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  3. 大规模化工流程模拟问题的方程组分析与预重排技术
OpenAtomGeneral MLIntermediateRegistration unverified

大规模化工流程模拟问题的方程组分析与预重排技术

A intermediate general ml competition requiring roughly 12–40 hours for an end-to-end practice run.

Difficulty
Intermediate
Estimated effort
12–40 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

适合具备科学计算或高性能计算背景、能处理大型稀疏非对称矩阵的开发者/团队。参赛者需要能实现方程与变量的重排及原始—重排序号映射,并交付可由C++ 17调用的代码接口;化工流程知识有助于理解EO模型的矩阵结构,但页面未将其列为硬性资格要求。

Read the original official blurb
大规模化工流程模拟问题的方程组分析与预重排技术:对于大规模流程的面向方程(EO)模拟模型的非线性方程组,基于图论或其他技术,实现方程组中方程顺序与变量顺序的重排,使得方程组Jacobi矩阵的非零元素排列具有规律性,以便实现其并行LU分解,最终提高非线性方程组的迭代求解效率。赛事以“方程组预重排+Jacobi矩阵LU分解”总耗时者少获胜,若耗时相同则以开源代码率低者获胜,若前两者皆相同,则以作品提交时间靠前者获胜。 Official category: 实战竞技赛. Organizers: 石化盈科信息技术有限责任公司. Competition code: 2025OAC069. Participants: 6.

Preparation

From registration to a first submission

  1. 01

    稀疏矩阵数据结构

  2. 02

    图论重排序算法

  3. 03

    并行LU分解

  4. 04

    C++17与DLL接口

  5. 05

    多核CPU性能优化

  6. 06

    开源许可证合规

Before you commit: 核心难点不是单独找到一种矩阵重排,而是在高度稀疏且结构不均衡的约6万变量方程组上,让重排后的矩阵既适合并行LU分解,又尽量避免各线程子块工作量失衡。作品还要在指定调用环境中以“预重排+LU分解”的总耗时竞争,并提供可复现源码、DLL、可视化工具和映射关系。

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Oct 10, 2025

Entry assessment

est.
48

Prepare before joining

Overall fit estimate

Beginner fit48/100
Learning value60/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

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Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

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Source page verifiedMetric confirmedReviewed
Source published
Oct 10, 2025
Source created
Oct 10, 2025
Published on this site
Jul 21, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.

reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.