GRAPHICAL PARTITIONING FOR PARALLEL EXECUTION OF EXECUTABLE BLOCK DIAGRAM MODELS

• US 20110078652A1
• Filed: 09/30/2010
• Published: 03/31/2011
• Est. Priority Date: 05/31/2005
• Status: Active Grant

• Current Assignee: The MathWorks, Inc.
• Original Assignee: The MathWorks, Inc.
• Inventors: SIMSEK, Hidayet Tunc, MANI, Ramamurthy, POPOVICI, Katalin Maria, MARTIN, Benjamin Charles, CIOLFI, John Edward
• Granted Patent: US 8,756,044 B2
• Time in Patent Office: Days
• US Class Current: 717/105
• CPC Class Codes: G06F 8/10 Requirements analysis; Specification techniques
• CPC Class Codes: G06F 8/34 Graphical or visual programming
• CPC Class Codes: G06F 8/35 model driven
• CPC Class Codes: G06F 9/5066 Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs mappping at compile time, see G06F8/451

Abstract

Exemplary embodiments allow executable graphical models, such as block diagram models, to be graphically partitioned for execution on concurrent computing resources. Embodiments allow model components to be grouped into subtasks that are affiliated with tasks associated with concurrent computing resources. Tasks and sub graphs can be mapped to concurrent computing resources according to characteristics, such as sample time, solver type, etc. Embodiments further allow mappings to be visually indicated to a user via various display techniques including color, text, icons, shading, grouping of identifiers, etc. Concurrently executing portions of a model allows model results to be obtained faster than can be obtained when models are executed on a single computing resource, such as a single processor.

Claims

1. A computer-implemented method comprising:

interacting with an executable block diagram model, where the model includes a first component and a second component;

partitioning the model graphically, where the partitioning associates;

a first sub graph with the first component, where the first sub graph performs a first operation when the model executes, and

a second sub graph with the second component, where the second sub graph performs a second operation when the model executes;

identifying a task group component, where the task group component includes;

a first task identifier, where;

the first task identifier indentifies a first task,

the first task is associated with a first concurrent computing resource, where the first task executes on the first concurrent computing resource when the model executes,

the first task identifier is associated with the first sub graph, and

the associating allows the first sub graph to execute on the first concurrent computing resource when the model executes, and

a second task identifier, where

the second task identifier indentifies a second task,

the second task is associated with a second concurrent computing resource, where the second task executes on the second concurrent computing resource when the model executes,

the second task identifier is associated with the second sub graph, and

the associating allows the second sub graph to execute on the second concurrent computing resource when the model executes; and

executing the model, where the executing;

executes the first sub graph on the first resource,

executes the second sub graph on the second resource, where the second sub graph concurrently executes when the first sub graph is executing on the first resource, and

produces an execution result.

2. The method of claim 1, where:

the sub graphs are associated with sub graph identifiers, and

task identifiers or the sub graph identifiers graphically represent a number of concurrent computing resources that include the first concurrent computing resource and the second concurrent computing resource.

3. The method of claim 1, where:

sub graphs have sub graph identifiers for visually indicating the sub graphs, and

the sub graph identifiers are used to map the sub graphs to the first and second concurrent computing resources.

4. The method of claim 1, where the partitioning comprises hierarchically partitioning the model using the components.

5. The method of claim 1, where:

the first task has a first sample time and the second task has a second sample time, and

the model is partitioned based on the first sample time or the second sample time.

6. The method of claim 5, where the first sample time and the second sample time are the same.

7. The method of claim 1, where the first task is a continuous task or the second task is a continuous task.

8. The method of claim 1, where the model is interpretively executed.

9. The method of claim 1, further comprising:

generating code for the model prior to the executing, where the executing executes the generated code.

10. The method of claim 9, where the generated code is configured for real-time execution on the concurrent computing resources.

11. The method of claim 10, where the concurrent computing resources reside in one or more target environments.

12. The method of claim 1, where the concurrent computing resources are threads or processes of an operating system operating on a parallel platform.

13. The method of claim 1, where the concurrent computing resources are cores.

14. The method of claim 1, where the model is partitioned in response to a user input.

15. The method of claim 1, where:

the tasks are mapped to sub graphs using a programmatic application program interface (API), or

the sub graphs are mapped to the concurrent computing resources using the programmatic API.

16. One or more non-transitory computer-readable media holding executable instructions that when executed on a processor partition a model for concurrent processing, the media holding one or more instructions for:

interacting with an executable block diagram model, where the model includes a first component and a second component;

partitioning the model graphically, where the partitioning associates;

a first sub graph with the first component, where the first sub graph performs a first operation when the model executes, and

a second sub graph with the second component, where the second sub graph performs a second operation when the model executes;

identifying a task group component, where the task group component includes;

a first task identifier, where;

the first task identifier indentifies a first task,

the first task is associated with a first concurrent computing resource, where the first task executes on the first concurrent computing resource when the model executes,

the first task identifier is associated with the first sub graph, and

the associating allows the first sub graph to execute on the first concurrent computing resource when the model executes, and

a second task identifier, where

the second task identifier indentifies a second task,

the second task is associated with a second concurrent computing resource, where the second task executes on the second concurrent computing resource when the model executes,

the second task identifier is associated with the second sub graph, and

the associating allows the second sub graph to execute on the second concurrent computing resource when the model executes; and

executing the model, where the executing;

executes the first sub graph on the first resource,

executes the second sub graph on the second resource, where the second sub graph concurrently executes when the first sub graph is executing on the first resource, and

produces an execution result.