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Lecture 10

# Al locality items with nearby addresses tend to be

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Unformatted text preview: on Why Caches Work     Locality: Programs tend to use data and instruc?ons with addresses near or equal to those they have used recently Temporal locality:   Recently referenced items are likely to be referenced again in the near future   block Spa?al locality:   Items with nearby addresses tend to be referenced close together in :me   How do caches take advantage of this? Caches and Locality block University of Washington Example: Locality? sum = 0; for (i = 0; i < n; i++) sum += a[i]; return sum;   Data:   Temporal: sum referenced in each itera:on   Spa:al: array a accessed in stride ­1 paTern   Instruc?ons:   Temporal: cycle through loop repeatedly   Spa:al: reference instruc:ons in sequence   Being able to assess the locality of code is a crucial skill for a programmer Caches and Locality University of Washington Another Locality Example int sum_array_3d(int a[M][N][N]) { int i, j, k, sum = 0; }     for (i = 0; i < N; i++) for (j = 0; j < N; j++) for (k = 0; k < M; k++) sum += a[k][i][j]; return sum; What is wrong with this code? How can it be ﬁxed? Caches and Locality University of Washington Memory and Caches           Cache basics Principle of locality Memory hierarchies Cache organiza?on Program op?miza?ons that consider caches Caches  ­ Memory Hierarchy University of Washington Cost of Cache Misses   Huge diﬀerence between a hit and a miss   Could be 100x, if just L1 and main memory   Would you believe 99% hits is twice as good as 97%?   Consider: Cache hit :me of 1 cycle Miss penalty of 100 cycles   Average access :me:       97% hits: 1 cycle + 0.03 * 100 cycles = 4 cycles 99% hits: 1 cycle + 0.01 * 100 cycles = 2 cycles This is why “miss rate” is used instead of “hit rate” Caches  ­ Memory Hierarchy University of Washington Cache...
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