On Thu, Jun 01, 2017 at 08:39:07AM +0100, Russel Winder via Digitalmars-d-learn wrote: > On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn > wrote: > > > […] > > With D, we can have the cake and eat it too. The understandable / > > naïve implementation can be available as a fallback (and reference > > implementation), with OS-specific optimized implementations guarded > > under version() or static-if blocks so that one could, in theory, > > provide implementations specific to each supported platform that > > would give the best performance. > > But isn't that the compiler's job?
Unfortunately, the compiler can only go so far, because it doesn't understand the larger context of what you're trying to accomplish. Modern optimizing compilers certainly go a lot further than before, but still, at some point some amount of human judgment is needed. Also, compiler implementors do still have to do the "heroics", or rather, teach the compiler to do the "heroics" when compiling straightforward code. So while the general programmer probably will have less need for it, compiler writers still need to know how to do them in order to write the optimizing compilers in the first place. > The lesson of functional programming, logic programming, etc. (when > the acolytes remember the actual lesson) is that declarative > expression of goal is more comprehensible to people than detailed > explanation of how the computer calculates a result. Object-oriented > computing lost the plot somewhere in the early 2000s. There is no argument that straightforward code is more comprehensible to people. The question here is whether it delivers maximum performance. We know from Kolgomorov complexity theory that global optimization, in the general case, is undecidable, so no optimizing compiler is going to be able to generate optimal code in all cases. There will always be cases where you have to manually tweak it yourself. Of course, that doesn't mean you go around micro-optimizing everything -- the usual approach is to write it the straightforward way first, then profile it, identify the hotspots, and find ways to improve performance in the hotspots. Well, at a higher level, the first order of business is really to look at it from an algorithmic POV and decide whether or not a different algorithm ought to be used (and no optimizing compiler can help you there). Then if that's still not enough, then you dig into the details and see if you can squeeze more juice out of your current algorithms -- if the profiler has indicated that they are the bottleneck. > The advance of Scala, Kotlin, Groovy, and now Rust and Go (but only to > some extent), which D has, is to express algorithms as declarative > requirements in a dataflow manner. > > One day hardware people will catch up with the hardware innovations of > the 1970s and 1980s regarding support of dataflow rather than state. Dataflow can only capture a limited subset of algorithms. Of course, in my observation, 90% of code being written today generally belongs in the category of what I call "linear shuffling of data", i.e., iterate over some linear set of objects and perform some transformation X on each object, copying linear memory region X to linear memory region Y, rearranging some linear set of objects, permuting the order of some linear set of things, etc.. This category basically subsumes all of GUI programming and web programming, which in my estimation covers 90% or maybe even 95% of code out there. The bulk of game code also falls under this category -- they are basically a matter of copying X items from A to B, be they pixels to be copied to the video buffer, traversing the list of in-game objects to update their current position, direction, speed, or traversing scanlines of a polygon to map a 3D object to 2D, etc.. Almost all business logic also falls under this category. All of these are easily captured by dataflow models. However, there are algorithms outside of this category, that are not easily captured by the dataflow model. Solving certain graph theory problems, for example, require actual insight into the structure of the problem than mere "move X items from A to B". Route planning, which is an NP-complete problem that, for practical applications, can only be approximated, and therefore actual thought is required for how you actually go about solving the problem beyond "data X moves from A to B". Computing the convex hull of a set of input points, used for solving optimization problems, if expressed and solved in a naïve way, has O(n^3) time complexity, and therefore impractical for non-trivial problem instances. True, your average general programmer may not even know what a convex hull problem is, and probably doesn't even need to care -- at worst, there are already libraries out there that implement the algorithms for you. But the point is, *somebody* out there needs to write these algorithms, and they need to implement these algorithms in an optimal way so that it will continue to be useful for non-trivial problem sizes. You cannot just say, "here is the problem specification, now just let the computer / AI system / whatever figure out for themselves how to obtain the answer". *Somebody* has to actually sit down and specify exactly how to compute the answer, because generic ways of arriving at the answer are exponential or worse and are therefore useless. And even a feasible algorithm may require a lot of "heroics" in order to make medium-sized problems more tractable. You want your weather forecasting model to produce an answer by tomorrow before the 10am news, not 3 months later when the answer is no longer relevant. > > I disagree about the philosophy of "if you need to go faster, use a > > bigger computer". There are some inherently complex problems (such > > as NP-complete, PSPACE-complete, or worse, outright exponential > > class problems) where the difference between a "heroic > > implementation" of a computational primitive and a naïve one may > > mean the difference between obtaining a result in this lifetime vs. > > practically never. Or, more realistically speaking, the difference > > between being able to solve moderately-complex problem instances vs. > > being able to solve only trivial toy instances. When you're dealing > > with exponential complexity, every small bit counts, and you can > > never get a big enough computer. > > There are always places for experimentation, and innovation. Hard > problems will always be hard, we just need to find the least hard way > of expressing the solutions. Some problems are inherently hard, and no amount of searching can reduce its complexity past a certain limit. These require extreme measures to be even remotely tractable. > The crucial thing is that people always work to remove the heroicism > of the initial solutions, creating new computational models, new > programming languages, etc. to do it. [...] But *somebody* has to implement those computational models and programming languages. If nobody knows how to write "heroic" code, then nobody would know how to write an optimizing compiler that produces such code either, and these computational models and programming languages wouldn't exist in the first place. I know that the average general programmer doesn't (and shouldn't) care. But *somebody* has to, in order to implement the system in the first place. *Somebody* had to implement the "heroic" version of memchr so that others can use it as a primitive. Without that, everyone would have to roll their own, and it's almost a certainty that the results will be underwhelming. T -- Question authority. Don't ask why, just do it.
