3 个仓库
Distributing computational workloads across multiple CPU cores using worker pools.
Distinct from Multi-Core Workload Distribution: Candidates are either too focused on cloud infrastructure (ECS/K8s) or GPU-specific distribution.
Explore 3 awesome GitHub repositories matching operating systems & systems programming · Multi-Process Task Distribution. Refine with filters or upvote what's useful.
This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
Demonstrates how to distribute workloads across multiple CPU cores using worker pools to bypass execution locks.
Phan is a static analyzer and type checker for PHP that identifies bugs and type incompatibilities without executing the code. It serves as a quality gate for continuous integration pipelines and a tool for verifying type safety, specifically checking union types, generics, and array shapes. The project is distinguished by its use of a background daemon and Language Server Protocol implementation, which provide real-time diagnostics and navigation within editors. It also features a baseline-based suppression system that allows developers to record existing errors in a snapshot file to focus e
Distributes the analysis workload across multiple CPU cores using worker pools to reduce processing time.
Dshell 是一个网络取证分析框架和流量处理器,专为 IPv4 和 IPv6 流量的深度包检测而设计。它作为一个可扩展的取证插件系统,能够捕获、检查和分析网络数据,以识别安全异常并重构通信流。 该系统采用基于插件的处理引擎,支持自定义插件开发和插件链式调用。这种模块化架构使得创建专门的分析流水线成为可能,网络数据可按顺序通过一系列处理单元进行多步分析。 该框架涵盖了广泛的取证功能,包括实时流量监控、网络流重组以及通过外部参考数据库进行 IP 地理位置映射。为处理海量数据,引擎采用并行数据处理,将任务分配到多个系统进程中。处理后的分析结果可通过专门的输出处理器导出为多种结构化文件格式。
Distributes packet capture and analysis workloads across multiple system processes to increase throughput.