Abstract - Boolean Parametric Data Flow
Dataflow programming models are well-suited to program many-core
streaming applications. However, many streaming applications have a
dynamic behavior. To capture this behavior, parametric dataflow models
have been introduced over the years. Still, such models do not allow
the topology of the dataflow graph to change at runtime, a feature
that is also required to program modern streaming applications.
To overcome these restrictions, we propose a new model of computation,
the Boolean Parametric Data Flow (BPDF) model which combines
integer parameters (to express dynamic rates) and boolean parameters
(to express the activation and deactivation of communication
The major challenge with such dynamic models is to guarantee liveness
and boundedness. We present static analyses which ensure statically the
liveness and the boundedness of BDPF graphs.
Finally, a dynamic model like BPDF creates new challenges when it comes to its
We present a flexible approach to produce parallel schedules for BPDF
applications on many core platforms.
Vagelis Bebelis is a PhD student at INRIA Grenoble in
collaboration with STMicroelectronics. He is working on models of
computation and more specifically data flow models. Before his PhD, he
finsihed a bachelor on electrical engineering at the University of
Patras, Greece and a master on embedded systems from the same
university. He carried out his master thesis in IMEC Leuven, Belgium
working on power optimization of the front-end of 60GHz radio.