fortran矩阵向量乘法优化 [英] fortran matrix vector multiplication optimization

问题描述

我试图测量Fortran中不同矩阵向量乘法方案的差异.我实际上已经编写了以下代码: http://pastebin.com/dmKXdnX6

I tried to measure the difference of different matrix-vector-multiplication schemes in Fortran. I have actually written the following code: http://pastebin.com/dmKXdnX6

优化版本"旨在通过交换循环以访问矩阵元素来尊重矩阵的内存布局.所提供的代码应使用gfortran进行编译，并以以下相当令人惊讶的结果运行:

The 'optimized version' is meant to respect the memory layout of the matrix, by swapping the loops to access the matrix-elements. The provided code should compile with gfortran and it runs with the following rather surprising results:

Vectors match! Calculations are OK.
Optimized time:  0.34133333333333332     
Naive time:   1.4133333333333331E-002
Ratio (t_optimized/t_naive):   24.150943396226417 

我可能犯了一个令人尴尬的错误，但我无法发现它.我希望其他人能帮助我.

I've probably made an embarrassing mistake, but I'm unable to spot it. I hope someone else can help me.

我知道fortran提供了一些优化的版本，但出于好奇，我正在对此进行评估.

I know that there are optimized versions provided by fortran, but I'm measuring this just out of curiosity.

预先感谢.

推荐答案

好吧，这是一个简单的偏执问题:

Well, it's a simple matter of paranthesis:

t_optimized = t2-t1/iterations

肯定是错误的...您可能是说

is most certainly wrong... You probably mean

t_optimized = (t2-t1)/iterations

因此，我得到了〜2的加速.

With that I get a speed-up of ~2.

我需要纠正/调整的其他几件事:

A couple of other things I needed to correct/adjust:

• 第一个循环是错误的，您正在尝试将元素设置在边界之外.它应显示为:

A(j,i) = (-1.0)**(i-j) 

• 现代编译器非常智能.他们可能注意到您没有在循环体内更改函数调用的输入.然后，他们可以优化整个循环！为了防止这种情况，我插入了以下行:

do i = 1,iterations
  call optimized(A, m, n, x, y1)  
  x(1:n) = y1
end do

(与 y2 相同).不要忘记在每个基准测试开始时重新初始化 x .

(and the same for y2). Don't forget to re-initialize x at the beginning of each benchmark.

• 不要使用; 那么多-除非您想在一行上放置多个语句，否则不需要使用
• 不要在Fortran中使用制表符-有些编译器不喜欢它-而是使用空格
• Don't use ; that much - it is not required unless you want to put multiple statements on one line
• Don't use tabs in Fortran - some compilers don't like it - use whitespaces instead

这篇关于fortran矩阵向量乘法优化的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！