c++-内存管理-debug_allocator

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博客围绕C++的内存管理展开，重点提及debug_allocator。在C++编程中，内存管理至关重要，debug_allocator有助于调试和优化内存使用，保障程序的稳定性和性能。

蓝牙--关于Bta_gattc_api.cc文件的讲解
专业开发者的博客，一个从事Android WiFi和蓝牙的工程师
09-10 408
GATT是低功耗蓝牙属性应用规范，应用于主机和从设备之间的数据传输。
蓝牙--关于bta_av_main.cc文件的讲解
专业开发者的博客，一个从事Android WiFi和蓝牙的工程师
09-09 882
上层在建立A2dp connect连接的时候，先调用到btif层的btif_av.cc中函数src_connect_sink，接着调用bta层bta_av_api.cc中函数BTA_AvOpen，经过内部state machine处理，最终调用Stack层l2c_api.c中的函数L2CA_ErtmConnectReq。这样就在bluedroid中的连接过程就完成了。这里我们重点讲解下bta_av_main.cc 的代码(BTA AV的主要实施文件)逻辑。BTA：提供各种profile的实现。
c++ 内存管理（3）allocator的设计与应用
weixin_37651557的博客
11-29 665
本文根据《侯捷–c++内存管理》一课进行学习总结： allocator的演变 第一个版本是我们自己在类里面进行内存申请，先申请一大块chunk大小的内存空间，再用链表将chunk中切成块，每一块是一个screenChunk=24的大小，用linked list的方式进行连接。 第二个版本和第一个版本类似，区别就是使用了union，并用一个*next作为头指针（指向第二块作为开始）。 第三个版本，将内存分配封装起来，由allocator统一分配处理。 // 第三个版本 class alloc
c++内存管理（2）std::allocator
kaikai_sk的博客
05-23 263
vc6 malloc
CUJ：标准库：调试用的Allocator
taodm的专栏
04-02 3658
The Standard Librarian ：A Debugging AllocatorMatt Austernhttp://www.cuj.com/experts/1912/austern.htm?topic=experts--------------------------------------------------------------------------------
C++ allocator内存管理（一）
ylh的博客
12-20 612
标准库allocator类定义在头文件memory中，他帮助我们把内存分配和对象的构造分离开来，提供一种类型感知的内存分配的方法。分配之后，p是string* 类型。相当于new string[ 3 ]，但是使用alloc分配的内存是未构造的。因此，在使用alloc分配的内存我们需要使用，来完成初始化或者赋值。正确方式：这样我们就可以完成对分配的内存构造成对象，并且进行赋值。注意：我们的，后面的参数为可变参数，表示接受一个，在这里我们指定它接受默认的string的构造函数。
C++内存管理(二)allocator内存分配器
ai_XZP_master
04-13 323
VC6 malloc 内存管理目的是使效率得到更高，空间利用要精简。 VC6标准分配器之实现 BC5标准分配器实现
C:\Users\25385\Documents\build-Elgamal-Desktop_Qt_5_12_10_MinGW_64_bit-Debug\debug\widget.o:-1: In function `void std::allocator_traits<std::allocator<long long> >::construct<long long, long long const&>(std::allocator<long long>&, long long*, long long const&)':
05-06
2. 确保使用的allocator符合C++标准，特别是在Mingw环境下，可能需要调整allocator的实现，如引用[1]中提到的添加std::命名空间。 3. 检查编译器版本和标准库实现，确认是否支持C++11或更高版本中的allocator_traits...
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-external:W0 /DWIN32 /D_WINDOWS /EHsc /Zc:__cplusplus /bigobj /FS /utf-8 -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE /wd4624 /wd4068 /wd4067 /wd4267 /wd4661 /wd4717 /wd4244 /wd4804 /wd4273 -DHAVE_AVX512_CPU_DEFINITION -DHAVE_AVX2_CPU_DEFINITION /Zc:preprocessor /Zc:preprocessor /Zc:preprocessor /O2 /Ob2 /DNDEBUG /bigobj -DNDEBUG -std:c++17 -MD /permissive- /EHsc /bigobj -O2 /utf-8 /showIncludes /Focaffe2\torch\CMakeFiles\torch_python.dir\csrc\Module.cpp.obj /Fdcaffe2\torch\CMakeFiles\torch_python.dir\ /FS -c E:\PyTorch_Build\pytorch\torch\csrc\Module.cpp E:\PyTorch_Build\pytorch\torch\csrc\Module.cpp(34): fatal error C1083: 无法打开包括文件: “libshm.h”: No such file or directory [4872/7459] Building CUDA object caffe2\CMakeFiles\torch_cuda.dir\__\aten\src\ATen\native\cuda\group_norm_kernel.cu.obj group_norm_kernel.cu tmpxft_00005adc_00000000-7_group_norm_kernel.cudafe1.cpp [4873/7459] Building CUDA object caffe2\CMakeFiles\torch_cuda.dir\__\aten\src\ATen\UfuncCUDA_add.cu.obj UfuncCUDA_add.cu tmpxft_00006bdc_00000000-7_UfuncCUDA_add.cudafe1.cpp [4875/7459] Building CUDA object caffe2\CMakeFiles\torch_cuda.dir\__\aten\src\ATen\native\cuda\Unique.cu.obj Unique.cu tmpxft_00000658_00000000-7_Unique.cudafe1.cpp ninja: build stopped: subcommand failed. (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch>” “PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env Error: [Errno 13] Permission denied: 'E:\\PyTorch_Build\\pytorch\\rtx5070_env\\Scripts\\python.exe' PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> conda install -c conda-forge openblas 3 channel Terms of Service accepted Retrieving notices: done Channels: - conda-forge - defaults - nvidia - pytorch-nightly Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done ## Package Plan ## environment location: C:\Miniconda3 added / updated specs: - openblas The following packages will be downloaded: package | build ---------------------------|----------------- libopenblas-0.3.30 |pthreads_ha4fe6b2_2 3.8 MB conda-forge openblas-0.3.30 |pthreads_h4a7f399_2 262 KB conda-forge ------------------------------------------------------------ Total: 4.0 MB The following NEW packages will be INSTALLED: libopenblas conda-forge/win-64::libopenblas-0.3.30-pthreads_ha4fe6b2_2 openblas conda-forge/win-64::openblas-0.3.30-pthreads_h4a7f399_2 Proceed ([y]/n)? y Downloading and Extracting Packages: Preparing transaction: done Verifying transaction: done Executing transaction: done (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置cuDNN路径 (rtx5070_env) PS E:\PyTorch_Build\pytorch> @" >> set(CUDNN_INCLUDE_DIR "E:/Program Files/NVIDIA/CUNND/v9.12/include/13.0") >> set(CUDNN_LIBRARY "E:/Program Files/NVIDIA/CUNND/v9.12/lib/13.0/x64/cudnn64_9.lib") >> message(STATUS "Applied custom cuDNN settings") >> "@ | Set-Content set_cudnn.cmake (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 在CMakeLists.txt第一行插入引用 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $cmakeFile = "CMakeLists.txt" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $content = Get-Content $cmakeFile (rtx5070_env) PS E:\PyTorch_Build\pytorch> $newContent = @() (rtx5070_env) PS E:\PyTorch_Build\pytorch> $newContent += "include(set_cudnn.cmake)" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $newContent += $content (rtx5070_env) PS E:\PyTorch_Build\pytorch> $newContent | Set-Content $cmakeFile (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装OpenBLAS (rtx5070_env) PS E:\PyTorch_Build\pytorch> conda install -c conda-forge openblas -y 3 channel Terms of Service accepted Channels: - conda-forge - defaults - nvidia - pytorch-nightly Platform: win-64 Collecting package metadata (repodata.json): done Solving environment: done # All requested packages already installed. (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "✅ 构建环境优化完成" -ForegroundColor Green ✅ 构建环境优化完成 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 检查GPU支持状态 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -c "import torch; print(f'CUDA可用: {torch.cuda.is_available()}')" Traceback (most recent call last): File "<string>", line 1, in <module> File "E:\PyTorch_Build\pytorch\torch\__init__.py", line 415, in <module> from torch._C import * # noqa: F403 ImportError: DLL load failed while importing _C: 找不到指定的模块。 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 查看已完成任务比例 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $total = 7459 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $completed = (Get-ChildItem build -Recurse -File | Measure-Object).Count (rtx5070_env) PS E:\PyTorch_Build\pytorch> $percent = [math]::Round(($completed/$total)*100, 2) (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "构建进度: $completed/$total ($percent%)" 构建进度: 12552/7459 (168.28%) (rtx5070_env) PS E:\PyTorch_Build\pytorch>”
最新发布
09-04
注意：在Windows上，PyTorch通常使用文件映射而不是共享内存，因此这些函数在Windows上可能不会被调用。但是为了通过编译，我们需要提供声明。 创建`libshm.h`文件的内容如下（最小化实现）： ```c #pragma once ...
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