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I am working in HPC, and I beg to differ. Yes, compilers should do optimisation whereever possible, but that is only a small percentage of the story. If the p

I am working in HPC, and I beg to differ. Yes, compilers should do optimisation whereever possible, but that is only a small percentage of the story. If the programmer doesnt know which algorithms to choose, the compiler is unable to fix that. If the programmer does not understand the underlying architecture at least a bit, the compiler is also likely going to fail fixing that. Take one of the most basic programming examples, fib. Depending on how your programming language might be implemented, the recursive definition is likely slower then necessary. So at the end of the lesson, you're being taught to make a loop out of the recursion, so save stack space and such. But usually, the lesson ends here. But this is sooooo far away from the best solution. Think 2x2 matrix and the power of n. No compiler will ever transform your naiv recursive fib to that. So no, the compiler is not the ultimate answer. If you dont manage to educate more programmers, we will continue to run slow software.

wiz21c 5y ago
yeah, but the more you optimize, the less your code is a way to communicate an idea on how/why (business wise) you did some things. I used to optimize some code. When I try to squeeze every CPU cycle, then I usually have to turn my algorithm upside down so that they "fit" correctly to the CPU architecture (cache, branch prediction, avoiding costly, instructions). In the end, algorithm cleverness, which is important to understand to read my code is totally muddied by optimization techniques here and there. Having the right balance between performance and readability is tough... Another example I cam across a while ago is matrix multiplication. See https://en.wikipedia.org/wiki/Computational_complexity_of_matrix_multiplication https://en.wikipedia.org/wiki/Computational_complexity_of_ma... The naive algorithm is super simple to understand and corresponds to the actual math definition. But the optimized algorithm, that's something totally different :-)

wiz21c 5y ago

yeah, but the more you optimize, the less your code is a way to communicate an idea on how/why (business wise) you did some things. I used to optimize some code. When I try to squeeze every CPU cycle, then I usually have to turn my algorithm upside down so that they "fit" correctly to the CPU architecture (cache, branch prediction, avoiding costly, instructions). In the end, algorithm cleverness, which is important to understand to read my code is totally muddied by optimization techniques here and there. Having the right balance between performance and readability is tough... Another example I cam across a while ago is matrix multiplication. See https://en.wikipedia.org/wiki/Computational_complexity_of_matrix_multiplication https://en.wikipedia.org/wiki/Computational_complexity_of_ma... The naive algorithm is super simple to understand and corresponds to the actual math definition. But the optimized algorithm, that's something totally different :-)