Which programming paradigms are supported by this skill?

It primarily supports functional programming via Lambda Calculus, concurrent logic via Interaction Nets, and symbolic manipulation via Lisp-style S-expressions.

How does this skill utilize CUDA for rewriting?

It maps interaction net reductions to GPU kernels, enabling the simultaneous execution of independent rewrite rules (redexes). This provides significant speedups for complex term evaluations compared to serial CPU processing.

What is the homoiconic property in this context?

It refers to the 'Code = Data = Graph' principle, where programs are represented as structured data like S-expressions or graphs, allowing the code to be transformed, optimized, and reduced as a mathematical object.

What role do ACSets play in the rewriting pipeline?

ACSets (Algebraic Databases) provide a formal framework for representing data as graphs. This allows for rigorous rule validation and application via Double Pushout (DPO) rewriting, ensuring mathematical correctness in data transformations.

Homoiconic Graph Rewriting

Name: Homoiconic Graph Rewriting
Author: plurigrid

byplurigrid

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開発者ツール

Implements unified homoiconic graph rewriting to bridge lambda calculus, interaction nets, and parallel CUDA-based reduction.

概要

This skill enables Claude to manage a sophisticated computation pipeline where code, data, and graphs are treated as identical representations (homoiconicity). It facilitates the transformation of lambda-calculus terms into interaction nets for massively parallel reduction on GPUs, while leveraging ACSets (Algebraic Databases) for rule validation and structured data manipulation. It is an essential tool for developers and researchers working on high-performance compilers, symbolic mathematics, and the application of Category Theory to computational logic.

主な機能

2 GitHub stars
Support for symmetric interaction combinators and linear logic mappings
Bidirectional transformation between S-expressions, Lambda-calculus, and Interaction Nets
ACSet-based rewriting using Double Pushout (DPO) semantics for algebraic data
CUDA-accelerated parallel graph reduction for high-performance term evaluation
GF(3) balanced dependency management for coordinating complex toolchains

ユースケース

Implementing massively parallel graph rewriting systems for complex symbolic logic
Modeling category-theoretic data structures and transformations using Algebraic Databases
Developing high-performance functional programming language compilers and runtimes

概要

主な機能

2 GitHub stars
Support for symmetric interaction combinators and linear logic mappings
Bidirectional transformation between S-expressions, Lambda-calculus, and Interaction Nets
ACSet-based rewriting using Double Pushout (DPO) semantics for algebraic data
CUDA-accelerated parallel graph reduction for high-performance term evaluation
GF(3) balanced dependency management for coordinating complex toolchains

ユースケース

Implementing massively parallel graph rewriting systems for complex symbolic logic
Modeling category-theoretic data structures and transformations using Algebraic Databases
Developing high-performance functional programming language compilers and runtimes