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Mature Optimization

Optimizing Objective-C programs is, in the end, not necessarily hard. In fact, this very amenability to optimization in general and late-in-the-game optimization in particular is a large part of what makes this language popular with expert programmers: you really can leave the “small efficiencies,” a few of which we’ve shown, for later.

Although Knuth’s quote above is well-known, what is less well-known is that it is just an introduction to extolling the importance and virtues of optimization. It continues as follows:

  • Yet we should not pass up our opportunities in that critical 3%. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified.

And the section before the one in question couldn’t be more different:

  • The conventional wisdom shared by many of today’s software engineers calls for ignoring efficiency in the small; but I believe this is simply an overreaction to the abuses they see being practiced by penny-wise-and-pound-foolish programmers, who can’t debug or maintain their “optimized” programs. In established engineering disciplines a 12% improvement, easily obtained, is never considered marginal; and I believe the same viewpoint should prevail in software engineering. Of course I wouldn’t bother making such optimizations on a one-shot job, but when it’s a question of preparing quality programs, I don’t want to restrict myself to tools that deny me such efficiencies.

    • “Structured Programming with Go To Statements,” Knuth, 1974.

The quote is embedded in the paper “Structured Programming with Go To Statements” from 1974, which is largely about achieving better performance via the use of go to statements. It is in fact, in large part, an advocacy piece for program optimization, not against it, containing such gems as the idea that engineers in other disciplines would be excluded from practicing their profession if they gave up performance as readily as programmers.

What makes Objective-C so powerful is that once you have the information as to what needs optimization, you can really pounce, smash-bits, and exploit all the hardware has to give. Both until that point and for the parts that don’t need it, you can enjoy the remarkable productivity of a highly dynamic object-oriented language.

Swift takes a different approach: make everything much more static up-front and then let the compiler figure it out. While superficially sound, this approach inverts Knuth’s dictum by making microperformance a deciding factor in not just application modeling, but language design. In addition to the approach being questionable in principle, it currently just doesn’t work: Swift is not just slower than optimized Objective-C, it is often significantly slower than non-optimized Objective-C, without any further recourse than waiting for the compiler to get better or rewriting your code in C. So that questionable premature optimization doesn’t even pay off.

That said, a little bit of structural forethought and planning is extremely helpful in order to enjoy the benefits of late optimization: You should have an idea of the order of magnitude of data you will be dealing with (one, a thousand, a million?), what operations you need to support, and whether the machine you are targeting can handle this amount of data, at least in principle.

As you are designing the system, keep in mind the asymmetric 1:5:50:200 relationship for primitive operations : messaging : key-value access : object creation that we have illuminated throughout this chapter. With that in mind, see if your most numerous pieces of data can be mapped to primitives, and try to keep your interfaces as message-centric as possible. The messaging system has a nice sweet spot in the relationship between cost and expressiveness.

The arguments of those messages should be as simple (primitive types preferred) and expressive as possible. Large-volume data should be contained in bulk objects and hidden behind bulk interfaces. Key-value stores, if needed, should be hidden behind messaging interfaces and temporary objects should be avoided, especially as a requirement for an interface. If temporary objects can’t be avoided, try to keep your APIs defined in such a way that you will be able to “cheat” with object caches or other techniques for reusing those objects when the time comes.

Fortunately, these measures tend to simplify code, rather than make it more complicated. Simpler, smaller, well-factored code is not only often faster than complicated code, because code that isn’t there doesn’t take any time to run, it also makes a much better basis for future optimization efforts because modifying a few spots will have a much greater impact.

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