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![Page 1: Modeling and Analysis of Printer Data Paths using Synchronous Data Flow Graphs in Octopus Ashwini Moily Under the supervision of Dr. Lou Somers, Prof.](https://reader035.fdocuments.net/reader035/viewer/2022062515/56649f455503460f94c67913/html5/thumbnails/1.jpg)
Modeling and Analysis of Printer Data Paths using Synchronous Data Flow
Graphs in OctopusAshwini Moily
Under the supervision of
Dr. Lou Somers, Prof. Dr. Twan Basten, Dr. Nikola Trčka
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Outline of the presentation
• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Introduction
• Research is carried out at Océ and ESI• Part of the Octopus project• Joint collaboration between Océ, ESI and
several other Dutch academic research groups.
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Octopus Toolset
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Printer Data flow path
scanner
DF
print process
finisher
paper trays
Datapath
network
Cost price
Productivity(speed)
Print imageQuality
Features
design Time to market
• Image pipelines for different use cases
scannercorrections
resample,rotate,
histogramfiltering
contrastenhancement
print enginecorrections,halftoning
Figure 1: Data flow in a printer at Océ Courtesy: Océ Technologies B.V5
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Octopus
• Early design decisions• Design Space Exploration• Model driven approach• Y- chart methodology
PlatformApplication
Mapping
Diagnostics
Analysis
Figure 2 : The Y- chart[1]
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Octopus
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Figure 3a : Conceptual architecture of the integrated framework in Octopus
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Octopus toolset
Figure 3b : Architecture of the toolset in Octopus9
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Problem description
Figure 4 : Architecture of the toolset in Octopus11
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Problem Statement
RASDF graph
DSEIR model
• Conservative translation w.r.to throughput • Guaranteed worst-case throughput for the
given model
Figure 5 : Translation to be achieved
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• Introduction• Octopus• Problem Statement• Tools used
• DSEIR• RASDF• SDF3• ResVis
• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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DSEIR
• Design Space Exploration Intermediate Representation
• Modeling language used to specify models• Components analogous to Y-chart:
• Application− Tasks− Loads / handovers
• Platform− Resources− Services
• Mapping− Schedulers− Priority
PlatformApplication
Mapping
Diagnostics
Analysis
Figure 6 : The Y- chart[1]
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Application
Task
Port
Edge
Task parameters
Tokens
Task guards
Edge condition
Value sent over edge
Binding expression
Task name
Global variable
Figure 7 : Tasks and edges in DSEIR
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Port condition
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Platform
• Resource• Name• Capacity• Speed• Type of service
Capacity
Name
Speed
Type of service
Figure 8 : Platform component in DSEIR
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Load/ handover
Figure 9 : Load perspective in DSEIR
Handover
Amount required
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• Load : Amount of service required by a particular task• Handover : Amount of service handed over to the
subsequent task
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Mapping
• Schedulers : Map services to resources• Priorities : For each task instance• Pre emption : Allowed
Fig 10 :Schedulers and resources for each service Fig 11: Schedulers and tasks with priorities
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• Introduction• Octopus• Problem Statement• Tools used
• DSEIR• RASDF• SDF3• ResVis
• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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RASDF
• Resource Aware Synchronous Data flow graphs• Allow for design time analysis of multi processor
systems
Figure 12 : An example of SDF[3]
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Example of a RASDF graph
Fig 13: Example of an RASDF graph
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• Introduction• Octopus• Problem Statement• Tools used
• DSEIR• RASDF• SDF3• ResVis
• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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SDF3
• Simulation tool used to analyze RASDF/ SDF graphs.• Very fast throughput analysis• Generates simulation traces ResVis.
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• Introduction• Octopus• Problem Statement• Tools used
• DSEIR• RASDF• SDF3• ResVis
• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Trace file in ResVis
Figure 14: Visualization of SDF3 trace files
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Approach used for translation
RASDF graph
RASDF graph
DSEIR-RASDFDSEIR-RASDF
DSEIR
Figure 15 : Translation methodolgy
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DSEIR-RASDF
• Restrictive subset of DSEIR• 1:1 mapping to RASDF• Features
• Application− No edge, port or task condition− No token values− No parameters− No global variables
• Platform− Fixed values; no distributions
• Mapping− Non-preemption− Static priority− No expressions
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DSEIR-RASDF to RASDF
• Translation• Application
− Tasks Actors− Edges Channels− Ports Input ports− Tokens Rates
• Platform − Resources Resources
• Mapping− Schedulers (with task and resource information)
resource for each task − Priority priority
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RASDF to DSEIR-RASDF to RASDF
• Testing purposes• RASDF to DSEIR-RASDF
• Application− Actors Tasks− Edges Channels− Input ports Ports− Rates of ports tokens on ports
• Platform− Resource resources
• Mapping− Task + resources schedulers
(Assumption : each task has an unique scheduler)
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RASDF to SDF3 to ResVis
• SDF3: Worst case guaranteed throughput for the DSEIR model
• Visualization of job traces with ResVis• RASDF SDF3 ResVis connection in
Octopus
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with DSEIRDSEIR-RASDF translation• Completed objectives• Planned objectives
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Issues with DSEIR DSEIR-RASDF translation
1. SDF3: Very fast memory efficient conservative throughput
2. DSEIR is more expressive than RASDF1. Data dependent parameters
2. Variable load/ actor execution times
3. Data dependent choices
4. Data dependent loops
5. Variable production/consumption rates of data
6. Scheduling differences
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Issues
• Data dependent parameters and variable load/ actor execution time• Solution:
− No data dependent values− MinMaxExtractor: extract range− Could lead to: non-monotone models
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Non–monotone behavior
• Variable execution time of actors• Maximum execution time does not guarantee worst
case behavior• Detection of this behavior is extremely difficult
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Non monotone behavior
Figure 16: An example of RASDF
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Non monotone behavior
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50.15
0.16
0.17
0.18
0.19
0.2
0.21
Actor B
Execution time
Th
rou
gh
pu
t
• Simulation traces
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Issues
• Data dependent choices• Selecting one among the choices OR• Executing all choices and comparing throughput
behavior
• Scheduling differences• Static non-preemptive scheduling
• Variable production/consumption rates of data• Worst case behavior
• Data dependent loops
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Not yet solved
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Completed objectives
• DSEIR-RASDF RASDF SDF3• RASDF DSEIR-RASDF RASDF SDF3• DSEIR DSEIR-RASDF RASDF SDF3ResVis
translation that gives a set of models for varying execution times
• Verify the translation using the different use cases of the printers
• Use SDF3 to detect non-monotone behavior
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• Introduction• Octopus• Problem Statement• Tools used• Translation procedures• Issues with translation• Completed objectives• Planned objectives
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Objectives that are yet to be realized
• Implement a smart algorithm that detects non monotone behavior using SDF3 without having to search the entire state space.
• Try to implement an algorithm that will detect non monotone behavior by static analysis of the DSEIR model (without using SDF3)
• Implement checks to detect the type of models that can or cannot be translated. Extend this further to accommodate more models.
• Create a plug-in that will read DSEIR models from the editor and directly produce the throughput behavior for the model.
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Questions/ Feedback
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Thank you
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