awesome-repositories.com
© 2026 Bringes Technology SRL·VAT RO45896025·hello@bringes.io
MCPSitemapPrivacyTerms
Graph and Symbolic Execution Engines · Awesome GitHub Repositories

4 repos

Awesome GitHub RepositoriesGraph and Symbolic Execution Engines

Runtimes that utilize dependency graphs, symbolic representations, or deferred evaluation to optimize computational tasks.

Explore 4 awesome GitHub repositories matching programming languages & runtimes · Graph and Symbolic Execution Engines. Refine with filters or upvote what's useful.

  1. Home
  2. Programming Languages & Runtimes
  3. Runtime Execution Environments
  4. Runtime Environments
  5. Runtimes
  6. Graph and Symbolic Execution Engines

Awesome Graph and Symbolic Execution Engines GitHub Repositories

Describe the repository you're looking for…
We'll search the best matching repositories with AI.
  • tensorflow/tensorflow

    tensorflow/tensorflow

    193,864GitHubView on GitHub↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The syst

    C++deep-learningdeep-neural-networksdistributed
  • Comfy-Org/ComfyUI

    Comfy-Org/ComfyUI

    103,654GitHubView on GitHub↗

    ComfyUI is a node-based generative AI orchestration engine designed for constructing, testing, and executing complex image and video synthesis pipelines. By utilizing a directed acyclic graph execution model, the platform allows users to build reproducible workflows through modular, interconnected processing blocks wit

    Pythonaicomfycomfyui
  • pytorch/pytorch

    pytorch/pytorch

    97,601GitHubView on GitHub↗

    PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic diffe

    Pythonautograddeep-learninggpu
  • d2l-ai/d2l-zh

    d2l-ai/d2l-zh

    75,708GitHubView on GitHub↗

    This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners

    Pythonbookchinesecomputer-vision

Explore sub-tags

  • Deferred-Execution Symbolic GraphsSystems that construct symbolic representations of operations before execution to enable graph-level optimizations.
  • Directed Acyclic Graph Execution EnginesEngines that process computational pipelines by traversing dependency-ordered graphs of nodes.
  • Hybrid Execution ModesExecution modes that combine imperative programming flexibility with performance-oriented graph optimizations.
Lazy Evaluation Engines
Engines that compute only the necessary operations required for final output by tracking state changes.
  • Operation KernelsDefined units of work that serve as the fundamental building blocks for computational operations.