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. Dr. Twan Basten, Dr. Nikola Trčka

2. Outline of the presentation Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 2

3. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 3

4. 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. 4

5. Printer Data flow path scanner DF print process finisher paper trays Data path network Cost price Productivity (speed) Print image Quality Features design Time to market Image pipelines for different use cases Figure 1: Data flow in a printer at Océ Courtesy: Océ Technologies B.V 5

6. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 6

7. Octopus Early design decisions Design Space Exploration Model driven approach Y- chart methodology Platform Application Mapping Diagnostics Analysis Figure 2 : The Y- chart[1] 7

8. Octopus 8 Figure 3a : Conceptual architecture of the integrated framework in Octopus

9. Octopus toolset Figure 3b : Architecture of the toolset in Octopus 9

10. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 10

11. Problem description Figure 4 : Architecture of the toolset in Octopus 11

12. 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 12

13. Introduction Octopus Problem Statement Tools used DSEIR RASDF SDF3 ResVis Translation procedures Issues with translation Completed objectives Planned objectives 13

14. 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 Platform Application Mapping Diagnostics Analysis Figure 6 : The Y- chart[1] 14

15. 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 15 Port conditio n

16. Platform Resource Name Capacity Speed Type of service Capacity Name Speed Type of service Figure 8 : Platform component in DSEIR 16

17. Load/ handover Figure 9 : Load perspective in DSEIR Handover Amount required 17 Load : Amount of service required by a particular task Handover : Amount of service handed over to the subsequent task

18. 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 18

19. Introduction Octopus Problem Statement Tools used DSEIR RASDF SDF3 ResVis Translation procedures Issues with translation Completed objectives Planned objectives 19

20. RASDF Resource Aware Synchronous Data flow graphs Allow for design time analysis of multi processor systems Figure 12 : An example of SDF[3] 20

21. Example of a RASDF graph Fig 13: Example of an RASDF graph 21

22. Introduction Octopus Problem Statement Tools used DSEIR RASDF SDF3 ResVis Translation procedures Issues with translation Completed objectives Planned objectives 22

23. SDF3 Simulation tool used to analyze RASDF/ SDF graphs. Very fast throughput analysis Generates simulation traces  ResVis. 23

24. Introduction Octopus Problem Statement Tools used DSEIR RASDF SDF3 ResVis Translation procedures Issues with translation Completed objectives Planned objectives 24

25. Trace file in ResVis Figure 14: Visualization of SDF3 trace files 25

26. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 26

27. Approach used for translation RASDF graph DSEIR- RASDF DSEIR Figure 15 : Translation methodolgy 27

28. 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 28

29. 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 29

30. 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) 30

31. 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 31

32. Introduction Octopus Problem Statement Tools used Translation procedures Issues with DSEIR  DSEIR-RASDF translation Completed objectives Planned objectives 32

33. Issues with DSEIR  DSEIR-RASDF translation 1.SDF3: Very fast memory efficient conservative throughput 2.DSEIR is more expressive than RASDF 1.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 33

34. Issues Data dependent parameters and variable load/ actor execution time Solution: −No data dependent values −MinMaxExtractor: extract range −Could lead to: non-monotone models 34

35. Non–monotone behavior Variable execution time of actors Maximum execution time does not guarantee worst case behavior Detection of this behavior is extremely difficult 35

36. Non monotone behavior Figure 16: An example of RASDF 36

37. Non monotone behavior Simulation traces 37

38. 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 38 Not yet solved

39. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 39

40. Completed objectives DSEIR-RASDF  RASDF  SDF3 RASDF  DSEIR-RASDF  RASDF  SDF3 DSEIR  DSEIR-RASDF  RASDF  SDF3  ResVis 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 40

41. Introduction Octopus Problem Statement Tools used Translation procedures Issues with translation Completed objectives Planned objectives 41

42. 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. 42

43. Questions/ Feedback

44. Thank you 44