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Comment by rhdunn | Hacker News Reader

Premature optimization is about not making a micro-implementation change (e.g. `++i` vs `i++`) for the sake of percieved performance. You should always measure to identify slow points in expected workloads, profile to identify the actual slow areas, make high-level changes (data structure, algorithm) first, then make more targetted optimizations if needed.

In some cases it makes sense, like writing SIMD/etc. specific assembly for compression/decompression or video/audio codecs, but more often than not the readable version is just as good -- especially when compilers can do the optimizations for you.

A lot of times I've found performance increases have come from not duplicating work -- e.g. not fetching the same data each time within a loop if it is fixed.

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Not really. Knuth was talking about putting effort to make a non-critical portion of the software more optimized. He's saying put effort into the smaller parts where performance is critical and don't worry about the rest. It's not about `++i` vs. `i++` (which is semantically different but otherwise in modern compilers not an optimization anyways but I digress).

The optimizations he was talking about were things like writing in assembly or hand-unrolling loops. It was assumed that you’ve already picked an performant algorithm / architecture and are writing in a performant low level language like C.

Also, your digression about modern compilers is irrelevant to the context of the quote, since Knuth talked about premature optimization at a time when compilers were much simpler than today.

That was my point, though. Don't worry about minor possible changes to the code where the performance doesn't matter. For example, if the ++i/i++ is only ever executed at most 10 times in a loop, is on an integer (where the compiler can elide the semantic difference) and the body of the loop is 100x slower than that.

If you measure the code's performance and see the ++i/i++ is consuming a lot of the CPU time then by all means change it, but 99% of the time don't worry about it. Even better, create a benchmark to test the code performance and choose the best variant.

That's not my interpretation. If you're profiling and benchmarking you're already engaging in (premature) optimization. This process you're describing of finding out whether `i++` is taking a lot of CPU time and then changing it is exactly what Knuth is saying not to worry about for 97% of your code. Knuth is saying it doesn't matter if `i++` is slow if it's in a non-performance critical part of your code. Any large piece of software has many parts where it doesn't matter for any practical purpose how fast they run and certainly one loop in that piece of software doesn't matter. For example, the software I'm working on these days has some fast C code and then a pile of slow Python code. In your analogy all the Python code is known to be much slower than the C code, we don't need a profiler or benchmarks to tell that, but it also doesn't matter because the core performant functionality is in that C code.

Knuth says forget about small efficiencies in 97% of your code. Indeed, the `i++` optimization isn't apt to make more than a small difference, even with the most naive compiler, but other decisions could lead to larger chasms. It seems he is still in favour of optimizing for the big wins across the entire codebase, even if it doesn't really matter in practice.

But it's your life to live. Who cares what someone else thinks?

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