Awesome GitHub RepositoriesCross-Runtime Execution Hosts

BenchmarkDotNet is a library and tool suite for measuring the execution time and memory allocation of .NET code. It utilizes statistical sampling and warm-up iterations to determine the stability and precise execution speed of specific methods. The project provides a JIT disassembly viewer to inspect processor disassembly and analyze how the compiler executes code paths. It includes a memory allocation profiler that tracks managed and native memory traffic to identify efficiency bottlenecks. Additionally, a runtime performance comparator allows the same benchmarks to be executed across differ

This project is a polyglot runtime bridge and interop framework designed to execute .NET and Node.js code within a single operating system process. It functions as a cross-runtime execution engine and in-process scripting host, enabling bidirectional communication and data exchange between the Common Language Runtime and JavaScript environments to eliminate cross-process communication overhead. The framework distinguishes itself by providing a system for bidirectional asynchronous callbacks and automatic data marshalling of binary buffers and serializable values between different managed obje

This project is a cross-language quantitative trading framework designed to implement and execute trading strategies consistently across Python, JavaScript, C++, and PineScript. It functions as a polyglot trading strategy translator and a multi-language algorithmic trading engine that maps high-level scripting and block-based logic to executable binaries. The system features a financial domain-specific language parser that translates specialized trading syntax and visual programming blocks into a standardized internal representation. It includes a technical analysis pattern library providing

Polynote 是一个多语言笔记本环境和交互式文档系统，旨在在单个文档中执行多种语言的代码。它作为一个跨语言数据分析工具和 JVM 语言 IDE，允许用户结合可执行代码、富文本和数据可视化来原型化和记录技术工作流。 该系统的特点在于能够在不同的语言运行时（如 Python 和 JVM）之间共享数据和变量。它使用跨语言对象转换和数据包装来在运行时之间传递对象，从而实现多语言数据工作流。此外，它与 Apache Spark 集成，通过本地或远程集群提交来执行分布式数据处理任务。 该平台提供了一套全面的数据分析和可视化功能，包括用于运行时监控的实时符号表，以及对 Vega 规范渲染的支持。它使用基于坐标的解析来管理 JVM 和 Python 运行时的依赖关系，并提供具有自动补全和错误高亮功能的 IDE 增强编辑体验。 文档管理功能包括动态目录、跨笔记本内容搜索以及基于浏览器的备份恢复，以防止文件损坏导致的数据丢失。