From bd2ea0e1f84bb6522a66d44a072598153634cade Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 5 Jun 2018 15:48:25 +0800 Subject: [PATCH] add host_memory_profiling_cn.md --- .../optimization/host_memory_profiling_cn.md | 89 ++++++++++++++++++ .../optimization/memory_cpu_allocator.jpg | Bin 0 -> 77570 bytes .../optimization/memory_cpu_allocator.pdf | Bin 0 -> 26831 bytes .../optimization/memory_leak_protobuf.pdf | Bin 0 -> 40244 bytes .../optimization/memory_program_desc.png | Bin 0 -> 30768 bytes .../optimization/memory_protobuf_arena.png | Bin 0 -> 32597 bytes 6 files changed, 89 insertions(+) create mode 100644 doc/fluid/howto/optimization/host_memory_profiling_cn.md create mode 100644 doc/fluid/howto/optimization/memory_cpu_allocator.jpg create mode 100644 doc/fluid/howto/optimization/memory_cpu_allocator.pdf create mode 100644 doc/fluid/howto/optimization/memory_leak_protobuf.pdf create mode 100644 doc/fluid/howto/optimization/memory_program_desc.png create mode 100644 doc/fluid/howto/optimization/memory_protobuf_arena.png diff --git a/doc/fluid/howto/optimization/host_memory_profiling_cn.md b/doc/fluid/howto/optimization/host_memory_profiling_cn.md new file mode 100644 index 000000000..464368ec0 --- /dev/null +++ b/doc/fluid/howto/optimization/host_memory_profiling_cn.md @@ -0,0 +1,89 @@ +## 堆内存分析和优化 + +计算机程序都可能有内存泄露的风险。**内存泄露**一般是由于程序在堆(heap)上分配了内存而没有释放,随着程序的运行占用的内存越来越大,一方面会影响程序的稳定性,可能让运行速度越来越慢,或者造成oom,甚至会影响运行程序的机器的稳定性,造成宕机。 + + +目前有很多内存泄露分析工具,比较经典的有[valgrind](http://valgrind.org/docs/manual/quick-start.html#quick-start.intro), [gperftools](https://gperftools.github.io/gperftools/)。 + +因为Fluid是用Python驱动C++ core来运行,valgrind直接分析非常困难,需要自己编译debug版本的、带valgrind支持的专用版本,而且输出的信息中大部分是Python自己的符号和调用信息,分析起来很困难,所以不建议使用。 + +本教程主要介绍[gperftools](https://gperftools.github.io/gperftools/)的使用。 + +gperftool主要支持以下四个功能: + +- thread-caching malloc +- heap-checking using tcmalloc +- heap-profiling using tcmalloc +- CPU profiler + +Paddle也提供了基于gperftool的[CPU性能分析教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/cpu_profiling_cn.md)。 + +对于堆内存的分析,主要用到thread-caching malloc和heap-profiling using tcmalloc。 + +## 使用流程 +#### 环境 +本教程基于paddle提供的Docker开发环境paddlepaddle/paddle:latest-dev,基于Ubuntu 16.04.4 LTS环境。 + +#### 使用流程 + +- 安装google-perftools + +``` +apt-get install libunwind-dev +apt-get install google-perftools +``` + +- 安装pprof + +``` +go get -u github.com/google/pprof +``` + +- 设置运行环境 + +``` +export PPROF_PATH=/root/gopath/bin/pprof +export PPROF_BINARY_PATH=/root/gopath/bin/pprof +export LD_PRELOAD=/usr/lib/libtcmalloc.so.4 +``` + +- 使用heap profile来运行python程序。本质上是周期性的对堆的分配情况做一次快照。 + +``` +# HEAPPROFILE 设置生成的堆分析文件的目录和文件前缀 +# HEAP_PROFILE_ALLOCATION_INTERVAL 设置每分配多少存储dump一次dump,默认1GB +env HEAPPROFILE="./perf_log/test.log" HEAP_PROFILE_ALLOCATION_INTERVAL=209715200 python trainer.py +``` + +随着程序的运行,会在perf_log这个文件夹下生成很多文件,如下: + +``` +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0001.heap +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0002.heap +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0003.heap +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0004.heap +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0005.heap +-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0006.heap +``` + +- 使用pprof对heap文件进行分析。分析有两种模式: + - 完整模式。会对当前heap做一个分析,显示目前分配内存一些调用路径。 + + ``` + pprof --pdf python test.log.0012.heap + ``` + 上述命令会生成一个profile00x.pdf的文件,可以直接打开,例如:[allocator](./memory_cpu_allocator.pdf)。从下图可以看出,在CPU版本fluid的运行过程中,分配存储最多的模块式CPUAllocator. 而别的模块相对而言分配内存较少,所以被忽略了,这对于分配内存泄露是很不方便的,因为泄露是一个缓慢的过程,在这种图中是无法看到的。 + + ![result](memory_cpu_allocator.jpg) + + - Diff模式。可以对两个时刻的heap做diff,把一些内存分配没有发生变化的模块去掉,而把增量部分显示出来。 + ``` + pprof --pdf --base test.log.0010.heap python test.log.1045.heap + ``` + 生成的结果为:[`memory_leak_protobuf`](./memory_leak_protobuf.pdf) + + 从图中可以看出:ProgramDesc这个结构,在两个版本之间增长了200MB+,所以这里有很大的内存泄露的可能性,最终结果也确实证明是这里造成了泄露。 + + 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