Every Model of High-Level Computation (MHC) has an underlying composition mechanism for combining simple computing 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, branching 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.

翻译：每一种高层计算模型（MHC）都具备一种底层组合机制，能够将简单的计算设备组合成更复杂的系统。组合可通过（显式或隐式地）定义控制流、数据流或其任意组合来实现。控制流规定了各个计算单元被激活的顺序，而数据流则定义了数据在它们之间交换的方式。然而，传统的高层计算模型要么将数据与控制混杂在一起，要么仅显式考虑其中一个维度，这使得单独推理数据流和控制流变得困难。正交地推理这些维度对于优化、可维护性和验证而言至关重要。本文提出了一种新颖的高层计算模型，该模型将数据流和控制流明确视为两个独立维度，同时保持了模块化特性。由于该模型植根于范畴论，它提供了范畴论操作，用于组合式地构建顺序、并行、分支或迭代的复合体。组合性意味着复合体能够展现与其各自组成部分相同的属性，包括关注点分离和模块化。最后，本文通过展示如何将我们的方法应用于软件工程和人工智能两个不同应用领域中的高层计算建模来作为结论。