Optimal Implementation of Simulink Models on Multicore Architectures with Partitioned Fixed Priority Scheduling
| dc.contributor.author | Bansal, Shamit | en |
| dc.contributor.committeechair | Zeng, Haibo | en |
| dc.contributor.committeemember | Patterson, Cameron D. | en |
| dc.contributor.committeemember | Schaumont, Patrick R. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2018-09-21T08:00:16Z | en |
| dc.date.available | 2018-09-21T08:00:16Z | en |
| dc.date.issued | 2018-08-02 | en |
| dc.description.abstract | Model-based design based on the Simulink modeling formalism and the associated toolchain has gained its popularity in the development of complex embedded control systems. However,the current research on software synthesis for Simulink models has a critical gap for providing a deterministic, semantics-preserving implementation on multicore architectures with partitioned fixed-priority scheduling. In this thesis, we propose to judiciously assign task offset, task priority, and task communication mechanism, to avoid simultaneous access to shared memory by tasks on different cores, to preserve the model semantics, and to optimize the control performance. We develop two approaches to solve the problem: (a) a mixed integer linear programming (MILP) formulation; and (b) a problem specific exact algorithm that may run several magnitudes faster than MILP. | en |
| dc.description.abstractgeneral | To save development time and money, automotive industries have been developing models using software, before implementing them directly on hardware. For reliability, the model generated from the software tool should behave in a well defined manner, coherent to the ideal design of the model. While the current tools are able to generate this reliable model for a single processor system, they are not able to do so for a system with multiple processors. When two or more processors contend to access the same resource at the same time, the existing tools are unable to provide a well defined execution order in their model. Since modern embedded systems need multiple processors to meet their increasing performance demands, it is imperative that the software tools scale up to multiple processors as well. In this work, we seek to bridge this gap by presenting two solutions that generate a deterministic software implementation of a system with multiple processors. In our solutions, we generate a model with well defined execution order by ensuring that at any given time, only one processor accesses a given resource. Furthermore, apart from ensuring determinism, we also improve upon the performance of the generated model by ensuring that there is minimal end-to-end latency in the system. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:15509 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/85057 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Simulink | en |
| dc.subject | Multicore | en |
| dc.subject | Software Synthesis | en |
| dc.subject | Partitioned scheduling | en |
| dc.title | Optimal Implementation of Simulink Models on Multicore Architectures with Partitioned Fixed Priority Scheduling | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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