原文地址:使用valgrind檢查cache命中率,提高程序性能 作者:GFree_Wind
======================================================================================================
下面使用一個古老的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;
- }
這個示例代碼從很久就開始用於說明利用局部性來增加cache的命中率。傳統的答案是第一個for循環的性能要優於第二個循環。
我使用條件編譯,在沒有打開任何優化開關的條件下,第一種情況生成文件爲test1,第二種情況生成文件爲test2。
下面是輸出
- [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% )
結果有點出人意料,第一種情況在D1的命中率反而低於第二種情況。
這個結果其實是應該可以理解的。
1. 現在的CPU的cache是以line爲單位的。這樣,當數組的size不大時,第二種情況的循環,雖然沒有使用局部性原則,但是並不會因此降低cache的命中率,並且可能可以迅速的將數據填到cache中
2. 現在的CPU的cache空間較大。這樣,當數組的size不大時,即使沒有使用局部性原則,也不會導致cache的頻繁更新。
由於我對cache的理解,也比較粗淺,所以不能明確的指出這個結果的根本原因。根據上面的兩個條件,基本上也可以理解爲什麼第二種情況更快。
爲了使cachegrind的結果與傳統的答案一樣,我們就需要破壞上面兩個條件。那麼,現在將SIZE從100增大的1000。再次看一下輸出結果:
- [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% )
對比紅色的兩行,第一種情況的miss率爲1.7%,而第二種情況的miss率高達14.5%。現在符合了傳統答案。
總結一下:
1. 我們可以使用cachegrind來檢查cache的命中率,提高程序性能;
2. 盡信書不如無書。書中的一些結果面對現在的環境,很可能是錯誤的。畢竟IT技術更新太快。還是自己動手實踐一下更好!
注:Valgrind對於cache的測量,只是一種模擬。但是按照valgrind的文檔,結果的可靠性還是有保證的。