def __init__(self, numchannels, numantpol, accumulation_length, skybandwidth, input_bitwidth, fft_out_bitwidth):
self.blocks = {}
self.totalblocks = 0
self.maxdesigns = 1
self.singleimplementation = 1
self.windowsize = 1024
cost = 'dollars'
#cost = 'power'
#add the platforms
self.platforms = {}
#self.platforms['GTX580'] = Platform.createGTX580Server(cost)
#self.platforms['ROACH'] = Platform.createRoach(cost)
self.platforms['DualGTX690'] = Platform.createDualGTX690Server(cost)
self.platforms['ROACH2'] = Platform.createRoach2(cost)
# add the ADC
adc_bw = skybandwidth*2*input_bitwidth
#self.blocks['ADC'] = CBlock('ADC',CBlock.getADCModel(self.platforms, skybandwidth, input_bitwidth),-1,0,0,'FIR',0,4*adc_bw,numantpol/4)
# we are using a 16 input adc board
# multiplier needs to be a multiple of 4 because the benchmarks do 4 parallel firs and ffts
adcmultiplier = 4 # process 4 streams at a time
self.blocks['ADC'] = CBlock('ADC',CBlock.getADCModel(self.platforms, skybandwidth, input_bitwidth), -1,0,0,'PFB',0,adcmultiplier*adc_bw,numantpol/adcmultiplier, CBlock.getADCMaximums(self.platforms, adcmultiplier))
self.totalblocks += numantpol/adcmultiplier
#use pfb to process 4 channels at a time
fft_out_bandwidth = skybandwidth * 2 * fft_out_bitwidth
pfb_model = CBlock.getPFBModel(self.platforms, skybandwidth, input_bitwidth, numchannels)
fft_model = CBlock.getFFTRealModel(self.platforms, skybandwidth, numchannels)
firfftmodel = CBlock.combineModels(pfb_model, fft_model)
#print firfftmodel
#firfft2xmodel = CBlock.combineModels(firfftmodel, firfftmodel)
#print firfft2xmodel
self.blocks['PFB'] = CBlock('PFB',firfftmodel,'ADC',0,adcmultiplier*adc_bw,'Transpose',0,adcmultiplier*fft_out_bandwidth,numantpol/adcmultiplier)
self.totalblocks += numantpol/adcmultiplier
transposemodel = CBlock.getTransposeModel(self.platforms, skybandwidth, numchannels, self.windowsize)
self.blocks['Transpose'] = CBlock('Transpose',transposemodel,'PFB',0,adcmultiplier*fft_out_bandwidth,'XEng',0,adcmultiplier*fft_out_bandwidth,numantpol/adcmultiplier)
self.totalblocks += numantpol/adcmultiplier
# add the PFBTranspose
#fft_out_bandwidth = skybandwidth * 2 * fft_out_bitwidth
#firmodel = CBlock.getPFBModel(self.platforms, skybandwidth, input_bitwidth, numchannels)
#fftmodel = CBlock.getFFTRealModel(self.platforms, skybandwidth, numchannels)
#transposemodel = CBlock.getTransposeModel(self.platforms, skybandwidth, numchannels, self.windowsize)
#combinedmodel = CBlock.combineModels(firmodel, CBlock.combineModels(fftmodel, transposemodel))
#we need 4 of these for our 16 input adc
#self.blocks['PFBTranspose'] = CBlock('PFBTranspose',combinedmodel,'ADC',0,adcmultiplier*adc_bw,'XEng',1,adcmultiplier * fft_out_bandwidth,numantpol/adcmultiplier)
#self.totalblocks += numantpol/adcmultiplier
# add the XEngines
gtx580_max_bw = {32:0.06914, 64:0.03095, 96:0.01748, 128:0.01069, 192:0.00536, 256:0.00318, 512:0.00087, 1024:0.00023}
# the minimum number of xengines we need
# if we use any fewer, they will not fit on the gpu
mingpuxengines = int(numpy.power(2,numpy.ceil(numpy.log2(skybandwidth/gtx580_max_bw[numantpol]))))
# assume xengine is running at 200MHz, takes nantpol clock cycles to get the data out for a single frequency channel
# maximum bandwidth it can process is 200MHz/nantpol
maxfpgaxengbw = .2/numantpol
#the maximum amount of bandwidth we can process in an xengine and still support our platforms
maxxenginebw = min(maxfpgaxengbw,gtx580_max_bw[numantpol])
# we need to create this many xengines to meet the spec
minxengines = int(skybandwidth/maxxenginebw)
#note: this needs to be a power of 2
numxengines = 8*minxengines
#numxengines = 4*mingpuxengines
#print 'Num xengines is: ' + `numxengines`
#numxengines = mingpuxengines*4
xengine_sky_bandwidth = skybandwidth/numxengines
#print 'Sky bw is: ' + `xengine_sky_bandwidth`
#print xengine_sky_bandwidth
xengine_in_bandwidth = numantpol*fft_out_bandwidth/numxengines
#print CBlock.getXEngModel(self.platforms, xengine_sky_bandwidth, numantpol)
self.blocks['XEng'] = CBlock('XEng',CBlock.getXEngModel(self.platforms, xengine_sky_bandwidth, numantpol) ,'Transpose', 1,xengine_in_bandwidth,-1,0,0,numxengines)
self.totalblocks += numxengines
def __init__(self, numchannels, numantpol, accumulation_length,
skybandwidth, input_bitwidth, fft_out_bitwidth):
self.blocks = {}
self.totalblocks = 0
self.maxdesigns = 1
self.singleimplementation = 1
self.windowsize = 1024
cost = 'dollars'
#cost = 'power'
#add the platforms
self.platforms = {}
#self.platforms['GTX580'] = Platform.createGTX580Server(cost)
#self.platforms['ROACH'] = Platform.createRoach(cost)
self.platforms['DualGTX690'] = Platform.createDualGTX690Server(cost)
self.platforms['ROACH2'] = Platform.createRoach2(cost)
# add the ADC
adc_bw = skybandwidth * 2 * input_bitwidth
#self.blocks['ADC'] = CBlock('ADC',CBlock.getADCModel(self.platforms, skybandwidth, input_bitwidth),-1,0,0,'FIR',0,4*adc_bw,numantpol/4)
# we are using a 16 input adc board
# multiplier needs to be a multiple of 4 because the benchmarks do 4 parallel firs and ffts
adcmultiplier = 4 # process 4 streams at a time
self.blocks['ADC'] = CBlock(
'ADC',
CBlock.getADCModel(self.platforms, skybandwidth,
input_bitwidth), -1, 0, 0, 'PFB', 0,
adcmultiplier * adc_bw, numantpol / adcmultiplier,
CBlock.getADCMaximums(self.platforms, adcmultiplier))
self.totalblocks += numantpol / adcmultiplier
#use pfb to process 4 channels at a time
fft_out_bandwidth = skybandwidth * 2 * fft_out_bitwidth
pfb_model = CBlock.getPFBModel(self.platforms, skybandwidth,
input_bitwidth, numchannels)
fft_model = CBlock.getFFTRealModel(self.platforms, skybandwidth,
numchannels)
firfftmodel = CBlock.combineModels(pfb_model, fft_model)
#print firfftmodel
#firfft2xmodel = CBlock.combineModels(firfftmodel, firfftmodel)
#print firfft2xmodel
self.blocks['PFB'] = CBlock('PFB', firfftmodel, 'ADC', 0,
adcmultiplier * adc_bw, 'Transpose', 0,
adcmultiplier * fft_out_bandwidth,
numantpol / adcmultiplier)
self.totalblocks += numantpol / adcmultiplier
transposemodel = CBlock.getTransposeModel(self.platforms, skybandwidth,
numchannels, self.windowsize)
self.blocks['Transpose'] = CBlock('Transpose', transposemodel, 'PFB',
0, adcmultiplier * fft_out_bandwidth,
'XEng', 0,
adcmultiplier * fft_out_bandwidth,
numantpol / adcmultiplier)
self.totalblocks += numantpol / adcmultiplier
# add the PFBTranspose
#fft_out_bandwidth = skybandwidth * 2 * fft_out_bitwidth
#firmodel = CBlock.getPFBModel(self.platforms, skybandwidth, input_bitwidth, numchannels)
#fftmodel = CBlock.getFFTRealModel(self.platforms, skybandwidth, numchannels)
#transposemodel = CBlock.getTransposeModel(self.platforms, skybandwidth, numchannels, self.windowsize)
#combinedmodel = CBlock.combineModels(firmodel, CBlock.combineModels(fftmodel, transposemodel))
#we need 4 of these for our 16 input adc
#self.blocks['PFBTranspose'] = CBlock('PFBTranspose',combinedmodel,'ADC',0,adcmultiplier*adc_bw,'XEng',1,adcmultiplier * fft_out_bandwidth,numantpol/adcmultiplier)
#self.totalblocks += numantpol/adcmultiplier
# add the XEngines
gtx580_max_bw = {
32: 0.06914,
64: 0.03095,
96: 0.01748,
128: 0.01069,
192: 0.00536,
256: 0.00318,
512: 0.00087,
1024: 0.00023
}
# the minimum number of xengines we need
# if we use any fewer, they will not fit on the gpu
mingpuxengines = int(
numpy.power(
2,
numpy.ceil(numpy.log2(skybandwidth /
gtx580_max_bw[numantpol]))))
# assume xengine is running at 200MHz, takes nantpol clock cycles to get the data out for a single frequency channel
# maximum bandwidth it can process is 200MHz/nantpol
maxfpgaxengbw = .2 / numantpol
#the maximum amount of bandwidth we can process in an xengine and still support our platforms
maxxenginebw = min(maxfpgaxengbw, gtx580_max_bw[numantpol])
# we need to create this many xengines to meet the spec
minxengines = int(skybandwidth / maxxenginebw)
#note: this needs to be a power of 2
numxengines = 8 * minxengines
#numxengines = 4*mingpuxengines
#print 'Num xengines is: ' + `numxengines`
#numxengines = mingpuxengines*4
xengine_sky_bandwidth = skybandwidth / numxengines
#print 'Sky bw is: ' + `xengine_sky_bandwidth`
#print xengine_sky_bandwidth
xengine_in_bandwidth = numantpol * fft_out_bandwidth / numxengines
#print CBlock.getXEngModel(self.platforms, xengine_sky_bandwidth, numantpol)
self.blocks['XEng'] = CBlock(
'XEng',
CBlock.getXEngModel(self.platforms, xengine_sky_bandwidth,
numantpol), 'Transpose', 1,
xengine_in_bandwidth, -1, 0, 0, numxengines)
self.totalblocks += numxengines
