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LCTES 2018
co-located with PLDI 2018

Finite-state machine-based scenario-aware dataflow (FSM-SADF) is a dynamic dataflow model of computation that combines streaming data and finite-state control. For the most part, it preserves the determinism of its underlying synchronous dataflow (SDF) concurrency model and only when necessary introduces the non-deterministic variation in terms of scenarios that are represented by SDF graphs. This puts FSM-SADF in a sweet spot in the trade-off space between expressiveness and analyzability. However, FSM-SADF supports no notion of compositionality, which hampers its usability in modeling and consequent analysis of large systems. In this work we propose a compositional semantics for FSM-SADF that overcomes this problem. We base the semantics of the composition on standard composition of processes with rendezvous communication in the style of CCS or CSP at the control level and the parallel, serial and feedback composition of SDF graphs at the dataflow level. We evaluate the approach on a case study from the multimedia domain.

Tue 19 Jun
Times are displayed in time zone: (GMT-04:00) Eastern Time (US & Canada) change

11:00 - 12:15: LCTES 2018 - Full paper session on Programming Languages at Discovery AB
LCTES-2018-papers11:00 - 11:25
James DevineLancaster University, Joe Finney, Peli de HalleuxMicrosoft Research, Michał MoskalMicrosoft Research, Thomas BallMicrosoft Research, Steve HodgesMicrosoft
LCTES-2018-papers11:25 - 11:50
Mladen SkelinEindhoven University of Technology, Marc GeilenEindhoven University of Technology
LCTES-2018-papers11:50 - 12:15
Guilherme F. LimaPUC-Rio, Rodrigo C. M. SantosPUC-Rio, Edward Hermann HaeuslerPUC-Rio, Roberto IerusalimschyPUC-Rio, Francisco Sant'AnnaRio de Janeiro State University