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    <p>For many reasons.</p> <p>First, Fortran compilers are highly optimized, and the language allows them to be as such. C and C++ are very loose in terms of array handling (e.g. the case of pointers referring to the same memory area). This means that the compiler cannot know in advance what to do, and is forced to create generic code. In Fortran, your cases are more streamlined, and the compiler has better control of what happens, allowing him to optimize more (e.g. using registers).</p> <p>Another thing is that Fortran store stuff columnwise, while C stores data row-wise. I havent' checked your code, but be careful of how you perform the product. In C you must scan row wise: this way you scan your array along contiguous memory, reducing the cache misses. Cache miss is the first source of inefficiency.</p> <p>Third, it depends of the blas implementation you are using. Some implementations might be written in assembler, and optimized for the specific processor you are using. The netlib version is written in fortran 77.</p> <p>Also, you are doing a lot of operations, most of them repeated and redundant. All those multiplications to obtain the index are detrimental for the performance. I don't really know how this is done in BLAS, but there are a lot of tricks to prevent expensive operations.</p> <p>For example, you could rework your code this way</p> <pre><code>template&lt;class ValT&gt; void mmult(const ValT* A, int ADim1, int ADim2, const ValT* B, int BDim1, int BDim2, ValT* C) { if ( ADim2!=BDim1 ) throw std::runtime_error("Error sizes off"); memset((void*)C,0,sizeof(ValT)*ADim1*BDim2); int cc2,cc1,cr1, a1,a2,a3; for ( cc2=0 ; cc2&lt;BDim2 ; ++cc2 ) { a1 = cc2*ADim2; a3 = cc2*BDim1 for ( cc1=0 ; cc1&lt;ADim2 ; ++cc1 ) { a2=cc1*ADim1; ValT b = B[a3+cc1]; for ( cr1=0 ; cr1&lt;ADim1 ; ++cr1 ) { C[a1+cr1] += A[a2+cr1]*b; } } } } </code></pre> <p>Try it, I am sure you will save something.</p> <p>On you #1 question, the reason is that matrix multiplication scales as O(n^3) if you use a trivial algorithm. There are algorithms that <a href="http://en.wikipedia.org/wiki/Coppersmith%E2%80%93Winograd_algorithm" rel="nofollow noreferrer">scale much better</a>. </p>
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    1. POHow does BLAS get such extreme performance?
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    1. USStefano Borini
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    1. POHow does BLAS get such extreme performance?
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    1. COIn terms of the multiplications to find indices, I tried actually taking them out and only doing them the minimum, ie. C, B the mult can be moved below the first for loop. This showed no improvement, I believe my compiler optimized it somehow... I think register optimization would definitely improve my code, however any response to #1?
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    2. COdid you try to crank the optimization flag to -O3 as well ?
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    3. COYa its got all optimizations on. Thanks for the link, although that one is 'never implemented' but the Strassen algorithm is interesting.
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