Modeling and Analysis of Printer Data Paths using Synchronous Data Flow Graphs in Octopus

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

Outline of the presentation

• 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

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 contrast

enhancement

print enginecorrections,halftoning

Figure 1: Data flow in a printer at Océ Courtesy: Océ Technologies B.V5


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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

Octopus toolset

Figure 3b : Architecture of the toolset in Octopus9


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Problem description

Figure 4 : Architecture of the toolset in Octopus11

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

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

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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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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SDF3

• Simulation tool used to analyze RASDF/ SDF graphs.• Very fast throughput analysis• Generates simulation traces ResVis.

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Trace file in ResVis

Figure 14: Visualization of SDF3 trace files

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Approach used for translation

RASDF graph

DSEIR-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 parameters2. Variable load/ actor execution times3. Data dependent choices4. Data dependent loops5. Variable production/consumption rates of data6. 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

Thro

ughp

ut

• 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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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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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

Thank you

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Modeling and Analysis of Printer Data Paths using Synchronous Data Flow Graphs in Octopus

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