作者:gfree.wind@gmail.com
博客:blog.focus-linux.net linuxfocus.blog.chinaunix.net
博客:blog.focus-linux.net linuxfocus.blog.chinaunix.net
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Valgrind为一个debugging 和 profiling的工具包,检查内存问题只是其最知名的一个用途。今天介绍一下,valgrind工具包中的cachegrind。关于cachegrind的具体介绍,请参见valgrind的在线文档http://www.valgrind.org/docs/manual/cg-manual.html
下面使用一个古老的cache示例:
- #include <stdio.h>
- #include <stdlib.h>
- #define SIZE 100
-
int main(int argc, char **argv)
-
{
- int array[SIZE][SIZE] = {0};
- int i,j;
- #if 1
- for (i = 0; i < SIZE; ++i) {
- for (j = 0; j < SIZE; ++j) {
- array[i][j] = i + j;
- }
- }
- #else
- for (j = 0; j < SIZE; ++j) {
- for (i = 0; i < SIZE; ++i) {
- array[i][j] = i + j;
- }
- }
- #endif
- return 0;
- }
我使用条件编译,在没有打开任何优化开关的条件下,第一种情况生成文件为test1,第二种情况生成文件为test2。
下面是输出
- [fgao@fgao-vm-fc13 test]$ valgrind --tool=cachegrind ./test1
- ==2079== Cachegrind, a cache and branch-prediction profiler
- ==2079== Copyright (C) 2002-2009, and GNU GPL'd, by Nicholas Nethercote et al.
- ==2079== Using Valgrind-3.5.0 and LibVEX; rerun with -h for copyright info
- ==2079== Command: ./test1
- ==2079==
- ==2079==
- ==2079== I refs: 219,767
- ==2079== I1 misses: 614
- ==2079== L2i misses: 608
- ==2079== I1 miss rate: 0.27%
- ==2079== L2i miss rate: 0.27%
- ==2079==
- ==2079== D refs: 124,402 (95,613 rd + 28,789 wr)
- ==2079== D1 misses: 2,041 ( 621 rd + 1,420 wr)
- ==2079== L2d misses: 1,292 ( 537 rd + 755 wr)
- ==2079== D1 miss rate: 1.6% ( 0.6% + 4.9% )
- ==2079== L2d miss rate: 1.0% ( 0.5% + 2.6% )
- ==2079==
- ==2079== L2 refs: 2,655 ( 1,235 rd + 1,420 wr)
- ==2079== L2 misses: 1,900 ( 1,145 rd + 755 wr)
- ==2079== L2 miss rate: 0.5% ( 0.3% + 2.6% )
- [fgao@fgao-vm-fc13 test]$ valgrind --tool=cachegrind ./test2
- ==2080== Cachegrind, a cache and branch-prediction profiler
- ==2080== Copyright (C) 2002-2009, and GNU GPL'd, by Nicholas Nethercote et al.
- ==2080== Using Valgrind-3.5.0 and LibVEX; rerun with -h for copyright info
- ==2080== Command: ./test2
- ==2080==
- ==2080==
- ==2080== I refs: 219,767
- ==2080== I1 misses: 614
- ==2080== L2i misses: 608
- ==2080== I1 miss rate: 0.27%
- ==2080== L2i miss rate: 0.27%
- ==2080==
- ==2080== D refs: 124,402 (95,613 rd + 28,789 wr)
- ==2080== D1 misses: 1,788 ( 621 rd + 1,167 wr)
- ==2080== L2d misses: 1,292 ( 537 rd + 755 wr)
- ==2080== D1 miss rate: 1.4% ( 0.6% + 4.0% )
- ==2080== L2d miss rate: 1.0% ( 0.5% + 2.6% )
- ==2080==
- ==2080== L2 refs: 2,402 ( 1,235 rd + 1,167 wr)
- ==2080== L2 misses: 1,900 ( 1,145 rd + 755 wr)
- ==2080== L2 miss rate: 0.5% ( 0.3% + 2.6% )
这个结果其实是应该可以理解的。
1. 现在的CPU的cache是以line为单位的。这样,当数组的size不大时,第二种情况的循环,虽然没有使用局部性原则,但是并不会因此降低cache的命中率,并且可能可以迅速的将数据填到cache中
2. 现在的CPU的cache空间较大。这样,当数组的size不大时,即使没有使用局部性原则,也不会导致cache的频繁更新。
由于我对cache的理解,也比较粗浅,所以不能明确的指出这个结果的根本原因。根据上面的两个条件,基本上也可以理解为什么第二种情况更快。
为了使cachegrind的结果与传统的答案一样,我们就需要破坏上面两个条件。那么,现在将SIZE从100增大的1000。再次看一下输出结果:
- [fgao@fgao-vm-fc13 test]$ valgrind --tool=cachegrind ./test1
- ==2094== Cachegrind, a cache and branch-prediction profiler
- ==2094== Copyright (C) 2002-2009, and GNU GPL'd, by Nicholas Nethercote et al.
- ==2094== Using Valgrind-3.5.0 and LibVEX; rerun with -h for copyright info
- ==2094== Command: ./test1
- ==2094==
- ==2094==
- ==2094== I refs: 11,519,463
- ==2094== I1 misses: 617
- ==2094== L2i misses: 611
- ==2094== I1 miss rate: 0.00%
- ==2094== L2i miss rate: 0.00%
- ==2094==
- ==2094== D refs: 7,305,498 (6,038,310 rd + 1,267,188 wr)
- ==2094== D1 misses: 125,791 ( 621 rd + 125,170 wr)
- ==2094== L2d misses: 125,763 ( 595 rd + 125,168 wr)
- ==2094== D1 miss rate: 1.7% ( 0.0% + 9.8% )
- ==2094== L2d miss rate: 1.7% ( 0.0% + 9.8% )
- ==2094==
- ==2094== L2 refs: 126,408 ( 1,238 rd + 125,170 wr)
- ==2094== L2 misses: 126,374 ( 1,206 rd + 125,168 wr)
- ==2094== L2 miss rate: 0.6% ( 0.0% + 9.8% )
- [fgao@fgao-vm-fc13 test]$ valgrind --tool=cachegrind ./test2
- ==2095== Cachegrind, a cache and branch-prediction profiler
- ==2095== Copyright (C) 2002-2009, and GNU GPL'd, by Nicholas Nethercote et al.
- ==2095== Using Valgrind-3.5.0 and LibVEX; rerun with -h for copyright info
- ==2095== Command: ./test2
- ==2095==
- ==2095==
- ==2095== I refs: 11,519,463
- ==2095== I1 misses: 617
- ==2095== L2i misses: 611
- ==2095== I1 miss rate: 0.00%
- ==2095== L2i miss rate: 0.00%
- ==2095==
- ==2095== D refs: 7,305,498 (6,038,310 rd + 1,267,188 wr)
- ==2095== D1 misses: 1,063,300 ( 621 rd + 1,062,679 wr)
- ==2095== L2d misses: 116,261 ( 595 rd + 115,666 wr)
- ==2095== D1 miss rate: 14.5% ( 0.0% + 83.8% )
- ==2095== L2d miss rate: 1.5% ( 0.0% + 9.1% )
- ==2095==
- ==2095== L2 refs: 1,063,917 ( 1,238 rd + 1,062,679 wr)
- ==2095== L2 misses: 116,872 ( 1,206 rd + 115,666 wr)
- ==2095== L2 miss rate: 0.6% ( 0.0% + 9.1% )
总结一下:
1. 我们可以使用cachegrind来检查cache的命中率,提高程序性能;
2. 尽信书不如无书。书中的一些结果面对现在的环境,很可能是错误的。毕竟IT技术更新太快。还是自己动手实践一下更好!
注:Valgrind对于cache的测量,只是一种模拟。但是按照valgrind的文档,结果的可靠性还是有保证的。