您如何解释cachegrind输出的缓存未命中? [英] How do you interpret cachegrind output for caching misses?
问题描述
出于好奇，我编写了几种不同版本的矩阵乘法，并对其进行了cachegrind.在下面的结果中，我想知道L1，L2，L3缺少哪些部分和参考，这到底意味着什么?如果有人需要，下面也是我的矩阵乘法代码.
Out of curiosity I ran coded up several different versions of matrix Multiplication and ran cachegrind against it. In my results below, I was wondering which parts were L1,L2,L3 misses and references and what it all really means? Below is my code for the matrix multiplications also, in case anyone needs that.
#define SLOWEST
==6933== Cachegrind, a cache and branch-prediction profiler
==6933== Copyright (C) 2002-2012, and GNU GPL'd, by Nicholas Nethercote et al.
==6933== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==6933== Command: ./a.out 500
==6933==
--6933-- warning: L3 cache found, using its data for the LL simulation.
--6933-- warning: pretending that LL cache has associativity 24 instead of actual 16
Multiplied matrix A and B in 60.7487 seconds.
==6933==
==6933== I refs: 6,039,791,314
==6933== I1 misses: 1,611
==6933== LLi misses: 1,519
==6933== I1 miss rate: 0.00%
==6933== LLi miss rate: 0.00%
==6933==
==6933== D refs: 2,892,704,678 (2,763,005,485 rd + 129,699,193 wr)
==6933== D1 misses: 136,223,560 ( 136,174,705 rd + 48,855 wr)
==6933== LLd misses: 53,675 ( 5,247 rd + 48,428 wr)
==6933== D1 miss rate: 4.7% ( 4.9% + 0.0% )
==6933== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==6933==
==6933== LL refs: 136,225,171 ( 136,176,316 rd + 48,855 wr)
==6933== LL misses: 55,194 ( 6,766 rd + 48,428 wr)
==6933== LL miss rate: 0.0% ( 0.0% + 0.0% )
#define SLOWER
==8463== Cachegrind, a cache and branch-prediction profiler
==8463== Copyright (C) 2002-2012, and GNU GPL'd, by Nicholas Nethercote et al.
==8463== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==8463== Command: ./a.out 500
==8463==
--8463-- warning: L3 cache found, using its data for the LL simulation.
--8463-- warning: pretending that LL cache has associativity 24 instead of actual 16
Multiplied matrix A and B in 49.7397 seconds.
==8463==
==8463== I refs: 4,537,213,120
==8463== I1 misses: 1,571
==8463== LLi misses: 1,487
==8463== I1 miss rate: 0.00%
==8463== LLi miss rate: 0.00%
==8463==
==8463== D refs: 2,891,485,608 (2,761,862,312 rd + 129,623,296 wr)
==8463== D1 misses: 59,961,522 ( 59,913,256 rd + 48,266 wr)
==8463== LLd misses: 53,113 ( 5,246 rd + 47,867 wr)
==8463== D1 miss rate: 2.0% ( 2.1% + 0.0% )
==8463== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==8463==
==8463== LL refs: 59,963,093 ( 59,914,827 rd + 48,266 wr)
==8463== LL misses: 54,600 ( 6,733 rd + 47,867 wr)
==8463== LL miss rate: 0.0% ( 0.0% + 0.0% )
#define SLOW
==9174== Cachegrind, a cache and branch-prediction profiler
==9174== Copyright (C) 2002-2012, and GNU GPL'd, by Nicholas Nethercote et al.
==9174== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==9174== Command: ./a.out 500
==9174==
--9174-- warning: L3 cache found, using its data for the LL simulation.
--9174-- warning: pretending that LL cache has associativity 24 instead of actual 16
Multiplied matrix A and B in 35.8901 seconds.
==9174==
==9174== I refs: 3,039,713,059
==9174== I1 misses: 1,570
==9174== LLi misses: 1,486
==9174== I1 miss rate: 0.00%
==9174== LLi miss rate: 0.00%
==9174==
==9174== D refs: 1,893,235,586 (1,763,112,301 rd + 130,123,285 wr)
==9174== D1 misses: 63,285,950 ( 62,987,684 rd + 298,266 wr)
==9174== LLd misses: 53,113 ( 5,246 rd + 47,867 wr)
==9174== D1 miss rate: 3.3% ( 3.5% + 0.2% )
==9174== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==9174==
==9174== LL refs: 63,287,520 ( 62,989,254 rd + 298,266 wr)
==9174== LL misses: 54,599 ( 6,732 rd + 47,867 wr)
==9174== LL miss rate: 0.0% ( 0.0% + 0.0% )
#define MEDIUM
==7838== Cachegrind, a cache and branch-prediction profiler
==7838== Copyright (C) 2002-2012, and GNU GPL'd, by Nicholas Nethercote et al.
==7838== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==7838== Command: ./a.out 500
==7838==
--7838-- warning: L3 cache found, using its data for the LL simulation.
--7838-- warning: pretending that LL cache has associativity 24 instead of actual 16
Multiplied matrix A and B in 23.4097 seconds.
==7838==
==7838== I refs: 2,548,967,151
==7838== I1 misses: 1,610
==7838== LLi misses: 1,522
==7838== I1 miss rate: 0.00%
==7838== LLi miss rate: 0.00%
==7838==
==7838== D refs: 1,399,237,303 (1,267,363,440 rd + 131,873,863 wr)
==7838== D1 misses: 592,807 ( 293,091 rd + 299,716 wr)
==7838== LLd misses: 53,147 ( 5,248 rd + 47,899 wr)
==7838== D1 miss rate: 0.0% ( 0.0% + 0.2% )
==7838== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==7838==
==7838== LL refs: 594,417 ( 294,701 rd + 299,716 wr)
==7838== LL misses: 54,669 ( 6,770 rd + 47,899 wr)
==7838== LL miss rate: 0.0% ( 0.0% + 0.0% )
#define MEDIUMISH
==8438== Cachegrind, a cache and branch-prediction profiler
==8438== Copyright (C) 2002-2012, and GNU GPL'd, by Nicholas Nethercote et al.
==8438== Using Valgrind-3.8.1 and LibVEX; rerun with -h for copyright info
==8438== Command: ./a.out 500
==8438==
--8438-- warning: L3 cache found, using its data for the LL simulation.
--8438-- warning: pretending that LL cache has associativity 24 instead of actual 16
Multiplied matrix A and B in 24.0327 seconds.
==8438==
==8438== I refs: 2,550,211,553
==8438== I1 misses: 1,576
==8438== LLi misses: 1,488
==8438== I1 miss rate: 0.00%
==8438== LLi miss rate: 0.00%
==8438==
==8438== D refs: 1,400,107,343 (1,267,610,303 rd + 132,497,040 wr)
==8438== D1 misses: 339,977 ( 42,583 rd + 297,394 wr)
==8438== LLd misses: 53,114 ( 5,248 rd + 47,866 wr)
==8438== D1 miss rate: 0.0% ( 0.0% + 0.2% )
==8438== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==8438==
==8438== LL refs: 341,553 ( 44,159 rd + 297,394 wr)
==8438== LL misses: 54,602 ( 6,736 rd + 47,866 wr)
==8438== LL miss rate: 0.0% ( 0.0% + 0.0% )
矩阵乘法代码.
#if defined(SLOWEST)
void multiply (float **A, float **B, float **out, int size) {
for (int row=0;row<size;row++)
for (int col=0;col<size;col++)
for (int in=0;in<size;in++)
out[row][col] += A[row][in] * B[in][col];
}
// Takes in 1-D arrays, same as before.
#elif defined(SLOWER)
void multiply (float *A, float *B, float *out, int size) {
for (int row=0;row<size;row++)
for (int col=0;col<size;col++)
for (int in=0;in<size;in++)
out[row * size + col] += A[row * size + in] * B[in * size + col];
}
// Flips first and second loops
#elif defined(SLOW)
void multiply (float *A, float *B, float *out, int size) {
for (int col=0;col<size;col++)
for (int row=0;row<size;row++) {
float curr = 0; // prevents from calculating position each time through
for (int in=0;in<size;in++)
curr += A[row * size + in] * B[in *size + col];
out[row * size + col] = curr;
}
}
#elif defined(MEDIUM)
// Keeps it organized for future codes.
float dotProduct(float *A, float *B, int size) {
float curr = 0;
for (int i=0;i<size;i++)
curr += A[i] * B[i];
return curr;
}
void multiply (float *A, float *B, float *out, int size) {
float *temp = new float[size];
for (int col=0;col<size;col++) {
for (int i=0;i<size;i++) // stores column into sequential array
temp[i] = B[i * size + col];
for (int row=0;row<size;row++)
out[row * size + col] = dotProduct(&A[row], temp, size); // uses function above for dot product.
}
delete[] temp;
}
#elif defined(MEDIUMISH)
float dotProduct(float *A, float *B, int size) {
float curr = 0;
for (int i=0;i<size;i++)
curr += A[i] * B[i];
return curr;
}
void multiply (float *A, float *B, float *out, int size) {
for (int i=0;i<size-1;i++)
for (int j=i+1;j<size;j++)
std::swap(B[i * size + j], B[j * size + i]);
for (int col=0;col<size;col++)
for (int row=0;row<size;row++)
out[row * size + col] = dotProduct(&A[row], &B[row], size); // uses function above for dot product.
}
#elif defined(FAST)
#elif defined(FASTER)
#endif
推荐答案
根据文档 cachegrind仅模拟第一级和最后一级缓存:
According to the documentation cachegrind only simulate the first and the last level caches:
Cachegrind模拟您的程序如何与计算机的缓存进行交互 层次结构和(可选)分支预测变量.它模拟一台机器 具有独立的第一级指令和数据缓存(I1和D1)， 由统一的二级缓存(L2)支持.这完全符合 许多现代机器的配置.
Cachegrind simulates how your program interacts with a machine's cache hierarchy and (optionally) branch predictor. It simulates a machine with independent first-level instruction and data caches (I1 and D1), backed by a unified second-level cache (L2). This exactly matches the configuration of many modern machines.
但是，某些现代计算机具有三或四个级别的缓存.为了 这些机器(在Cachegrind可以自动检测 缓存配置)Cachegrind模拟第一级 最后一级的缓存.之所以选择此选项，是因为最后一级 缓存对运行时的影响最大，因为它会屏蔽对main的访问 记忆.此外，L1缓存通常具有较低的关联性，因此 模拟它们可以检测代码与之交互不良的情况 此缓存(例如，按行长度逐列遍历矩阵 是2的幂.
However, some modern machines have three or four levels of cache. For these machines (in the cases where Cachegrind can auto-detect the cache configuration) Cachegrind simulates the first-level and last-level caches. The reason for this choice is that the last-level cache has the most influence on runtime, as it masks accesses to main memory. Furthermore, the L1 caches often have low associativity, so simulating them can detect cases where the code interacts badly with this cache (eg. traversing a matrix column-wise with the row length being a power of 2).
这意味着您无法获取L2信息，而只能获取L1和L3.
What that means is that you can't get L2 information but only L1 and L3 in your case.
cachegrind输出的第一部分报告有关L1指令缓存的信息.在您的所有示例中，L1指令高速缓存未命中的数量都是微不足道的，未命中率始终为0％.这意味着您所有的程序都适合您的L1指令缓存.
The first part of cachegrind's output reports information about L1 instructions cache. In all your example, the number of L1 instruction caches misses is insignifiant, the miss rate is always 0%. It means that all your programs fit in your L1 instruction cache.
输出的第二部分报告有关L1和LL(最后一级缓存，在您情况下为L3)数据缓存的信息.使用 D1丢失率:信息，您应该看到哪个矩阵乘法算法是最有效的缓存"
The second part of the output reports information about L1 and LL (last level cache, L3 in your case) data caches. Using the D1 miss rate: information you should see which version of your matrix multiplication algorithm is "the most cache efficient"
cachegrind输出的最后部分总结了有关指令和数据的LL(最后一级缓存，在您的情况下为L3)的信息.因此，它给出了内存访问的数量以及由缓存服务的内存请求的百分比.
The final part of cachegrind output summs up information about LL (last level cache, L3 in your case) for both instructions and data. It thus gives the number of memory accesses and the percentage of memory requests served by the cache.
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