Couillard: Parallel programming via coarse-grained Data-flow Compilation

Marzulo, Leandro A. J.; Alves, Tiago A. O.; França, Felipe M. G.; Costa, Vítor Santos

doi:10.1016/j.parco.2014.10.002

Cited by 19 publications

(6 citation statements)

References 30 publications

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“…TALM is an execution model which provides an instruction set including all special instructions required for dataflow model such as the Steer and IncTag instructions, introduced in Section 2.2, in order to enable dataflow execution on top of von Neumann machines. It has a toolchain composed of Trebuchet (a virtual machine), Couillard (a dataflow compiler) 21 and an assembler that generates the binary code from the TALM assembly language to run on Trebuchet .…”

Section: Experimental Validation Considerationsmentioning

confidence: 99%

“…TALM 20 introduces a user‐defined coarse‐grained parallel data‐flow model, where programmers identify code blocks, called super‐instructions, to be run in parallel and connect them in a data‐flow graph. Using the Trebuchet as a TALM's implementation, the Couillard 21 is a full compiler that creates a dataflow graph based on a super‐instructions expressed in an annotated C‐program. In 2014, was proposed a Python library that provides a dataflow programming in a minimalistic and reasonably simple syntax: the Sucuri 22 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study

Mello

Araújo

Alves

et al. 2021

Concurrency and Computation

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With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the current generation of computers. In this context, dynamic dataflow and Gamma—General Abstract Model for Multiset mAnipulation—emerge as interesting computational model choices. In dynamic dataflow model, operations are performed as soon as their associated operands are available, without rely on a Program Counter to dictate the execution order of instructions. The Gamma paradigm is based on a parallel multiset rewriting scheme. It provides a nondeterministic execution model inspired by an abstract chemical machinemetaphor, where operations are formulated as reactions that occur freely among matching elements belonging to the multiset. In this work, equivalence relations between the dynamic dataflow and Gamma paradigms are exposed and explored, while methods to convert from dataflow to Gamma paradigm and vice versa are provided. It is shown that vertices and edges of a dynamic dataflow graph can correspond, respectively, to reactions and multiset elements in the Gamma paradigm. This work provides the scientific community with the possibility of taking profit of both parallel programming models, contributing with a versatility component to researchers and developers.

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Section: Experimental Validation Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study

Mello

Araújo

Alves

et al. 2021

Concurrency and Computation

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show abstract

“…Figure 5A shows the distribution only for reused subgraphs for the Eager-Match detection strategy with and without inspection (EM+I and EM, respectively). Each column shows the percentage of reused subgraphs with 2 to 4 nodes ( [2,4]), 5 to 9 nodes ( [5,9]), 10 to 49 nodes ( [10,49]), 50…”

Section: Of 12mentioning

confidence: 99%

“…In Dataflow, tasks are executed according to their dependencies, instead of following program order, thus allowing concurrent execution without the need of a program counter. Several recent investigations use the dataflow model as an appealing alternative for parallel programming that is more transparent to developers, while still providing the desirable performance . Moreover, dataflow architectures have been proposed by different research groups and are already a reality in the industry of high performance computing accelerators …”

Section: Introductionmentioning

confidence: 99%

DF‐DTM: Dynamic Task Memoization and reuse in dataflow

Rouberte

Sena

Nery

et al. 2018

Concurrency and Computation

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Summary Instruction Reuse is a technique adopted in Von Neumann architectures that improves performance by avoiding redundant execution of instructions when the result to be produced can be obtained by searching an input/output memoization table for such instruction. Trace reuse can be applied to traces of instructions in a similar fashion. However, those techniques are yet to be studied in the context of the Dataflow model, which has been gaining traction in the high performance computing community due to its inherent parallelism. Dataflow programs are represented by directed graphs where nodes are instructions or tasks and edges denote data dependencies between tasks. This work presents Dataflow Dynamic Task Memoization (DF‐DTM), a technique that allows the reuse of both nodes and subgraphs in dataflow, which are analogous to instructions and traces, respectively. The potential of DF‐DTM is evaluated by a series of experiments that analyze the behavior of redundant tasks in five relevant benchmarks, where up to 99.70% of the instantiated tasks could be reused. Moreover, this paper evaluates how reuse rates can be affected by limiting subgraph size, memoization table size, task granularity, and problem size, showing that DF‐DTM can yield good reuse rates in more realistic environments.

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“…The optimal partitioning is an NP-complete problem [51], in order to avoid time-consuming in automatic analysis of the code, the construction of the graph A for each application may be made in the design stage. The proposed characterization for CPS environments is based on a coarse-grained data flow approach which is a natural paradigm for describing digital signal processing applications for concurrent implementation on parallel hardware [57].…”

Section: Target Applicationsmentioning

confidence: 99%

Distributed computational model for shared processing on Cyber-Physical System environments

Mora

Colom

Gil

et al. 2017

Computer Communications

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A computation model to distribute the application workload over the CPS is proposed.  The scheduling method dynamically shares the tasks among the computing nodes of the CPS.  This approach facilitates the networking and integration of heterogeneous computer devices.

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Couillard: Parallel programming via coarse-grained Data-flow Compilation

Cited by 19 publications

References 30 publications

Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study

Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study

DF‐DTM: Dynamic Task Memoization and reuse in dataflow

Distributed computational model for shared processing on Cyber-Physical System environments

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