Q: In retrospect, wasn't the decision to trade off programmer efficiency, security, and software reliability in exchange for runtime performance a fundamental mistake?
A: Well, I don’t think I made such a tradeoff. I want elegant and efficient code. Sometimes I get it. The efficiency vs. correctness, efficiency vs. programmer time, efficiency vs. high level, etc. dichotomies are largely bogus.
Unlimited-precision symbolic computation is more elegant than floating point numbers. You simply never have any numerical stability problems. Anything algebraically correct – a valid way to solve for x given the equations involving x – is also computationally correct. You don't need to know all the quirks of floating point, and you won't need "numerical recipes" which are basically ways to deal with these quirks.
Symbolic computation is rather widely available – say, in Mathematica, Matlab/maple, etc. – but it's not used nearly as much as floating point. That's because floating point is much more efficient, and a whole lot of things can not be done in a reasonable amount of time and space with symbolic computation.
It is undeniable that this efficiency comes at a cost of correctness (in terms of increased likelihood of bugs), programmer time, and elegance. There are plenty of algebraically elegant solutions which just don't work in floating point. If you don't notice, you have a bug; if you do notice, you spend your (programmer) time looking for an alternative, and said alternative may be less elegant.
Floating point is not the lowest level we can sink to. In many cases the quest for still more efficiency brings us to the dark quagmires of fixed point arithmetic. That's when you have an integer and you implicitly assume it's divided by 2^N – the exponent is statically known so the point isn't floating at run time, so to say. So to implement a+b you just use integer addition, and a*b is an integer multiplication followed by a right shift by N. Or there are midway scenarios where you have many integers with a common, dynamically computed exponent because they all have roughly the same range (FFT is one case where this is often done.)
Fixed point is so ugly that there's not even a recipes book that I'm aware of; it just doesn't come out tasty in the slightest. The biggest trouble isn't even the very likely overflow but the loss of precision: floating point guarantees a certain number of significant bits in the mantissa, while fixed point doesn't – unless you explicitly normalize the number at some point using CLZ or similar (making it more of a floating point emulation than "true" fixed point where exponents aren't represented at runtime). You think you have a 32-bit mantissa and an implicit exponent so the number is rather precise – but those 32 bits can have most of the high bits set to zero and then it's not precise at all.
However, a mixture of 8-bit, 16-bit and 32-bit fixed point operations often beats the performance of floating point by a large margin; especially if you have SIMD instructions, because you can always fit 4x more 8-bit numbers into a register than 32-bit numbers, and a single-precision floating point multiplier is ~10x more costly in hardware than an 8-bit integer multiplier so you have less of them.
Again, this efficiency comes at a cost of correctness (in terms of increased likelihood of bugs), programmer time, and elegance.
In computer vision, you're often looking for objects of a certain class, and you have a classifier taking a rectangular image region and telling whether this region contains an object of that class. A simple and elegant object detection algorithm is to apply this classifier to every possible rectangle in the image, and then remove rectangles which mostly overlap (as in, if there are 15 similar rectangles saying there's a face in roughly the same place, make one rectangle out of them all).
This elegant algorithm is never used, because there are too many possible rectangles (every coordinate times every size). A common optimization is to use a cascade of classifiers. That is, apply a very cheap classifier with a lot of false positives but hopefully almost no false negatives to every region. The purpose is to throw away most of the rectangles so that the remaining smaller set still contains all the true positives – and a lot of false positives, of course, but much less.
This is repeated with many (possibly increasingly expensive) classifiers processing increasingly smaller sets of rectangles. The most widely deployed classifier cascade is probably the Viola-Jones face detector, currently available in most digital cameras displaying little squares around faces. As you could have noticed, it often misses a face, which is to be expected with all the little classifiers hurrying to throw rectangles away. And which is OK for a consumer application where a success rate of 90-95% is perfectly fine and an extra 1% of detection rate is not worth a $0.01 increase in price. The point is that the error rate is undeniably increased by stricter efficiency requirements.
The upshot is that object detection provides a broad family of examples where, again, runtime efficiency comes at a cost of correctness (in terms of increased likelihood of bugs – there's much more code to write – as well as the ultimate detection rate), programmer time, and elegance.
(Even the smallish sub-problem of merging overlapping rectangles provides an example where efficiency has to be bought with all those other things including elegance. A short, readable, elegant solution could use an O(N^2) nested loop where each rectangle is intersected with every other rectangle. One optimization is some sort of spatial data structure where you don't look at rectangles if they don't fall into the same bin of the spatial subdivision because then they can't intersect. That's faster, more buggy and less readable.)
Does this have anything to do with the quote by Stroustrup though? His implied point was how the std::sort template is more elegant as well as more efficient than C's qsort function fiddling with void pointers, right? Or ostream vs printf? Whereas these are all examples of "algorithmic efficiency" – is that even related to language design?
Well, the thing is, "algorithms" and "languages/code" is a continuum:
Think of all the psychic energy expended in seeking a fundamental distinction between "algorithm" and "program". — Alan Perlis
Given that it's a continuum, it is doubtful that a statement which is profoundly wrong in an "algorithmic" context could be true in a "programming" context. If the tradeoff between runtime efficiency and programmer efficiency/"elegance" is fundamental from an algorithmic point of view, then it's likely fundamental in computing in general.
For a concrete example of how blurry the line between "algorithmic efficiency" and "code efficiency" is, let's discuss corner detection. The FAST corner detector is a decision tree looking at pixels surrounding the central pixel and comparing the image intensity of the center to its surroundings. Similarly to other classifier cascades, "not a corner" is a quick decision, while "yes, a corner" is decided after all the checks are done.
The decision tree is implemented in several thousands of auto-generated C code lines with gotos. (That's one addition to the recent discussion about the utility of gotos in systems programming; add computer vision to the list of goto applications, I guess.)
Is it possible to implement the decision tree in a more elegant and readable way? Of course – but at the cost of efficiency; not asymptotic efficiency since it'd be the same decision tree, but efficiency nonetheless.
Is this goto business an "algorithmic" optimization or a "program" optimization? Consider that FAST's entire raison d'etre is being faster than, say, the Harris corner detector. Constants matter for high-resolution images processed in real time.
Consider furthermore that both FAST and Harris are O(#pixels) since they look at a finite, small number of pixels around each coordinate and execute a finite, small number of operations. Consider that which is more efficient depends on the platform – SIMD helps speed up Harris but not FAST, and different SIMD instruction sets speed it up by very different factors. (This is also true for linear classifiers vs Viola Jones and for other cases.) And consider the fact that algorithmically, they're wildly different – Harris looks at eigenvectors whereas FAST is an intensity-based decision tree, they have tunable decision thresholds with different meaning, and different sets of false positives and false negatives.
So is FAST a work in the area of "algorithms" or "programming", and is the auto-generated mountain of code essential to make it efficient an "algorithm" or a "program"? My answer is that it's both, in the sense that you can't really draw the line.
But what about std::sort and C++'s combination of efficiency and elegance? Well, C++ rather obviously does pay with programmer efficiency for runtime efficiency, without an option to opt out of the deal. Every allocation can leak, every reference can be dangling, every buffer can overflow, etc. etc. etc.
This blindingly obvious fact doesn't surprise those who realize the fundamental tradeoff between efficiency and a whole lot of other things, some of which can be collectively called "elegance". Whereas those refusing to believe in such a tradeoff manage to not even notice the consequences. For example:
The relatively small size of the C++ standard library – primarily reflecting the lack of resources in the ISO C++ standards committee – compared with the huge corporate libraries can be a real disadvantage for C++ developers compared to users of proprietary languages.
…So why do languages without corporate backing which are 2 to 3 times younger than C++, such as Perl, Python, and Ruby, have so much more libraries, both standard and non-standard, but widely used?
The best uses of C++ involve deliberate design. You design classes to represent the notions of your application, you organize those classes into hierarchies, you express your algorithms precisely and abstractly (no, that “and” is not a mistake), you use libraries, you build libraries, you devise error handling and resource management strategies and express them in code. The result can be beautiful, efficient, maintainable, etc. However, it’s not just sitting down and writing a series of statements directly manipulating characters, integers, and floating point numbers.
The thing is that actually doing something useful involves a whole lot of "direct manipulations" of characters, integers and floating point numbers – and strings, arrays, hash tables, files, sockets, windows, matrices, etc. Languages which let you "just sit down and write the series of statements" give programmers the extra productivity which results in all those extra libraries getting written.
However, equally undeniably it does cost you runtime efficiency, because you pay an overhead for built-in resource management strategies such as garbage collection, built-in error detection strategies such bounds checking, and a whole lot of other things.
It's not surprising that Stroustrup sees the problem in the fact that corporations "with the resources" invest them in what he thinks is the wrong thing, presumably because of their self-interested profit motives. Alex Stepanov who designed the STL expressed similar statements, and so did Alan Kay and every other perfectionist technologist. If you seek perfection to the point of denying the existence of most obvious tradeoffs – and tradeoffs are a pesky thing for a perfectionist because they imply that perfection is unattainable – then you're also likely to somewhat resent corporations, markets, etc. For a discussion of that, see my take on Worse Is Better vs The Right Thing.
(Of course there are plenty of perfectionists who, instead of rationalizing C++'s productivity problems, spend their time denying that Python is slow, or keep waiting for Python to become fast. It will not become fast. Also, all its combinations with C/C++ designed to remedy this inefficiency will forever be ugly. We had psyco, PyPy, pyrex, Cython, Unladen Swallow, CPython extension modules, Boost.Python, and who knows what else. Python is not designed to be efficient; it's designed for productivity and for extensibility through a necessarily ugly C FFI. The tradeoff is fundamental. Python is slow forever. Python bindings are ugly forever.)
So if the tradeoff is fundamental, should we give up on efficient resource utilization? No – if the elegant thing is to load the database table into RAM, it doesn't mean that we have enough RAM. Should we give up on programmer productivity? No – inline assembly or lock-free code which isn't obviously bug-free doesn't belong in our cold paths.
We should, however, give up on perfection. Some code will be slower than we want because we don't have time to optimize it, and some code will be uglier than we want because we have no choice but to optimize it.
A hope to defeat a fundamental tradeoff is nothing but a source of frustration, and it's a bliss to have lost such a hope.