:
PSF_Width=message['PSF_Width']
doProcessing=False
if message['Pipeline_Type']=='Spectral_Line':
Processing_Fraction = message['Number_Spectral_Major_Cycles'] * message['Number_Spectral_Channels'] * message['Number_Scales'] \
* message['Number_Iterations']*pow(PSF_Width,2)* Cleaning_Time / message['Duty_Cycle']
Processing_Fraction.SetUnit('')
if message['Number_Spectral_Major_Cycles'] > 0 and message['Number_Spectral_Channels'] > 0 and message['Number_Scales'] > 0 and message['Number_Iterations'] > 0:
print blockname,"Deconvolve_Engine processing required =", Processing_Fraction*message['quantity']
SendMessage('processor', message['quantity']*Processing_Fraction, message['purchase_year'])
doProcessing=True
elif message['Pipeline_Type']=='Continuum':
Processing_Fraction = message['Number_Continuum_Major_Cycles'] * message['Number_Taylor_Terms'] * message['Number_Scales'] \
* message['Number_Iterations']*pow(PSF_Width,2)* Cleaning_Time / message['Duty_Cycle']
Processing_Fraction.SetUnit('')
if message['Number_Continuum_Major_Cycles'] > 0 and message['Number_Taylor_Terms'] > 0 and message['Number_Scales'] > 0 and message['Number_Iterations'] > 0:
print blockname,"Deconvolve_Engine processing required =", Processing_Fraction*message['quantity']
SendMessage('processor', message['quantity']*Processing_Fraction, message['purchase_year'])
doProcessing=True
######################################################################################################
#
# Calculate the memory storage costs. We assume that we can reuse memory over major cycles
#
if doProcessing:
if message['Pipeline_Type']=='Spectral_Line':
Memory_Unit_Scalar = 2 * 4 * message['Number_Spectral_Channels'] * message['Number_Scales'] * message['Number_Pixels'] * message['Number_Pixels'] / 1024 / 1024 / 1024
SendMessage('memory', message['quantity']*Memory_Unit_Scalar, message['purchase_year'])
print blockname,"Deconvolve_Engine memory required = ", message['quantity'] * Memory_Unit_Scalar/1024 , "TB"
elif message['Pipeline_Type']=='Continuum':
Memory_Unit_Scalar = 2 * 4 * message['Number_Taylor_Terms'] * message['Number_Scales'] * message['Number_Pixels'] * message['Number_Pixels'] / 1024 / 1024 / 1024
SendMessage('memory', message['quantity']*Memory_Unit_Scalar, message['purchase_year'])
print blockname,"Deconvolve_Engine memory required = ", message['quantity'] * Memory_Unit_Scalar/1024 , "TB"
outputs['NumberOfUnits'] += message['quantity']
</implementation>
<DetailedDescription>Cost model for SKA Pipeline Computing
The rate parameters come from running tConvolve on a 2012 era processor. The costs and power
come from a substantial multi-node installation designed for running a similar job load to
that needed for SKA. The storage space is for a fast 5 GB/s Lustre filesystem.
The cost calculated are for the pipeline processor and the short-term storage space required.
</DetailedDescription>
<ToolVersion>0.3</ToolVersion>
<children>
<child>
<ImplementationDB>Computing_Memory</ImplementationDB>
<LocalName>memory</LocalName>
<UnitQuantity>1.0</UnitQuantity>
</child>
<child>
<ImplementationDB>Computing_Processor</ImplementationDB>
<LocalName>processor</LocalName>
<UnitQuantity>1.0</UnitQuantity>
</child>
</children>
<CalculatedSpecifications/>
</DesignBlock>