8 个仓库
General techniques to improve the runtime speed of code, such as inlining and type hints.
Distinct from Emulation Speed Optimizations: Candidates focus on emulators, web pages, or AI tokens, not general Lisp execution speed.
Explore 8 awesome GitHub repositories matching programming languages & runtimes · Execution Optimizations. Refine with filters or upvote what's useful.
This project is a collection of educational resources and technical guides focused on Go performance optimization. It provides instruction on improving execution speed and reducing memory usage through code and architectural refinements. The guides cover advanced strategies for low-level programming, including the use of assembly for SIMD instructions and unsafe pointers for direct memory manipulation. It also details concurrency optimization techniques such as lock sharding and cache-line padding to reduce contention and improve hardware utilization. The material encompasses broad capabilit
Implements runtime execution optimizations such as reducing interface call overhead and eliminating bounds checks.
This project is a comprehensive Lisp AI implementation library that provides reference implementations for various artificial intelligence paradigms and symbolic algorithms. It functions as a multi-purpose toolkit containing a logic programming engine, a natural language processing suite, and a symbolic mathematics toolkit. The library is distinguished by its diverse architectural frameworks, including a Prolog-style execution engine that uses unification and goal-driven backtracking, and a system for simulating human decision-making through expert system shells and certainty factors. It also
Increases performance using type declarations and function inlining to prioritize execution speed.
该项目是一个综合性教育计划和深度学习框架,旨在通过 Notebook 和代码示例教授 PyTorch 深度学习实践。它作为一个用于构建、训练和部署神经网络的高级库,充当模型训练编排器,协调 PyTorch 模型、优化器和损失函数。 该项目为计算机视觉、自然语言处理和表格数据预处理提供了专门的工具包。它通过高级训练控制脱颖而出,例如判别式学习率、用于自定义训练逻辑的双向回调系统,以及自动化设备放置和训练循环的高级学习器抽象。 该框架涵盖了广泛的能力面,包括自动化数据流水线构建、模型架构分析以及跨分类、回归和分割任务的性能评估。它还包括用于跨多个 GPU 进行分布式训练的工具、用于内存优化的混合精度训练,以及对医学影像数据的专门支持。 该项目以一系列 Jupyter Notebook 的形式交付。
Employs JIT scripts to accelerate the execution of forward and backward passes during training.
ArrayFire 是一个硬件无关的计算框架和 JIT 编译张量引擎,专为高性能数值计算而设计。它作为一个 GPU 数值计算库和并行信号处理工具包,抽象了硬件后端,允许同一代码库在各种 GPU 架构和 CPU 上执行。 该项目以其使用表达式编译来融合操作并最小化内存开销的 JIT 引擎而脱颖而出。它采用延迟执行图来优化计算链,并提供互操作性原语以与 CUDA 和 OpenCL 等外部计算平台共享数据和执行上下文。 该库涵盖了广泛的功能,包括并行线性代数、数字信号处理和加速计算机视觉。它提供了用于机器学习实现、金融建模模拟以及求解物理系统模拟偏微分方程的工具。其张量管理系统处理多维数组分配、切片和主机-设备数据传输。
Implements runtime optimizations to increase memory throughput and minimize temporary allocations during execution.
Scala.js 是一个编译器和跨平台语言工具链,将 Scala 源代码转换为 JavaScript 或 WebAssembly。它作为 JavaScript 生态系统的一种静态类型工具,支持为 Web 浏览器和 Node.js 环境开发应用程序。 该项目作为一个 JavaScript 互操作框架,允许创建类型安全的门面(facades)和绑定,以与外部库和全局对象进行交互。它提供了静态和动态 JavaScript 调用机制,包括生成 TypeScript 绑定以及导出内部逻辑以供外部 JavaScript 代码使用的能力。 该工具链包含一个用于生产环境打包和输出优化的前端构建工具,包括死代码消除和模块拆分。它涵盖了广泛的功能面,包括用于 UI 开发的 DOM 元素类型检查、用于全栈开发的跨平台代码共享,以及用于验证优化构建产物的各种测试框架。 编译后的脚本可以使用 JavaScript 解释器直接在命令行环境中执行。
Optimizes runtime speed by rewriting iterators as loops and applying function inlining during compilation.
Jint is a JavaScript interpreter for the .NET ecosystem that executes code without requiring a browser or Node.js environment. It is an ECMAScript compliant engine that provides a sandboxed scripting runtime with configurable limits on memory and time to run untrusted code. The engine features a native object bridge that exposes .NET classes and methods to JavaScript scripts for bidirectional data exchange. To reduce overhead during repeated executions, it utilizes a precompiled script cache to store parsed JavaScript in memory. The project covers asynchronous script execution and promise ha
Applies execution optimizations such as pre-compilation and strict mode to increase processing speed.
该项目是一个 Android 应用修补程序和可执行文件优化器,旨在修改和重新编译应用二进制文件。它作为一个基于 Root 的应用修改器,允许向 Android 应用添加功能并移除不需要的库。 该系统专注于通过添加非官方功能和移除广告来定制 YouTube 和 YouTube Music。它提供了通过 Root 管理器部署修改后应用模块的能力,以保持系统兼容性并绕过检测。 该工具涵盖了广泛的二进制转换能力,包括基于字节码的修补、非 Root 应用重新打包以及动态库的移除。它还处理针对特定目标的执行文件重新编译,以提高运行时性能和执行速度。
Recompiles invalidated executable files to increase overall execution speed and runtime performance.
IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Uses just-in-time compilation to optimize neural network functions within interactive sessions for immediate execution.