Hacktoberfest 2026:维护者为十月标记出来的 issue,仍然开放、适合新手。 浏览 Hacktoberfest issue

Job scheduling efficiency improvements

已关闭
#77 0 条评论 0 个 reaction 已指派 0 人 在 GitHub 查看

还没有人认领这个 Issue。

评估

难度
5/5
预计耗时
一周以上
新手友好度
25/100
Issue 类型
功能
描述清晰度
需要澄清
活跃度
停滞
技术栈
python
领域
tooling

调研方向

未指定任何文件、测试或入口点。首先定位 OpenTitan 夜间和每周回归所使用的 scheduler,然后将未勾选的目标拆分为范围彼此独立的工作。只有就计划持久化、考虑时间或资源的限制,或考虑许可证的调度达成一致的范围和验收标准,才能算作完成。

由索引模型根据 Issue 内容生成。

描述

The nightly and weekly regressions for OpenTitan take significant resources to run. It is helpful to have more flexibility in the scheduler so that we can make better use of those resources.

Some ideas:

  • Test ordering - for the nightly regression runs most of the tests are complete in the first hour or so. By starting the longest running tests first and infilling with shorter running tests we can make the overall regression run time shorter, but keeping our scheduling slots busy.
  • Split run schedule
    • Generate a schedule and save to disk
    • Restore the schedule and run it
    • Restore the schedule and run a subset - potentially on a different machine
    • Restore a schedule and based on the working files resume a WIP run
  • More refined max parallel job control
    • Tool specific max parallel - different tools have different resource requirements so it's appropriate to have different limits proportional to the resource usage
    • Time based job control - it's common to run tests overnight/weekends when there may less contention of resources (compute or licenses). However jobs may have to be stopped if they don't finish within the expected timeslot if they are starving resources from other users. It would be useful to have the ability to set time dependent resource usage limits, so big jobs can be started any time and make use of the available resources at the time.
    • License aware scheduling - periodically query the license server so that the number of parallel jobs can be dynamically changed to reflect the currently available licenses. Avoiding starving other users, while making more efficient use of licenses.
    • General resource aware scheduling - periodically poll an external service to know how much resource is required for each job. This mechanism could be used for license server query, but also potentially other things like the machines memory and CPU usage.
主要语言
Python
星标
16
派生
22
平均合并
1 天 11 小时
30 天内合并 PR
5

环境准备

从这里开始

  1. 先读完整个 Issue,再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

lowRISC/dvsim 的其他 Issue

查看 lowRISC/dvsim 的全部 Issue

相似的 Issue

更多 Python Issue

把新 issue 发到你的邮箱

精选适合新手参与的 GitHub issue 摘要。