StarRocks, a Linux Foundation project, is a next-generation sub-second MPP OLAP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics, and ad-hoc queries.
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Updated
Sep 30, 2024 - Java
StarRocks, a Linux Foundation project, is a next-generation sub-second MPP OLAP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics, and ad-hoc queries.
LakeSoul is an end-to-end, realtime and cloud native Lakehouse framework with fast data ingestion, concurrent update and incremental data analytics on cloud storages for both BI and AI applications.
heavily vectorized c++17 compile time string encryption.
World's fastest log analysis: λ + SQL + JSON + S3
[CVPR 2023] vMAP: Vectorised Object Mapping for Neural Field SLAM
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
Plot in NumPy arrays directly, overlay NumPy plots straight over video real-time, plot in Jupyter without a single loop
Scalable package delivery logistics simulator built using SingleStore and Vectorized Redpanda
This repo provides a starting point for building applications using SingleStore, Redpanda (by Vectorized), and the Go language. SingleStore is a scale-out relational database built for data-intensive workloads. Redpanda is a Kafka API compatible streaming platform for mission-critical workloads created by the team at Vectorized.
Fast and Strong Burrows Wheeler Model
fast + parallel AlphaZero in PyTorch
A C++17 header-only library that provides compile-time string encryption and decryption using SIMD instructions
TurboMath is an all inline SIMD SSE C++ Math-Library for use in Games and Graphics Windows-Apps
Vectorized CNN implementation from scratch using only numpy
Vectorized Associative Arrays for Python
Sentiment analysis of kinopoisk (https://www.kinopoisk.ru/) reviews. For analysis i used: classification methods (random forest, svc, k-neighbors) neural networks (lstm, mlp).
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