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जिज्ञासा से मैंने मैट्रिक्स गुणा के कई अलग-अलग संस्करणों को कोड किया और इसके खिलाफ कैशग्रिंड चलाया। नीचे मेरे परिणामों में, मैं सोच रहा था कि कौन से हिस्से एल 1, एल 2, एल 3 यादें और संदर्भ थे और इसका क्या अर्थ है? मैट्रिक्स गुणाओं के लिए नीचे मेरा कोड भी है, अगर किसी को इसकी आवश्यकता हो।कैशिंग मिसिंग के लिए कैश्रिंड आउटपुट की व्याख्या कैसे करते हैं?
#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% )
मैट्रिक्स गुणा कोड।
#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