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Compositional Separation of Control Flow and Data Flow

Research output: Working paper › Preprint

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Compositional Separation of Control Flow and Data Flow. / Arellanes, Damian.
Arxiv, 2023.

Research output: Working paper › Preprint

Harvard

Arellanes, D 2023 'Compositional Separation of Control Flow and Data Flow' Arxiv. <https://arxiv.org/abs/2309.06397v3>

APA

Arellanes, D. (2023). Compositional Separation of Control Flow and Data Flow. Arxiv. https://arxiv.org/abs/2309.06397v3

Vancouver

Arellanes D. Compositional Separation of Control Flow and Data Flow. Arxiv. 2023 Dec 23.

Author

Arellanes, Damian. / Compositional Separation of Control Flow and Data Flow. Arxiv, 2023.

Bibtex

@techreport{0809ab140f9a484dbbcc35337f6b6206,
title = "Compositional Separation of Control Flow and Data Flow",
abstract = "Every Model of High-Level Computation (MHC) has an underlying composition mechanism for combining simple computation devices into more complex ones. Composition can be done by (explicitly or implicitly) defining control flow, data flow or any combination thereof. Control flow specifies the order in which individual computations are activated, whereas data flow defines how data is exchanged among them. Unfortunately, traditional MHCs either mix data and control or only consider one dimension explicitly, which makes it difficult to reason about data flow and control flow separately. Reasoning about these dimensions orthogonally is a crucial desideratum for optimisation, maintainability and verification purposes. In this paper, we introduce a novel MHC that explicitly treats data flow and control flow as separate dimensions, while providing modularity. As the model is rooted in category theory, it provides category-theoretic operations for compositionally constructing sequential, parallel, branchial or iterative composites. Compositionality entails that a composite exhibits the same properties as its respective constituents, including separation of concerns and modularity. We conclude the paper by demonstrating how our proposal can be used to model high-level computations in two different application domains: software engineering and artificial intelligence.",
author = "Damian Arellanes",
year = "2023",
month = dec,
day = "23",
language = "English",
publisher = "Arxiv",
type = "WorkingPaper",
institution = "Arxiv",

}

RIS

TY - UNPB

T1 - Compositional Separation of Control Flow and Data Flow

AU - Arellanes, Damian

PY - 2023/12/23

Y1 - 2023/12/23

N2 - Every Model of High-Level Computation (MHC) has an underlying composition mechanism for combining simple computation devices into more complex ones. Composition can be done by (explicitly or implicitly) defining control flow, data flow or any combination thereof. Control flow specifies the order in which individual computations are activated, whereas data flow defines how data is exchanged among them. Unfortunately, traditional MHCs either mix data and control or only consider one dimension explicitly, which makes it difficult to reason about data flow and control flow separately. Reasoning about these dimensions orthogonally is a crucial desideratum for optimisation, maintainability and verification purposes. In this paper, we introduce a novel MHC that explicitly treats data flow and control flow as separate dimensions, while providing modularity. As the model is rooted in category theory, it provides category-theoretic operations for compositionally constructing sequential, parallel, branchial or iterative composites. Compositionality entails that a composite exhibits the same properties as its respective constituents, including separation of concerns and modularity. We conclude the paper by demonstrating how our proposal can be used to model high-level computations in two different application domains: software engineering and artificial intelligence.

AB - Every Model of High-Level Computation (MHC) has an underlying composition mechanism for combining simple computation devices into more complex ones. Composition can be done by (explicitly or implicitly) defining control flow, data flow or any combination thereof. Control flow specifies the order in which individual computations are activated, whereas data flow defines how data is exchanged among them. Unfortunately, traditional MHCs either mix data and control or only consider one dimension explicitly, which makes it difficult to reason about data flow and control flow separately. Reasoning about these dimensions orthogonally is a crucial desideratum for optimisation, maintainability and verification purposes. In this paper, we introduce a novel MHC that explicitly treats data flow and control flow as separate dimensions, while providing modularity. As the model is rooted in category theory, it provides category-theoretic operations for compositionally constructing sequential, parallel, branchial or iterative composites. Compositionality entails that a composite exhibits the same properties as its respective constituents, including separation of concerns and modularity. We conclude the paper by demonstrating how our proposal can be used to model high-level computations in two different application domains: software engineering and artificial intelligence.

M3 - Preprint

BT - Compositional Separation of Control Flow and Data Flow

PB - Arxiv

ER -