geodesic_raytracing VS FastAD

Compare geodesic_raytracing vs FastAD and see what are their differences.

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geodesic_raytracing FastAD
18 1
78 91
- -
9.6 2.5
2 months ago 7 months ago
C++ C++
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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geodesic_raytracing

Posts with mentions or reviews of geodesic_raytracing. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-04.

FastAD

Posts with mentions or reviews of FastAD. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-22.
  • WG21 January 2021 Mailing
    3 projects | /r/cpp | 22 Jan 2021
    What stage is the differentiation proposal in? It seems odd they talked about reverse mode AD but made no mention of memory management for it. They also don't mention anything about FastAD or Stan Math which I think do some pretty innovative things in this space.

What are some alternatives?

When comparing geodesic_raytracing and FastAD you can also consider the following projects:

cuda-samples - Samples for CUDA Developers which demonstrates features in CUDA Toolkit

autodiff - automatic differentiation made easier for C++

Drogon-torch-serve - Serve pytorch / torch models using Drogon

papers - ISO/IEC JTC1 SC22 WG21 paper scheduling and management

astray - A performance-portable geodesic ray tracing library.

TemporalSetInversion - Reference implementation for "Temporal Set Inversion for Animated Implicits" (SIGGRAPH 2023)

giada - Your Hardcore Loop Machine.

filesystem - An implementation of C++17 std::filesystem for C++11 /C++14/C++17/C++20 on Windows, macOS, Linux and FreeBSD.

AECforWebAssembly - A port of ArithmeticExpressionCompiler from x86 to WebAssembly, so that the programs written in the language can run in a browser. The compiler has been rewritten from JavaScript into C++.

Mission : Impossible (AutoDiff) - A concise C++17 implementation of automatic differentiation (operator overloading)