LUSTRE: a declarative language for real-time programming
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Efficient algorithms for optimum cycle mean and optimum cost to time ratio problems
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
StreamIt: A Language for Streaming Applications
CC '02 Proceedings of the 11th International Conference on Compiler Construction
A Hierarchical Multiprocessor Scheduling Framework for
A Hierarchical Multiprocessor Scheduling Framework for
Throughput Analysis of Synchronous Data Flow Graphs
ACSD '06 Proceedings of the Sixth International Conference on Application of Concurrency to System Design
Liveness and Boundedness of Synchronous Data Flow Graphs
FMCAD '06 Proceedings of the Formal Methods in Computer Aided Design
Throughput-Buffering Trade-Off Exploration for Cyclo-Static and Synchronous Dataflow Graphs
IEEE Transactions on Computers
Embedded Multiprocessors: Scheduling and Synchronization
Embedded Multiprocessors: Scheduling and Synchronization
Lucy-n: a n-synchronous extension of Lustre
MPC'10 Proceedings of the 10th international conference on Mathematics of program construction
IEEE Transactions on Signal Processing
A methodology for automated design of hard-real-time embedded streaming systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Cyclo-Static DataFlow Graphs (CSDFG in short) is a formalism commonly used to model parallel applications composed by actors communicating through buffers. The liveness of a CSDFG ensures that all actors can be executed infinitely often. This property is clearly fundamental for the design of embedded applications. This paper aims to present first an original sufficient condition of liveness for a CSDFG. Two algorithms of polynomial-time for checking the liveness are then derived and compared to a symbolic execution of the graph. An original method to compute close-to-optimal buffer capacities ensuring liveness is also presented and experimentaly tested. The performance of our methods are comparable to those existing in the literature for industrial applications. However, they are far more effective on randomly generated instances, ensuring their scalability for future more complex applications and their possible implementation in a compiler.