Alessio Bonfietti

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Our work focuses on allocating and scheduling a synchronous data-flow (SDF) graph onto a multi-core platform subject to a minimum throughput requirement. This problem has traditionally be tackled by incomplete approaches based on problem decomposition and local search, which could not guarantee optimality. Exact algorithms used to be considered reasonable(More)
Stream (data-flow) computing is considered an effective paradigm for parallel programming of high-end multi-core architectures for embedded applications (networking, multimedia, wireless communication). Our work addresses a key step in stream programming for embedded multicores, namely, the efficient mapping of a synchronous data-flow graph (SDFG) onto a(More)
A cyclic scheduling problem is specified by a set of activities that are executed an infinite number of times subject to precedence and resource constraints. The cyclic scheduling problem has many applications in manufacturing, production systems, embedded systems, compiler design and chemical systems. This paper proposes a Constraint Programming approach(More)
We present MPOpt-Cell, an architecture-aware framework for high-productivity development and efficient execution of stream applications on the CELL BE Processor. It enables developers to quickly build Synchronous Data Flow (SDF) applications using a simple and intuitive programming interface based on a set of compiler directives that capture the key(More)
In past papers, we have introduced Empirical Model Learning (EML) as a method to enable Combinatorial Optimization on real world systems that are impervious to classical modeling approaches. The core idea in EML consists in embedding a Machine Learning model in a traditional combinatorial model. So far, the method has been demonstrated by using Neural(More)
Data-Flowmodels are attracting renewed attention because they lend themselves to efficientmapping on multi-core architectures. The key problemof finding amaximum-throughput allocation and scheduling of Synchronous Data-Flow graphs (SDFGs) onto amulti-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms(More)
Resource constrained cyclic scheduling problems consist in planning the execution over limited resources of a set of activities, to be indefinitely repeated. In such a context, the iteration period (i.e. the difference between the completion time of consecutive iterations) naturally replaces the makespan as a quality measure; exploiting inter-iteration(More)
In the context of Scheduling under uncertainty, Partial Order Schedules (POS) provide a convenient way to build flexible solutions. A POS is obtained from a Project Graph by adding precedence constraints so that no resource conflict can arise, for any possible assignment of the activity durations. In this paper, we use a simulation approach to evaluate the(More)
This paper proposes a global cumulative constraint for cyclic scheduling problems. In cyclic scheduling a project graph is periodically re-executed on a set of limited capacity resources. The objective is to find an assignment of start times to activities such that the feasible repetition period λ is minimized. Cyclic scheduling is an effective method to(More)