这种问题反复出现，在Stack Overflow上应该比“MATLAB使用高度优化的库”或“MATLAB使用MKL”更清楚地回答。
历史:
矩阵乘法(连同矩阵-向量、向量-向量乘法和许多矩阵分解)是线性代数中最重要的问题。工程师们从早期开始就一直在用计算机解决这些问题。
I'm not an expert on the history, but apparently back then, everybody just rewrote his FORTRAN version with simple loops. Some standardization then came along, with the identification of "kernels" (basic routines) that most linear algebra problems needed in order to be solved. These basic operations were then standardized in a specification called: Basic Linear Algebra Subprograms (BLAS). Engineers could then call these standard, well-tested BLAS routines in their code, making their work much easier.
布拉斯特区:
BLAS从第1级(定义标量-向量和向量-向量运算的第一个版本)发展到第2级(向量-矩阵运算)，再到第3级(矩阵-矩阵运算)，并提供了越来越多的“核心”，从而标准化了越来越多的基本线性代数运算。最初的FORTRAN 77实现仍然可以在Netlib的网站上找到。
为了更好的表现:
因此，多年来(特别是在BLAS级别1和级别2发布之间:80年代初)，随着矢量操作和缓存层次结构的出现，硬件发生了变化。这些演进使得大幅度提高BLAS子例程的性能成为可能。然后，不同的供应商也推出了他们越来越高效的BLAS例程实现。
我不知道所有历史上的实现(那时候我还没出生，也不是孩子)，但最著名的两个实现出现在21世纪初:英特尔MKL和GotoBLAS。你的Matlab使用的是英特尔MKL，这是一个非常好的，优化的BLAS，这就解释了你所看到的出色性能。
矩阵乘法的技术细节:
那么为什么Matlab (MKL)在dgemm(双精度一般矩阵-矩阵乘法)上如此之快?简单来说:因为它使用了向量化和良好的数据缓存。用更复杂的术语来说:请参阅乔纳森•摩尔(Jonathan Moore)提供的文章。
Basically, when you perform your multiplication in the C++ code you provided, you are not at all cache-friendly. Since I suspect you created an array of pointers to row arrays, your accesses in your inner loop to the k-th column of "matice2": matice2[m][k] are very slow. Indeed, when you access matice2[0][k], you must get the k-th element of the array 0 of your matrix. Then in the next iteration, you must access matice2[1][k], which is the k-th element of another array (the array 1). Then in the next iteration you access yet another array, and so on... Since the entire matrix matice2 can't fit in the highest caches (it's 8*1024*1024 bytes large), the program must fetch the desired element from main memory, losing a lot of time.
如果您只是调换了矩阵的位置，以便访问相邻的内存地址，那么您的代码将运行得更快，因为现在编译器可以同时在缓存中加载整个行。试试这个修改过的版本:
timer.start();
float temp = 0;
//transpose matice2
for (int p = 0; p < rozmer; p++)
{
for (int q = 0; q < rozmer; q++)
{
tempmat[p][q] = matice2[q][p];
}
}
for(int j = 0; j < rozmer; j++)
{
for (int k = 0; k < rozmer; k++)
{
temp = 0;
for (int m = 0; m < rozmer; m++)
{
temp = temp + matice1[j][m] * tempmat[k][m];
}
matice3[j][k] = temp;
}
}
timer.stop();
So you can see how just cache locality increased your code's performance quite substantially. Now real dgemm implementations exploit that to a very extensive level: They perform the multiplication on blocks of the matrix defined by the size of the TLB (Translation lookaside buffer, long story short: what can effectively be cached), so that they stream to the processor exactly the amount of data it can process. The other aspect is vectorization, they use the processor's vectorized instructions for optimal instruction throughput, which you can't really do from your cross-platform C++ code.
最后，有人声称这是因为Strassen's或Coppersmith-Winograd算法是错误的，这两个算法在实践中都是不可实现的，因为上面提到的硬件方面的考虑。
