Awesome GitHub RepositoriesMemory Mapping Utilities

LLM101n is an educational machine learning curriculum and open-source resource designed to teach the fundamental principles and practical implementation of large language models. It functions as a technical manual that guides users through the end-to-end process of building and training neural network architectures from scratch using a dynamic tensor library for automatic differentiation and GPU-accelerated computation. The project distinguishes itself through interactive, notebook-based instruction that allows for real-time visualization of training processes. It supports rapid experimentati

This project is a low-dependency engine designed for training large language models using native C and CUDA. It provides a bare-metal environment for tensor computation, allowing for the execution of neural network operations directly on hardware accelerators without the overhead of high-level software abstractions. The framework distinguishes itself by implementing manual gradient backpropagation and custom hardware-specific kernels, providing granular control over memory mapping and computational precision. It supports distributed training across multiple graphics processors and compute nod

RPCS3 is a C++ application that functions as a software environment for executing and managing PlayStation 3 console titles on desktop operating systems. It operates by translating proprietary console hardware instructions and graphics commands into formats compatible with modern computer hardware, allowing for the execution of original game software. The emulator distinguishes itself through a comprehensive suite of tools for managing game libraries, applying software patches to modify performance characteristics, and tracking the compatibility status of individual titles. It includes integr

This project provides an open-source firmware platform and complete build environment for portable multi-tool hardware. It functions as an embedded operating system designed to manage radio, infrared, and physical interface components, enabling users to develop custom applications and system logic for specialized hardware devices. The firmware distinguishes itself through a modular architecture that organizes system functionality into isolated units, allowing for the development of custom user interfaces and logic. It includes a comprehensive collection of low-level drivers and applications s

This project is an educational resource for developing bare-metal operating systems and kernels from scratch on Raspberry Pi hardware. It provides a structured guide to systems programming using the Rust language, focusing on the implementation of core kernel components that execute directly on ARM-based hardware without the support of an underlying operating system or standard library. The tutorials emphasize a modular architecture that separates hardware-independent kernel logic from processor-specific and board-specific configurations. By utilizing a hardware abstraction layer and distinct

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra operations on NVIDIA GPUs. It provides a kernel composition framework for designing custom GPU kernels and a mixed-precision tensor library capable of executing operations across diverse data formats, ranging from 64-bit floating point to 4-bit integers. The project features a toolkit for operator fusion that integrates activation functions and bias calculations directly into matrix multiplication kernels to reduce memory passes. It also includes a Python-based domain-specific langu

Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein summation, and multi-dimensional array operations. It serves as an abstraction layer that works across NumPy, PyTorch, TensorFlow, and JAX, allowing for tensor transformations without changing the API. The library distinguishes itself through a declarative notation system that uses readable string patterns to describe tensor rearrangements and reductions. This approach includes an extended Einstein summation interface that supports multi-letter axis names and a named dimension mapping

Torch7 is a scientific computing environment and tensor computation library used for deep learning research and numerical analysis. It functions as a Lua-based framework for training neural networks and learning agents, providing a toolkit for implementing architectures and training through reinforcement learning algorithms. The project is distinguished by its tight integration with C, utilizing a binding layer to map high-level scripting to low-level C structures for direct memory access. It supports hardware-accelerated computation by offloading linear algebra and convolution operations to

This project is a game AI training framework designed to develop and monitor reinforcement learning agents within a legacy game environment. It functions as a training and monitoring system that optimizes autonomous agents to complete game objectives through exploration and reward-based learning. The framework includes tools for game memory mapping and real-time trajectory visualization. These capabilities translate raw game memory addresses into visual coordinates, allowing agent movements and session data to be streamed to a map for the analysis of navigation patterns and area exploration.

This project is a Nintendo Entertainment System emulator written in Go. It functions as a hardware simulation that executes game ROMs by mimicking the original console's circuitry, including the processor and picture processing unit. The emulator includes a game library browser that identifies local files and retrieves titles and thumbnails from online databases using file checksums. It also implements a system of memory management mappers to ensure compatibility across various game ROM formats and hardware configurations. The software covers high-level capabilities for game ROM execution an

TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc

tiny-llm is a large language model inference engine and transformer model implementation. It serves as a quantized model runtime and paged key-value cache manager, providing a specialized inference stack optimized for Apple Silicon. The system distinguishes itself through high-throughput execution techniques, including continuous batching and paged attention. It utilizes a paged memory system to eliminate fragmentation during token generation and employs on-the-fly dequantization of compressed weights to reduce the memory footprint during matrix multiplication. The project covers a broad ran

Neural Prophet 是一个基于 PyTorch 的时间序列预测库，专为可解释的机器学习而设计。它作为一个分解框架，将信号分解为自回归效应、分段线性趋势和基于傅里叶的季节性等组成部分，以预测未来值。 该项目通过结合神经网络与传统算法，生成能够解释潜在趋势驱动因素的预测，从而脱颖而出。它采用全局时间序列建模方法，允许单个模型在多个同步序列上进行训练，在共享学习模式的同时保持局部特异性。此外，它还作为不确定性量化工具，利用分位数回归和共形预测来生成可靠的预测区间。 该库提供了一套全面的数据管理功能，包括节假日检索、缺口填充和归一化。它涵盖了完整的建模生命周期，包括自动超参数优化、趋势变点检测以及未来和滞后回归变量的集成。通过预测分解和输入归因分析，用户可以可视化特定因素如何影响最终预测。

Xarray 是一个 Python 多维数组库和带标签的数据集框架。它扩展了 NumPy 数据结构，通过为数组添加标签，允许使用命名维度和坐标来组织复杂的 N 维数据。 该库提供了 NetCDF 数据接口，用于读写 NetCDF 和 Zarr 等科学数据格式。它通过在数学运算过程中维护数据与物理坐标之间的关系，实现了科学数组计算。 该项目涵盖了多维数据分析、地理空间数据处理和气候数据处理。它还支持用于管理随时间变化的观测序列的时间序列分析。

NuttX 是一个符合 POSIX 标准的实时操作系统，专为资源受限的嵌入式环境而设计。它作为可扩展的微控制器操作系统，提供了一个类 Unix 环境，用于管理硬件并在从 8 位到 64 位的架构上运行应用程序。 该系统通过实现遵循 POSIX 和 ANSI 标准的内核来确保高软件可移植性。这允许开发者在不同的硬件架构上使用标准化的 API 调用创建可移植的嵌入式应用程序。 该项目包括模块化内核架构和硬件抽象层，以将系统与特定芯片外设解耦。它利用基于优先级的抢占式调度来实现确定性响应，并提供模拟硬件环境的工具，以便在没有物理板的情况下测试固件。

Rocket-chip is a framework for the parametric design, synthesis, and verification of RISC-V based processors and system-on-chip hardware. It functions as a generator that converts high-level specifications into synthesizable Verilog files for FPGA or ASIC implementation. The project utilizes a Scala-based hardware description framework to produce customizable pipelined processor cores, memory hierarchies, and peripheral devices. It employs a parameter-driven model and a two-phase negotiation process to resolve hardware interface specifications between modules during the elaboration phase. Th