CLR Garbage Collection

本文详细解析了CLR内存管理机制,包括内存架构、对象代际划分、垃圾回收过程及优化建议等内容。介绍了不同代际的对象收集频率、根引用概念、对象终结及大对象堆等关键知识点。

1. Memory architecture

2. Generations
  • Generation 0 : Short lived objects (Collected frequently)
  • Generation 1 : Medium lived objects (Collected less frequently)
  • Generation 2 : Long lived objects (Variable size and expensive to collect)
  • Generation 0 and 1 is known as the ephemeral segment (Fixed size)

SOS : !eeheap -gc

SOSEX : !gcgen <address> 

3. Roots
  • GC uses roots to find which objects are alive or dead
  • Any object with an existing reference to it has a root and is thus considered alive
  • Roots are determined using the following components:JIT compoler, Stack walker, Handle table, Finalize Queue

SOS : !gcroot <address>

4. Finalization
  • GC only knows about managed objects
  • Objects that wrap native types need a cleanup mechanism
  • Objects that wrap a native types must:
    • Implement a Finalizer
    • Implement IDisposable
    • Both methods should use same private helper method 
Finalization Best Practices
  • Whenever possible do not rely on finalization rather always explicitly Dispose finalizable objects
  • If you implement a finalizer you should also implement IDisposable (Dispose suppresses the object finalization)
  • In C#, the using {} pattern automatically invokes the Dispose method
5. Large object heap
  • Objects greater than 85,000 bytes
  • Key difference is that LOH is not compacted (Very common cause of memory fragmentation)
  • Introduced to avoid the cost of compaction
6. Pinning problems
  • As part of compaction the GC may move an object around
  • Problem for objects passed to native code (For example, a buffer to async native operation)
  • Pinning tells the GC that it is not allowed to move the object
  • Excessive pinning common cause of memory fragmentation

转载于:https://www.cnblogs.com/Dennymei/archive/2013/02/05/2892696.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值