tuninglib
A C++ Class and Template Library for Performance Critical Applications (by spirick)
clrs
By google-deepmind
| tuninglib | clrs | |
|---|---|---|
| 3 | 4 | |
| 4 | 529 | |
| - | 0.9% | |
| 6.8 | 5.2 | |
| almost 3 years ago | 2 months ago | |
| C++ | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tuninglib
Posts with mentions or reviews of tuninglib.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Spirick TuningLib on GitHub
Spirick Tuning is a C++ class and template library for performance critical applications. Platforms: MS Windows/Linux Version 1.48 of the TuningLib is published as an open source project on GitHub. github.com/spirick/tuninglib
- Spirick Tuning Lib on GitHub
clrs
Posts with mentions or reviews of clrs.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-10.
-
[P] GITModel: Dynamically generate high-quality hierarchical topic tree representations of GitHub repositories using customizable GNN message passing layers, chatgpt, and topic modeling.
Example: https://github.com/deepmind/clrs
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[D] Is there any research into using neural networks to discover classical algorithms?
Bonus, there is this CLRS benchmark that might be useful for evaluation for your task.
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[R] A Generalist Neural Algorithmic Learner
The baseline code for the CLRS benchmark (which we use in the paper) has been open-sourced for a while now: https://github.com/deepmind/clrs
- GitHub - deepmind/clrs
What are some alternatives?
When comparing tuninglib and clrs you can also consider the following projects:
NumCpp - C++ implementation of the Python Numpy library
zingg - Scalable master data management, identity resolution, entity resolution, and deduplication using ML
Umpire - An application-focused API for memory management on NUMA & GPU architectures
GitModel - Codebase topic modeling using GNNs(Node aggregation and clustering)
CLRS - Algorithms implementation in C++ and solutions of questions (both code and math proof) from “Introduction to Algorithms” (3e) (CLRS) in LaTeX.
cp-algorithms - Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)