The Experts below are selected from a list of 405312 Experts worldwide ranked by ideXlab platform
Sanjoy Baruah - One of the best experts on this subject based on the ideXlab platform.
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applying real time scheduling theory to the synchronous Data Flow Model of computation
Euromicro Conference on Real-Time Systems, 2017Co-Authors: Abhishek Singh, Pontus Ekberg, Sanjoy BaruahAbstract:Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are Modeled using the synchronous Data-Flow graph (SDFG) Model. An enhancement to the standard SDFG Model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG Model.
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ECRTS - Applying Real-Time Scheduling Theory to the Synchronous Data Flow Model of Computation
2017Co-Authors: Abhishek Singh, Pontus Ekberg, Sanjoy BaruahAbstract:Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are Modeled using the synchronous Data-Flow graph (SDFG) Model. An enhancement to the standard SDFG Model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG Model.
Sander Stuijk - One of the best experts on this subject based on the ideXlab platform.
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a scenario aware Data Flow Model for combined long run average and worst case performance analysis
International Conference on Formal Methods and Models for Co-Design, 2006Co-Authors: B D Theelen, Marc Geilen, Twan Basten, J P M Voeten, Stefan Valentin Gheorghita, Sander StuijkAbstract:Data Flow Models are used for specifying and analysing signal processing and streaming applications. However, traditional Data Flow Models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which Data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous Data Flow Model, which uses a stochastic approach to Model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware Data Flow Model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware Data Flow Models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example.
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MEMOCODE - A scenario-aware Data Flow Model for combined long-run average and worst-case performance analysis
Fourth ACM and IEEE International Conference on Formal Methods and Models for Co-Design 2006. MEMOCODE '06. Proceedings., 1Co-Authors: B D Theelen, Marc Geilen, Twan Basten, J P M Voeten, Stefan Valentin Gheorghita, Sander StuijkAbstract:Data Flow Models are used for specifying and analysing signal processing and streaming applications. However, traditional Data Flow Models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which Data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous Data Flow Model, which uses a stochastic approach to Model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware Data Flow Model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware Data Flow Models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example.
Arnault Lapitre - One of the best experts on this subject based on the ideXlab platform.
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Correction to: PolyGraph: a Data Flow Model with frequency arithmetic
International Journal on Software Tools for Technology Transfer, 2020Co-Authors: Paul Dubrulle, Nikolai Kosmatov, Christophe Gaston, Arnault LapitreAbstract:The publication of this article unfortunately contained a mistake.
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PolyGraph: a Data Flow Model with frequency arithmetic
International Journal on Software Tools for Technology Transfer, 2020Co-Authors: Paul Dubrulle, Nikolai Kosmatov, Christophe Gaston, Arnault LapitreAbstract:Data Flow formalisms are commonly used to Model systems in order to solve problems of buffer sizing and task scheduling. A prerequisite for static analysis of a Modeled system is the existence of a periodic schedule in which the sizes of communication channels can be bounded for an unbounded execution (consistency), and that communication dependencies do not introduce a deadlock in such an execution (liveness). In the context of Cyber-Physical Systems, components are often interfaced with the physical world and have frequency constraints. The existing Data Flow formalisms lack expressiveness to fully cover the expected behavior of these components. We propose an extension to static Data Flow paradigms, called PolyGraph, that includes frequency constraints and adjustable communication rates. We show that with these extensions, the conditions for a Model to be consistent and live are no longer sufficient, and we extend the corresponding theorems with necessary and sufficient conditions to preserve these properties. We illustrate how PolyGraph can be used in practice on a realistic Advanced Driver Assistance System, and present a framework to check PolyGraph properties in the tool DIVERSITY, along with experiments on realistic and random Models.
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FASE - A Data Flow Model with Frequency Arithmetic
Fundamental Approaches to Software Engineering, 2019Co-Authors: Paul Dubrulle, Nikolai Kosmatov, Christophe Gaston, Arnault Lapitre, Stephane LouiseAbstract:Data Flow formalisms are commonly used to Model systems in order to solve problems of buffer sizing and task scheduling. A prerequisite for static analysis of a Modeled system is the existence of a periodic schedule in which the sizes of communication channels can be bounded for an unbounded execution (consistency), and that communication dependencies do not introduce a deadlock in such an execution (liveness). In the context of Cyber-Physical Systems, components are often interfaced with the physical world and have frequency constraints. The existing Data Flow formalisms lack expressiveness to fully cover the expected behavior of these components. We propose an extension to Synchronous Data Flow (SDF) formalism, called Polygraph, that includes frequency constraints and adjustable communication rates. We show that with these extensions, the conditions for a Model to be consistent and live are no longer sufficient, and we extend the corresponding theorems with necessary and sufficient conditions to preserve these properties. We also introduce a framework to check the liveness of a Polygraph Model, implemented in the tool DIVERSITY, along with preliminary experiments to validate this approach.
Yasser Y. Hanafy - One of the best experts on this subject based on the ideXlab platform.
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FCCM - High Performance Sparse LU Solver FPGA Accelerator Using a Static Synchronous Data Flow Model
2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines, 2015Co-Authors: Mohamed W. Hassan, Ahmed E. Helal, Yasser Y. HanafyAbstract:Sparse LU solvers are common in several scientific problems. The hardware utilization of previous implementations on massively parallel platforms never exceeded the 20% mark (including multicores, GPU, and FPGA). This is due to the highly irregular computation and memory access pattern of the algorithm. Reconfigurable fabrics, with its spatial execution Model, can expose the maximum inherent parallelism in the problem and achieve the highest hardware utilization. However, dynamic Data Flow Models implementations suffer from large overhead and scalability issues. In this paper, we propose a static DataFlow synchronous Model that maximizes the utilization of FPGA-based architectures. Synchronous DataFlow graph is mapped to a mesh of deeply-pipelined PEs to perform the factorization. This inspires the development of a customized Data structure format that reduces memory accesses, indexing overhead and pipelining hazards. The hardware Model is synthesized on a VIRTEX 7 FPGA and the results show a hardware utilization exceeding 60%, which was translated to more than 100 GFLOPS.
Abhishek Singh - One of the best experts on this subject based on the ideXlab platform.
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applying real time scheduling theory to the synchronous Data Flow Model of computation
Euromicro Conference on Real-Time Systems, 2017Co-Authors: Abhishek Singh, Pontus Ekberg, Sanjoy BaruahAbstract:Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are Modeled using the synchronous Data-Flow graph (SDFG) Model. An enhancement to the standard SDFG Model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG Model.
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ECRTS - Applying Real-Time Scheduling Theory to the Synchronous Data Flow Model of Computation
2017Co-Authors: Abhishek Singh, Pontus Ekberg, Sanjoy BaruahAbstract:Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are Modeled using the synchronous Data-Flow graph (SDFG) Model. An enhancement to the standard SDFG Model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG Model.
