Programming Models and Methods for Heterogeneous Parallel Embedded Systems

Casale-Brunet, Simone; Bezati, Endri; Mattavelli, Marco

doi:10.1109/mcsoc.2016.39

Cited by 3 publications

(1 citation statement)

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“…In this work, the Xronos backend [3] is used. It generates from an RVC-CAL description, a C++ implementation with all the necessary library dependencies.…”

Section: Dataflow Compilermentioning

confidence: 99%

Composite Data Types in Dynamic Dataflow Languages as Copyless Memory Sharing Mechanism

Bloch

Bezati

Mattavelli

2019

Lecture Notes in Computer Science

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This paper presents new optimization approaches aiming at reducing the impact of memory accesses on the performance of dataflow programs. The approach is based on introducing a high level management of composite data types in dynamic dataflow programming language for the memory processing of data tokens. It does not require essential changes to the model of computation (MOC) or to the dataflow program itself. The objective of the approach is to remove the unnecessary constraints of memory isolations without introducing limitations to the scalability and composability properties of the dataflow paradigm. Thus the identified optimizations allow to keep the same design and programming philosophy of dataflow, whereas aiming at improving the performance of the specific configuration implementation. The different optimizations can be integrated into the current RVC-CAL design flows and synthesis tools and can be applied to different sub-networks partitions of the dataflow program. The paper introduces the context, the definition of the optimization problem and describes how it can be applied to dataflow designs. Some examples of the optimizations are provided.

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“…In this work, the Xronos backend [3] is used. It generates from an RVC-CAL description, a C++ implementation with all the necessary library dependencies.…”

Section: Dataflow Compilermentioning

confidence: 99%

Composite Data Types in Dynamic Dataflow Languages as Copyless Memory Sharing Mechanism

Bloch

Bezati

Mattavelli

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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Methodologies for Synthesizing and Analyzing Dynamic Dataflow Programs in Heterogeneous Systems for Edge Computing

Bloch

Casale-Brunet

Mattavelli

2021

IEEE Open J. Circuits Syst.

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The possibility of using the increasing computing power available in cloud infrastructures requires the development of new approaches for application software development and optimization. Emerging edge computing paradigms offer the possibility of reducing bandwidth needs and of optimizing latency, features particularly relevant for Big Data applications, by bringing computation closer to the user and to the data generation processes. However, edge computing approaches pose several challenges in terms of how to be able to efficiently take advantage of a distributed network of heterogeneous processing nodes. This paper deals with this problem by extending a dynamic dataflow software development framework and related design flow tools to support heterogeneous platforms. The paper describes the methodology steps for the synthesis of application software executing on heterogeneous CPU/GPU co-processing nodes. The steps do include the optimization of the communication between heterogeneous processing elements, a technique for the efficient mapping and parallelization of computation on independent GPU partitions, and the introduction of dynamic programming approach for leveraging the SIMD nature of GPU computing. To complete the methodology of seamless porting of dataflow software and partition on CPU or GPU computing nodes, an automated methodology for exploring the configuration space and to identify high performance working points is developed.

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