Awesome GitHub RepositoriesDeterministic Execution Environments

This project is a high-level programming language and compiler toolchain designed for writing and deploying smart contracts on decentralized virtual machines. It provides a framework for creating self-executing code that operates within deterministic execution environments, ensuring that distributed nodes reach identical state transitions through stack-based machine execution. The language enables the development of complex decentralized logic by supporting state-machine-based contract structures and modular composition. It includes built-in capabilities for cryptographic signature verificati

This project is a comprehensive educational curriculum designed to teach developers how to build, test, and deploy decentralized applications and smart contracts on Ethereum-compatible networks. It serves as a structured technical guide for mastering programmable logic, decentralized finance protocols, and the architecture required to create secure distributed systems. The course distinguishes itself by focusing on the integration of blockchain smart contracts with web frontends, enabling the creation of interactive applications that allow users to read and write data directly from distribute

rr is a deterministic record and replay framework and reverse debugger for Linux processes. It provides a deterministic execution environment that captures program execution, allowing bugs and crashes to be reproduced exactly through replay. The tool enables reverse program execution, allowing a developer to move the program counter backward through recorded history to trace a bug from its effect back to its source. It utilizes a recording mechanism that ensures a process run can be replayed with identical memory and register states. The framework covers low-level software analysis and nativ

oneDNN 是一个深度学习加速库，为神经网络训练和推理提供优化的构建块。它管理跨 CPU 和 GPU 硬件的张量计算，支持执行用于模型训练和神经网络推理优化的高性能原语。 该项目通过硬件特定的内核优化和使用即时编译来针对特定处理器指令集脱颖而出。它支持使用静态和动态量化来执行量化神经网络，以减少内存使用并提高吞吐量。 该库涵盖了广泛的功能，包括卷积、矩阵乘法和循环神经网络执行等深度学习原语。它实现了先进的性能优化，包括操作融合、计算图优化和内存格式管理。通过稳定的 C ABI 和 C++ 包装器提供集成，并支持 SYCL、OpenCL 和外部线性代数库。 该系统包括用于硬件性能分析、原语基准测试和详细执行日志记录的观测工具。