特大工作

This document provides an overview of two open-source projects: XXL-JOB, a distributed task scheduling framework, and Mitsuba 3, a research-oriented rendering system. Both projects offer detailed documentation, tutorials, and community support. XXL-JOB focuses on ease of use and scalability, while Mitsuba 3 prioritizes high performance and differentiability in rendering simulations.

XXL-JOB

XXL-JOB, a distributed task scheduling framework.

-- Home Page --

Introduction

XXL-JOB is a distributed task scheduling framework.

It's core design goal is to develop quickly and learn simple, lightweight, and easy to expand.

Now, it's already open source, and many companies use it in production environments, real "out-of-the-box".

XXL-JOB是一个分布式任务调度平台，其核心设计目标是开发迅速、学习简单、轻量级、易扩展。现已开放源代码并接入多家公司线上产品线，开箱即用。

Sponsor

XXL-JOB is an open source and free project, with its ongoing development made possible entirely by the support of these awesome backers.

XXL-JOB 是一个开源且免费项目，其正在进行的开发完全得益于支持者的支持。开源不易，前往赞助项目开发

Documentation

Communication

Features

Development

于2015年中，我在github上创建XXL-JOB项目仓库并提交第一个commit，随之进行系统结构设计，UI选型，交互设计……

于2015-11月，XXL-JOB终于RELEASE了第一个大版本V1.0， 随后我将之发布到OSCHINA，XXL-JOB在OSCHINA上获得了@红薯的热门推荐，同期分别达到了OSCHINA的“热门动弹”排行第一和git.oschina的开源软件月热度排行第一，在此特别感谢红薯，感谢大家的关注和支持。

于2015-12月，我将XXL-JOB发表到我司内部知识库，并且得到内部同事认可。

于2016-01月，我司展开XXL-JOB的内部接入和定制工作，在此感谢袁某和尹某两位同事的贡献，同时也感谢内部其他给与关注与支持的同事。

于2017-05-13，在上海举办的 "第62期开源中国源创会" 的 "放码过来" 环节，我登台对XXL-JOB做了演讲，台下五百位在场观众反响热烈（图文回顾 ）。

于2017-10-22，又拍云 Open Talk 联合 Spring Cloud 中国社区举办的 "进击的微服务实战派上海站"，我登台对XXL-JOB做了演讲，现场观众反响热烈并在会后与XXL-JOB用户热烈讨论交流。

于2017-12-11，XXL-JOB有幸参会《InfoQ ArchSummit全球架构师峰会》，并被拍拍贷架构总监"杨波老师"在专题 "微服务原理、基础架构和开源实践" 中现场介绍。

于2017-12-18，XXL-JOB参与"2017年度最受欢迎中国开源软件"评比，在当时已录入的约九千个国产开源项目中角逐，最终进入了前30强。

于2018-01-15，XXL-JOB参与"2017码云最火开源项目"评比，在当时已录入的约六千五百个码云项目中角逐，最终进去了前20强。

于2018-04-14，iTechPlus在上海举办的 "2018互联网开发者大会"，我登台对XXL-JOB做了演讲，现场观众反响热烈并在会后与XXL-JOB用户热烈讨论交流。

于2018-05-27，在上海举办的 "第75期开源中国源创会" 的 "架构" 主题专场，我登台进行“基础架构与中间件图谱”主题演讲，台下上千位在场观众反响热烈（图文回顾 ）。

于2018-12-05，XXL-JOB参与"2018年度最受欢迎中国开源软件"评比，在当时已录入的一万多个开源项目中角逐，最终排名第19名。

于2019-12-10，XXL-JOB参与"2019年度最受欢迎中国开源软件"评比，在当时已录入的一万多个开源项目中角逐，最终排名"开发框架和基础组件类"第9名。

于2020-11-16，XXL-JOB参与"2020年度最受欢迎中国开源软件"评比，在当时已录入的一万多个开源项目中角逐，最终排名"开发框架和基础组件类"第8名。

于2021-12-06，XXL-JOB参与"2021年度OSC中国开源项目评选 "评比，在当时已录入的一万多个开源项目中角逐，最终当选"最受欢迎项目"。

我司大众点评目前已接入XXL-JOB，内部别名《Ferrari》（Ferrari基于XXL-JOB的V1.1版本定制而成，新接入应用推荐升级最新版本）。

据最新统计, 自2016-01-21接入至2017-12-01期间，该系统已调度约100万次，表现优异。新接入应用推荐使用最新版本，因为经过数十个版本的更新，系统的任务模型、UI交互模型以及底层调度通讯模型都有了较大的优化和提升，核心功能更加稳定高效。

至今，XXL-JOB已接入多家公司的线上产品线，接入场景如电商业务，O2O业务和大数据作业等，截止最新统计时间为止，XXL-JOB已接入的公司包括不限于：

更多接入的公司，欢迎在 登记地址 登记，登记仅仅为了产品推广。

欢迎大家的关注和使用，XXL-JOB也将拥抱变化，持续发展。

Contributing

Contributions are welcome! Open a pull request to fix a bug, or open an Issue to discuss a new feature or change.

欢迎参与项目贡献！比如提交PR修复一个bug，或者新建 Issue 讨论新特性或者变更。

Copyright and License

This product is open source and free, and will continue to provide free community technical support. Individual or enterprise users are free to access and use.

产品开源免费，并且将持续提供免费的社区技术支持。个人或企业内部可自由的接入和使用。如有需要可 邮件联系 作者免费获取项目授权。

example:

Mitsuba Renderer 3

Documentation	Tutorial videos	Linux	MacOS	Windows	PyPI

️

Warning

️

There currently is a large amount of undocumented and unstable work going on in

the master branch. We'd highly recommend you use our

latest release

until further notice.

If you already want to try out the upcoming changes, please have a look at

this porting guide.

It should cover most of the new features and breaking changes that are coming.

Introduction

Mitsuba 3 is a research-oriented rendering system for forward and inverse light

transport simulation developed at EPFL in Switzerland.

It consists of a core library and a set of plugins that implement functionality

ranging from materials and light sources to complete rendering algorithms.

Mitsuba 3 is retargetable: this means that the underlying implementations and

data structures can transform to accomplish various different tasks. For

example, the same code can simulate both scalar (classic one-ray-at-a-time) RGB transport

or differential spectral transport on the GPU. This all builds on

Dr.Jit, a specialized just-in-time(JIT) compiler developed specifically for this project.

Main Features

• Cross-platform: Mitsuba 3 has been tested on Linux (x86_64), macOS

  (aarch64, x8664), and Windows (x8664).

• High performance: The underlying Dr.Jit compiler fuses rendering code

  into kernels that achieve state-of-the-art performance using

  an LLVM backend targeting the CPU and a CUDA/OptiX backend

  targeting NVIDIA GPUs with ray tracing hardware acceleration.

• Python first: Mitsuba 3 is deeply integrated with Python. Materials,

  textures, and even full rendering algorithms can be developed in Python,

  which the system JIT-compiles (and optionally differentiates) on the fly.

  This enables the experimentation needed for research in computer graphics and

  other disciplines.

• Differentiation: Mitsuba 3 is a differentiable renderer, meaning that it

  can compute derivatives of the entire simulation with respect to input

  parameters such as camera pose, geometry, BSDFs, textures, and volumes. It

  implements recent differentiable rendering algorithms developed at EPFL.

• Spectral & Polarization: Mitsuba 3 can be used as a monochromatic

  renderer, RGB-based renderer, or spectral renderer. Each variant can

  optionally account for the effects of polarization if desired.

Tutorial videos, documentation

We've recorded several YouTube videos that provide a gentle introduction

Mitsuba 3 and Dr.Jit. Beyond this you can find complete Juypter notebooks

covering a variety of applications, how-to guides, and reference documentation

on readthedocs.

Installation

We provide pre-compiled binary wheels via PyPI. Installing Mitsuba this way is as simple as running

pip install mitsuba

on the command line. The Python package includes thirteen variants by default:

• scalar_rgb

• scalar_spectral

• scalarspectralpolarized

• llvmadrgb

• llvmadmono

• llvmadmono_polarized

• llvmadspectral

• llvmadspectral_polarized

• cudaadrgb

• cudaadmono

• cudaadmono_polarized

• cudaadspectral

• cudaadspectral_polarized

The first two perform classic one-ray-at-a-time simulation using either a RGB

or spectral color representation, while the latter two can be used for inverse

rendering on the CPU or GPU. To access additional variants, you will need to

compile a custom version of Dr.Jit using CMake. Please see the

documentation

for details on this.

Requirements

• Python >= 3.8

• (optional) For computation on the GPU: Nvidia driver >= 495.89

• (optional) For vectorized / parallel computation on the CPU: LLVM >= 11.1

Usage

Here is a simple "Hello World" example that shows how simple it is to render a

scene using Mitsuba 3 from Python:

# Import the library using the alias "mi"import mitsuba as mi# Set the variant of the renderermi.setvariant('scalarrgb')# Load a scenescene = mi.loaddict(mi.cornellbox())# Render the sceneimg = mi.render(scene)# Write the rendered image to an EXR filemi.Bitmap(img).write('cbox.exr')

Tutorials and example notebooks covering a variety of applications can be found

in the documentation.

About

This project was created by Wenzel Jakob.

Significant features and/or improvements to the code were contributed by

Sébastien Speierer,

Nicolas Roussel,

Merlin Nimier-David,

Delio Vicini,

Tizian Zeltner,

Baptiste Nicolet,

Miguel Crespo,

Vincent Leroy, and

Ziyi Zhang.

When using Mitsuba 3 in academic projects, please cite:

@software{Mitsuba3,title = {Mitsuba 3 renderer},author = {Wenzel Jakob and Sébastien Speierer and Nicolas Roussel and Merlin Nimier-David and Delio Vicini and Tizian Zeltner and Baptiste Nicolet and Miguel Crespo and Vincent Leroy and Ziyi Zhang},note = {https://mitsuba-renderer.org},version = {3.1.1},year = 2022}