{"title":"Carlo.jl：在 Julia 中进行蒙特卡罗模拟的通用框架","authors":"Lukas Weber","doi":"arxiv-2408.03386","DOIUrl":null,"url":null,"abstract":"Carlo.jl is a Monte Carlo simulation framework written in Julia. It provides\nMPI-parallel scheduling, organized storage of input, checkpoint, and output\nfiles, as well as statistical postprocessing. With a minimalist design, it aims\nto aid the development of high-quality Monte Carlo codes, especially for\ndemanding applications in condensed matter and statistical physics. This\nhands-on user guide shows how to implement a simple code with Carlo.jl and\nprovides benchmarks to show its efficacy.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Carlo.jl: A general framework for Monte Carlo simulations in Julia\",\"authors\":\"Lukas Weber\",\"doi\":\"arxiv-2408.03386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Carlo.jl is a Monte Carlo simulation framework written in Julia. It provides\\nMPI-parallel scheduling, organized storage of input, checkpoint, and output\\nfiles, as well as statistical postprocessing. With a minimalist design, it aims\\nto aid the development of high-quality Monte Carlo codes, especially for\\ndemanding applications in condensed matter and statistical physics. This\\nhands-on user guide shows how to implement a simple code with Carlo.jl and\\nprovides benchmarks to show its efficacy.\",\"PeriodicalId\":501369,\"journal\":{\"name\":\"arXiv - PHYS - Computational Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Computational Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.03386\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Computational Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.03386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
Carlo.jl 是一个用 Julia 编写的蒙特卡罗仿真框架。它提供 MPI 并行调度，有组织地存储输入、检查点和输出文件，以及统计后处理。它采用简约设计，旨在帮助开发高质量的蒙特卡罗代码，尤其是针对凝聚态物质和统计物理中的高要求应用。这本实用的用户指南展示了如何使用 Carlo.jl 实现一个简单的代码，并提供了基准来显示其功效。
Carlo.jl: A general framework for Monte Carlo simulations in Julia
Carlo.jl is a Monte Carlo simulation framework written in Julia. It provides
MPI-parallel scheduling, organized storage of input, checkpoint, and output
files, as well as statistical postprocessing. With a minimalist design, it aims
to aid the development of high-quality Monte Carlo codes, especially for
demanding applications in condensed matter and statistical physics. This
hands-on user guide shows how to implement a simple code with Carlo.jl and
provides benchmarks to show its efficacy.
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