TES Readout Implementation

\This page provides an overview of the status and inner workings of the TES Readout development and eventual implementation.

Description of TES

PseudoCode

TES Algorithm

CreateCircularBuffers (NumberOfChannels,
SamplesPerWindow, TotalNumberOfWindows)
LoadFilters()
ConfigureDataChannel()

While IncomingData:
            LoadDatainBuffers()
            ApplyCrosstalkCorrection(All_Channels,CurrentWindow)

            EventMatrix = TagEvents(All_Channels)

	For each ActiveWindow : 
            WindowIncrement=+1
				Parfor each Channel:            
                	Switch (CheckEvents(EventMatrix)):
						Case No Event Before And After
						Energy = ApplyFilter(Filter00)
						Case Event Before And After
						Energy = ApplyFilter(Filter11)

						Case Event Before Only
						Energy = ApplyFilter(Filter10)

						Case Event After Only
						Energy = ApplyFilter(Filter00)


            SaveEnergyAndTimestamp(xtcFile)


		If WindowIncrement == SaveIncrement
			SaveWaveformtoXTC(xtcFile, TotalTimeWindow, AllChannels)


 


Def TagEvents:
	Parfor each channel : 
		If Slope > MinimumSlope:
			CurrentWindowEvent=True
		Else:
			CurrentWindowEvent=False
	
		EventMatrix(channel, currentWindowIndex) = CurrentWindowEvent
	Return EventMatrix

Def ApplyFilter(Filter):
	For each i, sample :
		Temp = linearFunction(sample) * sample 
		Temp = temp* Filter(i) *Filter2(i) %More filters added if necessary
		Energy = sum(temp)
		%Details on operations required for this step
		% Can we pipeline the linear function with the multiply and add?

Benchmarks

This table lists the latest benchmarks.

Currently, it evaluates the average time (over 1e6 iterations) to complete the number of products indicated as well as the sum of the resulting vector. The length of the vector varies according to the number of samples.

The code currently uses a simple decision tree which directly indicates how many filters to use. In the future this tree will determine the case to use depending on which windows have a hit.

These benchmarks were obtained os psanagpu116, using O3 and vectored optimization.

Compile flags

g++ -std=c++11 -O3 -DNDEBUG -march=native -IEigen main.cpp

All values are in μs.

Number of products Number of samples	1	2	3	4
100	0.061	0.074	0.091	0.108
200	0.113	0.145	0.176	0.210
300	0.150	0.190	0.241	0.296
400	0.200	0.266	0.329	0.397
500	0.239	0.321	0.402	0.486
600	0.279	0.378	0.476	0.576
700	0.318	0.435	0.549	0.667

Tasks

Task	Status	Comment
Create fake data vectors	NOT STARTED
Implement event detection algorithm	NOT STARTED
Program arguments for benchmarking	IN PROGRESS	Arguments for the size of the vectors and the number of array products used
Decision tree for event cases	IN PROGRESS	Switch case tree for given number of filters - reusable for different cases
Input data from simulation	NOT STARTED

Page tree

TES Readout Implementation

Description of TES

PseudoCode

Benchmarks

Tasks