This free RAM cleaner uses native Windows features to optimize memory areas. It's a compact, portable, and smart application.

A fast and memory-optimized string library for heavy-text manipulation in Python

Free up your memory for free!

This code repository contains the code used for my "Optimizing Memory Usage for Training LLMs and Vision Transformers in PyTorch" blog post.

Contains materials about memory optimization and zero-allocation samples.

A performant and memory efficient storage for immutable strings with C++17. Supports all standard char types: char, wchar_t, char16_t, char32_t and C++20's char8_t.

Simple RAM Cleaner

A curated list of awesome optimizations that you can do to improve your redis deployment (both client side and server side).

An extension of micro mouse on WEBOTS using the flood filled algorithm, A star, Dijkstra’s and Breadth first search algorithm for moving the E-puck robot from start to goal in an NxM sized maze whose map was unknown to the robot (mapping and path planning). Further, leveraged Error Correction for accurate turning and recursive Backtracking algor…

Memory List Manager

Implementation of DAC'22 paper: Hierarchical Memory-Constrained Operator Scheduling of Neural Architecture Search Networks.

A generic Hash Table implemented in CPP

MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management, including MongoDB integration and OpenAI embeddings for semantic search capabilities.

Automatically reduce the memory size of any pandas dataframe based on downcasting bit types efficiently

Gets the smallest unsigned integer type that can represent a given value

Exploration of two important strategies to make our data analysis faster and independent of the dataset size.

A Lightweight AES-128/192/256 Implementation in C

Smaller Arrays Implementations fully built in python 3.8

Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.

Enhanced Convolutional Neural Network Accelerators with Memory Optimization for Routing Applications