psychec
OpenMLDB
| psychec | OpenMLDB | |
|---|---|---|
| 4 | 10 | |
| 497 | 1,550 | |
| - | 1.3% | |
| 7.5 | 9.6 | |
| 7 days ago | 6 days ago | |
| C++ | C++ | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
psychec
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The Jotai Benchmark Collection
We, at UFMG, have been working on a methodology to generate benchmarks in C. We have a working collection of benchmarks here with a bit more than 30K executable programs. Benchmarks are single functions mined from open-source repositories. We have designed a domain-specific language to generate inputs for them. We use psyche-c to infer missing types and declarations. We use kcc and AddressSanitizier to filter out as much undefined behavior as possible. We use CFGGrind to check input coverage and to count the number of instructions executed. These benchmarks can be used in many ways: to stress test compilers; to autotune predictive compilation tasks; to analyze the dynamic behavior of programs; to improve compiler optimizations; etc. We have a technical report here.
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Getting AST of C source code programmatically!
Did you take a look at psyche-C? https://github.com/ltcmelo/psychec
- Psyche: A C front end for implementation of static analysis tools
- adding a C# Roslyn-like API as part of the rewrite of my C compiler frontend project
OpenMLDB
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OpenMLDB v0.9.0 Release: Major Upgrade in SQL Capabilities Covering the Entire Feature Servicing Process
For detailed release notes, please refer to: https://github.com/4paradigm/OpenMLDB/releases/tag/v0.9.0
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Comparative Analysis of Memory Consumption: OpenMLDB vs Redis Test Report
b. Pull the testing code
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Ultra High-Performance Database OpenM(ysq)LDB: Seamless Compatibility with MySQL Protocol and Multi-Language MySQL Client
OpenMLDB has introduced a new service module called OpenM(ysq)LDB, expanding its capabilities to integrate with MySQL infrastructure. This extension redefines the “ML” in OpenMLDB to signify both Machine Learning and MySQL compatibility. Through OpenM(ysq)LDB, users gain the ability to utilize MySQL command-line clients or MySQL SDKs in various programming languages, enabling seamless access to OpenMLDB’s unique online and offline feature calculation capabilities.
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Mastering Distributed Database Development in 10 Minutes with OpenMLDB Developer Docker Image
OpenMLDB is an open-source, distributed in-memory database system designed for time-series data. It focuses on high performance, reliability, and scalability, making it suitable for handling massive time-series data and real-time computation of online features. In the wave of big data and machine learning, OpenMLDB has emerged as a promising player in the open-source database field, thanks to its powerful data processing capabilities and efficient support for machine learning.
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OpenMLDB new release v0.8.4
For detailed release notes, please refer to: https://github.com/4paradigm/OpenMLDB/releases/tag/v0.8.4 Feel free to try it out, and discuss it in the official Slack channel (https://join.slack.com/t/openmldb/shared_invite/zt-ozu3llie-K~hn9Ss1GZcFW2~K_L5sMg) if you have any thoughts on improvements or questions!
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Quickstart with OpenMLDB
New to OpenMLDB? Check out the quick workflow and quickstart blog post!
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Engineering Practice for Real-time Feature Store in Decision-Making Machine Learning
Website: https://openmldb.ai/
- [D] Your 🫵 Preferred Feature Stores?
- OpenMLDB: An new open-source database for production AI/ML workloads
What are some alternatives?
ImGuiColorTextEdit - Colorizing text editor for ImGui
Open3D - Open3D: A Modern Library for 3D Data Processing
ccache - ccache – a fast compiler cache
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
timemory - Modular C++ Toolkit for Performance Analysis and Logging. Profiling API and Tools for C, C++, CUDA, Fortran, and Python. The C++ template API is essentially a framework to creating tools: it is designed to provide a unifying interface for recording various performance measurements alongside data logging and interfaces to other tools.
libpmemobj-cpp - C++ bindings & containers for libpmemobj
color_coded - A vim plugin for libclang-based highlighting of C, C++, ObjC
feast - Feature Store for Machine Learning
jotai-benchmarks - Collection of executable benchmarks
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
CFGgrind - A dynamic control flow graph (CFG) reconstruction plugin for valgrind.
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba