vectorized-computation

StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.

Efficient Batched Reinforcement Learning in TensorFlow

A tool to graphically visualize SIMD code

Another OLAP database

A curated list of awesome SIMD frameworks, libraries and software

Fast nonlinear FEA tailored for topology optimization

Vectroized String Helper Functions

String functions using word-packing

An ML+NLP solution for linking misspelled titles with the true titles

Two-point connectivity statistics computation for hydrological patterns

Exercises, Descriptions, and Visualizations to build intuitions and confidence in working with PyTorch for accelerated Scientific Computing

Accelerating convolution using numba, cupy and xnor in python

While it is convenient to use advanced libraries for day-to-day modeling, it does not give insight into the details of what really happens underneath, when we run the codes. In this work, we implement a logistic regression model manually from scratch, without using any advanced library, to understand how it works.

Collection of experiments to carve out the differences between two types of relational query processing engines: Vectorizing (interpretation based) engines and compiling engines.

My finance tools in Python3

An Implementation of fuzzy clustering algorithms in Numpy

A Hands-On NumPy Tutorial for Data Scientists

This repository shows code of programming tasks which I completed during Machine Learning course on Coursera.

In this repo I improve the performance of the LIBtft144 controller using numpy and vector operations, and use it to show in real-time the image stream from my raspberry-pi camera on a SPI 144 display