Dataflow

Résumé

In computing, dataflow is a broad concept, which has various meanings depending on the application and context. In the context of software architecture, data flow relates to stream processing or reactive programming. Software architecture Dataflow computing is a software paradigm based on the idea of representing computations as a directed graph, where nodes are computations and data flow along the edges. Dataflow can also be called stream processing or reactive programming. There have been multiple data-flow/stream processing languages of various forms (see Stream processing). Data-flow hardware (see Dataflow architecture) is an alternative to the classic von Neumann architecture. The most obvious example of data-flow programming is the subset known as reactive programming with spreadsheets. As a user enters new values, they are instantly transmitted to the next logical "actor" or formula for calculation. Distributed data flows have also been proposed as a programming ab

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Dataflow process networks provide a versatile model of computation for specifying signal processing applications in a platform independent fashion. This attractive feature of dataflow has lately been realized in dataflow programming tools that allow synthesizing the same application specification as both fixed hardware circuits and as software for programmable processors. However, in practice, the specification granularity of the dataflow program remains an arbitrary choice of the designer. Dataflow specifications of the same application with equivalent I/O behaviour can range from a single dataflow actor to a very fine grained network composed of elementary processing operations. A very fine grained dataflow specification might result into a high performance implementation when synthesized as hardware, but might perform poorly when executed on a programmable processor. This article presents actor merging as one solution for this performance portability problem of dataflow programs. In contrast to previous work around actor merging, this article presents a methodology that can merge also dynamic dataflow actors. To support these claims, results of experiments on several processing platforms and application examples ranging from telecommunications to video compression are reported.

Ieee-Inst Electrical Electronics Engineers Inc2015

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Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Aurélien François Gilbert Bloch, Simone Casale Brunet, Marco Mattavelli

Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental components and features of both the design and of the platform to maximize the overall performance. The dataflow programming approach has proven to be an appropriate methodology for reaching such a difficult and complex goal for the intrinsic portability and the possibility of easily decomposing a network of actors on different processing units of the heterogeneous hardware. Nonetheless, such a design method might not be enough on its own to achieve the desired performance goals, and supporting tools are useful to be able to efficiently explore the design space so as to optimize the desired performance objectives. This article presents a methodology composed of several stages for enhancing the performance of dataflow software developed in RVC-CAL and generating low-level implementations to be executed on GPU/CPU heterogeneous hardware platforms. The stages are composed of a method for the efficient scheduling of parallel CUDA partitions, an optimization of the performance of the data transmission tasks across computing kernels, and the exploitation of dynamic programming for introducing SIMD-capable graphics processing unit systems. The methodology is validated on both the quantitative and qualitative side by means of dataflow software application examples running on platforms according to various different mapping configurations.

MDPI2022

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

Endri Bezati, Aurélien François Gilbert Bloch, Marco Mattavelli

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.

2019

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