An extremely easy way to perform background processing in Java. Backed by persistent storage. Open and free for commercial use.

Video and Image Processing and Computer Vision Library in pure JavaScript (Browser and Node.js)

Really High Efficient File Optimizer will losslessly recompress files as much as possible according to their mime-types

Exploring R for high-performance data analytics, including memory management, GPU computing, parallel processing, benchmarks, case studies, and comparisons with Python.

Python supercharged for the fastai library

A FPGA based grid ant colony heurastic optimization , with parallel agents for speed up

Welcome to the Computer Science Mini Projects repository! This repository serves as a central hub for various mini-projects related to different domains in computer science. Each project is housed in its own GitHub repository, and you can find the links to these projects below.

ReLM is the soft-core multiprocessor technology based on the unique memory architecture, enabling users to build a high-performance microcontroller on a relatively small FPGA board.

STREAM, for lots of devices written in many programming models

Concurrency, Multithreading and Parallel Computing in Java

C++ class for signal processing, async network programming, concurrency, parallel computing and multithreading

This project is a hands-on workshop focused on exploring and understanding the concepts of parallel programming in C#. It provides practical examples of using delegates, events, and various parallel programming constructs such as Parallel.For, Parallel.ForEach, Parallel.Invoke, and Task.Factory in a console application.

Open source library for parallel finite element analysis.

Dynamic-Zoom: Code Repository

C++ light-weight Thread Pool library

Efficient Resource Sharing across Heterogeneous Computing

Forking jobs in PHP to speed up processes

fragments image and re-assemble them back to original image. #Prim's MST algorithm

Parallel map, reduce and more using coroutines in Kotlin.

Extract insights from Toronto's bike share ridership data to guide digital advertising campaign initiatives aimed at growing enrollment in local SCS program