出于好奇,我跑了几个不同版本的矩阵乘法,并运行cachegrind反对它.在下面的结果中,我想知道哪些部分是L1,L2,L3缺失和引用,这些是什么意思？以下是我的矩阵乘法代码,万一有人需要.
#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,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,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: 3,713,059 ==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 (1,112,301 rd + 130,123,285 wr) ==9174== D1 misses: 63,285,950 ( 62,987,684 rd + 298,266 wr) ==9174== LLd misses: 53,867 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,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,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 (1,610,303 rd + 132,497,040 wr) ==8438== D1 misses: 339,977 ( 42,583 rd + 297,394 wr) ==8438== LLd misses: 53,114 ( 5,866 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,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,int size) {
float curr = 0;
for (int i=0;i<size;i++)
curr += A[i] * B[i];
return curr;
}
void multiply (float *A,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,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
解决方法
根据
documentation cachegrind只能模拟第一个和最后一个级别的缓存：
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.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,但只能使用L1和L3.
cachegrind输出的第一部分报告有关L1指令缓存的信息.在所有示例中,L1指令缓存未命中的数量是不明确的,错过率始终为0％.这意味着您的所有程序都适合您的L1指令缓存.
输出的第二部分报告关于L1和LL(最后一级缓存,在您的情况下为L3)数据高速缓存的信息.使用D1错误率：信息,你应该看到哪个版本的矩阵乘法算法是“最高缓存效率”
cachegrind输出的最后部分总结了关于指令和数据的LL(最后一级缓存,在您的情况下为L3)的信息.因此,它给出了缓存服务的存储器访问次数和存储器请求的百分比.
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