Scheduling Optimisations for SPIN to Minimise Buffer Requirements in Synchronous Data Flow

Synchronous Data flow (SDF) graphs have a simple and elegant semantics (essentially linear algebra) which makes SDF graphs eminently suitable as a vehicle for studying scheduling optimisations. We extend related work on using SPIN to experiment with scheduling optimisations aimed at minimising buffer requirements.We show that for a benchmark of commonly used case studies the performance of our SPIN based scheduler is comparable to that of state of the art research tools. The key to success is using the semantics of SDF to prove when using (even unsound and/or incomplete) optimisations are justified. The main benefit of our approach lies in gaining deep insight in the optimisations at relatively low cost

Verifying Modal Workflow Specifications Using Constraint Solving

International audienceNowadaysworkflowsareextensivelyusedbycompaniestoimproveorganizationalefficiencyandproductivity.Thispaperfocusesontheverificationofmodalworkflowspecificationsusingconstraintsolvingasacomputationaltool.ItsmaincontributionconsistsindevelopinganinnovativeformalframeworkbasedonconstraintsystemstomodelexecutionsofworkflowPetrinetsandtheirstructuralproperties,aswellastoverifytheirmodalspecifications.Finally,animplementationandpromisingexperimentalresultsconstituteapracticalcontribution

Scheduling Optimisations for SPIN to Minimise Buffer Requirements in Synchronous Data Flow:(with appendix)

Synchronous Data flow (SDF) graphs have a simple and elegant semantics (essentially linear algebra) which makes SDF graphs eminently suitable as a vehicle for studying scheduling optimisations. We extend related work on using SPIN to experiment with scheduling optimisations aimed at minimising buffer requirements. We show that for a benchmark of commonly used case studies the performance of our SPIN based scheduler is comparable to that of state of the art research tools. The key to success is using the semantics of SDF to prove when using (even unsound and/or incomplete) optimisations are justified. The main benefit of our approach lies in gaining deep insight in the optimisations at relatively low cost

Partial-order-based process mining: a survey and outlook

The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms

A Formally Verified Compiler for Lustre

International audienceThe correct compilation of block diagram languages like Lustre, Scade, and a discrete subset of Simulink is important since they are used to program critical embedded control software. We describe the specification and verification in an Interactive Theorem Prover of a compilation chain that treats the key aspects of Lustre: sampling, nodes, and delays. Building on CompCert, we show that repeated execution of the generated assembly code faithfully implements the dataflow semantics of source programs.We resolve two key technical challenges. The first is the change from a synchronous dataflow semantics, where programs manipulate streams of values, to an imperative one, where computations manipulate memory sequentially. The second is the verified compilation of an imperative language with encapsulated state to C code where the state is realized by nested records. We also treat a standard control optimization that eliminates unnecessary conditional statements

Essentials of computing systems

Computers were invented to “compute“, i.e., to solve all sort of mathematical problems. A computer system contains hardware and systems software that work together to run software applications. The underlying concepts that support the construction of a computer are relatively stable. In fact, (almost) all computer systems have a similar organization, i.e., their hardware and software components are arranged in hierarchical layers (or levels) and perform similar functions. This book is written for programmers and software engineers who want to understand how the components of a computer work and how they affect the correctness and performance of their programs.Publishe