I think in my test code, what affect the performance maybe the cpu branches
misses? The row num is bigger than the column num, so in column-wise
traverse, the inner loop is easier to predict.
perf result:
row-wise
0.034014712 741,811 branch-misses #
1.05% of all branches (37.17%)
column-wise
0.028383333 27,624 branch-misses #
0.04% of all branches (45.29%)
在 2019年9月29日星期日 UTC+8下午11:35:52，Michael Stiller写道：
>
> On my machine using an intel 9880H with a L2 Cache: Unified, 256 KiB,
> 4-way set associative,
>
> rows vs. columns performance is basically the same as long as the array
> size fits into the L2 cache.
>
> This seems to be the case for a rowSize = colSize = 180. For slightly
> higher values (190) the
> column bench gets slower as the row bench.
>
> If you print the array sizes using
>
> fmt.Println("array size:", unsafe.Sizeof(array))
>
> you get:
>
> 180: array size: 259200
>
> 190: array size: 288800
>
> If log the array’s element addresses to the code like this:
>
> for r := 0; r < rowSize; r++ {
> for c := 0; c < colSize; c++ {
> log.Printf("rows: %x", &array[r][c])
> sum += array[r][c]
> }
> }
>
> and
>
> for c := 0; c < colSize; c++ {
> for r := 0; r < rowSize; r++ {
> log.Printf("cols: %x", &array[r][c])
> sum += array[r][c]
> }
> }
>
> It seems that the “row method” looks more cache (line) friendly:
>
> 2019/09/29 17:33:59 rows: 1198400
> 2019/09/29 17:33:59 rows: 1198408
> 2019/09/29 17:33:59 rows: 1198410
> 2019/09/29 17:33:59 rows: 1198418
> 2019/09/29 17:33:59 rows: 1198420
> 2019/09/29 17:33:59 rows: 1198428
> 2019/09/29 17:33:59 rows: 1198430
> …
>
> 2019/09/29 17:33:59 cols: 1198400
> 2019/09/29 17:33:59 cols: 1198450
> 2019/09/29 17:33:59 cols: 11984a0
> 2019/09/29 17:33:59 cols: 11984f0
> 2019/09/29 17:33:59 cols: 1198540
> 2019/09/29 17:33:59 cols: 1198590
> 2019/09/29 17:33:59 cols: 11985e0
> 2019/09/29 17:33:59 cols: 1198630
> …
>
> Cheers,
>
> -Michael
>
>
>
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