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多次运行后令人困惑的性能统计结果

[英]Confusing perf stat results after multiple runs

I executed a 1000x1000 matrix multiplication code consecutively 6 times along with perf stat -e cache-misses command and got the following results 我与perf stat -e cache-misses命令一起连续执行了1000x1000矩阵乘法代码6次,并得到以下结果

Observation Cache-Misses Time elapsed(sec)
   1          48822173    7.697147087
   2          48663517    7.710045908
   3          48667119    7.701690126
   4          48867057    7.766267284
   5          48610651    7.701600681
   6          49203583    7.719180737 

As we can see here, cache-misses for observation 1 is greater than cache-misses in observation 2,3 & 5. But the elapsed time for observation 1 is lesser than observation 2, 3 & 5. On the other hand observation 4 has highest elapsed time among all these observations but cache-misses for observation 4 is lesser than observation 3 and observation 6. According to the textbook, increasing cache-misses elongate the execution time of a program. 正如我们在这里看到的,观察1的缓存丢失比观察2、3和5中的缓存丢失大。但是,观察1的经过时间比观察2、3和5短。在所有这些观察中,经过时间最长,但观察4的高速缓存丢失比观察3和观察6短。根据教科书,增加的高速缓存丢失会延长程序的执行时间。 Then how we can explain this behavior? 那么我们如何解释这种行为呢? Thanks 谢谢

Here is my system details: 这是我的系统详细信息:

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                4
On-line CPU(s) list:   0-3
Thread(s) per core:    2
Core(s) per socket:    2
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 42
Stepping:              7
CPU MHz:               2300.000
BogoMIPS:              4589.89
Virtualization:        VT-x
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              3072K
NUMA node0 CPU(s):     0-3

Several tools exist to find the root-cause of your cache misses. 存在多种工具来查找缓存未命中的根本原因。 But a lot of misses does not always mean longer execution time. 但是很多遗漏并不总是意味着更长的执行时间。 It depends also on cache-miss level. 它还取决于缓存未命中级别。

Moreover, it is recommended to do one or two observations runs without collecting statistics to warm caches (ie, filling them with data): subsequent runs will benefit from the first one which had warmed up the cache with necessary data. 此外,建议执行一次或两次观察运行,而无需收集统计信息以预热缓存(即,用数据填充它们):后续运行将受益于第一个运行时使用必要数据预热了缓存的数据。

A tool like dprof can help you to find causes and performances problems due to cache-misses. 诸如dprof类的工具可以帮助您查找由于缓存丢失而导致的原因和性能问题。 Try it. 试试吧。

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