Rambles around computer science
Diverting trains of thought, wasting precious time
Mon, 03 Dec 2012
Tools or not tools
Jim Coplien's keynote at SPLASH this year was a peculiar one. It featured two particularly provocative sentiments: firstly that too much abstraction is a bad thing, and secondly that building tools is not what we want to be doing. (The latter was actually due to Dave Ungar, during questions, but met with vociferous agreement from the speaker.)
These remarks met with some puzzlement from much of the audience, judging by a series of apparently disparaging remarks during subsequent conference talks and question sessions. Who could disapprove of abstraction or tools? I think there are some reasonable answers to that question; what follows is my attempt. (I have no idea whether it coincides with Coplien's or Ungar's.)
Abstraction all the way up
The abstraction issue is perhaps not terribly controversial. Most programmers are aware that abstractions present a trade-off. The temptation to abstract endlessly can be a rat-hole that distracts from actual progress on the task at hand. Ian Lance Taylor once blogged a fairly similar opinion. If you abstract too far, you abstract away essential features of the domain, rendering it unrecognisable. This yields “abstractions” that are actually complex, not simple, to use. Abstracting over multiple use cases, i.e. generality, is a common offender here. For example, rolling your own implementation of a graph algorithm can be easier than figuring out how to tame the monstrous generality of something like the Boost graph library. (Pardon my C++; no doubt you can think of your own examples.)
Sometimes, abstractions exploit specificity, by packaging up common case usage patterns. This can be very useful. In fact, in interesting counterpoint to the Taylor piece above was Rustan Leino's note about loop structures in a subsequent SPLASH keynote: inferring loop invariants is one of the hard problems faced by any verifier. By constraining the form of a loop, it becomes easier to find its invariant. Abstract loops are an extreme case of this, since the loop itself is in library code and not in user code, so the invariant need be found only once. But of course, just as Taylor hinted at, any user forcing themselves only to use such loops will end up spending rather a lot of time structuring their code to accommodate this constraint. (In this way, it shares a lot with other syntax-directed reasoning tools, including type checkers. These tools are superficially easy to market—hey, look, it shows you bugs in your code. But there is a hidden cost to using them, deriving from implicit constraints on how you can structure your code such that it interacts nicely with the tool. If you don't stick to these, your tool fails in some way, like false-positive type errors or solver timeouts.)
To end my rants about abstraction on a complaint, I could also roll out one of my previously-blogged complaints about common styles of functional programming—with liberal use of polymorphism, or unification of higher-order with “ordinary” operations (think currying, or juxtaposition-is-application), code can become needlessly hard to read. Lazy languages add the unification of storage with computation, which I concede is sometimes an incredibly useful abstraction, but easily makes the memory behaviour of your program incredibly difficult to understand.
What about tools?
For me, the most interesting issue concerns tools. Dave Ungar phrased it something like as follows: “if every bolt under my car had a little handle on it, I wouldn't need to get out to go and get a wrench”. So, let me frame the contrast I believe he was making as one of tools versus run-time systems. Dynamic O-O environments are very much systems, geared around the ability to push new capabilities down into the system's fabric, rather than having them sit on top. This “fabric” is what emerges from combining the messaging metaphor (messages are fundamentally proxyable) with dynamic extensibility (adding new messaging behaviour is a local change during runtime, not a far-reaching change at compile time). As I have rambled about previously, the lower some functionality is integrated into a system, the more pervasively available it is, so the more power and leverage it confers. Smalltalkers and other dynamic language advocates know this. It's a very tricky thing to convey to the unfamiliar. It's even harder to measure. Most of us don't use runtimes that have this amount of dynamism and immediacy, although Javascript may yet change that. Operating systems, not least Unix, are also dynamic runtimes in this way, although their inability to see inside application means (unfortunately, and avoidably) that a large amount of useful code and data (hence potential “extension”) is opaque to them.
Tools are fragmentary; runtimes are integrating One reason people develop tools and not runtime extensions is that integration is hard. If you write a command-line tool, you get to define its input domain, output domain and behaviour from a clean slate, according to your convenience. This is often (though not always) easier than plumbing something into a runtime, which is a bunch of code somebody else wrote. But let's imagine making the leap. To get slightly more concrete, suppose the “tool” we're interested in is a dynamic analysis tool—pick your favourite bug-finding, race detection, memory leak detection or other tool that fits the general mould. What's better about having it as a “runtime” rather than just a “tool”? Well, its functionality would be embedded right there in your running program. As a consequence, it supports exploratory, interactive, programmatic use. If you dropped to a REPL in your program, the innards of the tool would be laid out across your program state, pushed into fields on program objects. If your tool is a race detector using a lock-set algorithm, for example, then each object's lock-set would be accessible as a field on that object. If you're using timestamps or vector clocks, they would be there too. You're also not stuck with a fixed amount of insight the tool's authors saw fit to provide (e.g. when trying to track down the source of a data race); the tool's code is a service you're free to extend. Getting interactive, exploratory, programmatic usage seems like a useful payoff for the effort of integrating your tool into a runtime. Arguably, then, the challenge is building runtime infrastructures that are not unduly difficult to extend like this.
Progress? Is the “tools not runtimes” tendency getting stronger? “Systems, languages, applications” is the conference title's invariant. “Tools” is nowhere to be found. My vague impression is that today's programming research is more tool-focused, and less system-focused, than 20--30 years ago. (A near-dual property is also true: self-proclaimed “systems” research has less programming focus than it used to. I used to bemoan this frequently while doing my PhD in the systems research group in Cambridge.) But why? Simplistically, we might just say that integration is hard. I think there is something more subtle at work. Integration of research techniques into runtimes arguably scales poorly—since we all have to integrate into the same runtime, we have to achieve consensus on that runtime's interfaces. Tools, being freestanding and piecemeal, arguably scale better. You could say that lots of small, freestanding tools are the postmodern way, whereas “one true runtime system” is a classical ideal. (It's fitting that Noble and Biddle's “Notes on Postmodern Programming” was recognised at SPLASH this year for its influence, in the Onward! strand.)
Avoiding classical fallacy In the battle of the classical versus the postmodern, normally I side with the postmodern. How can we square this with the desire for the benefits of the runtime approach as I described it? I should probably save my thoughts for another post. But two ideas come to mind. The first is one I've already mentioned: design a runtime infrastructure that is specifically easy to extend. But that seems to be begging the question: if we knew how to build this magical runtime, and what kinds of extension it would need to support, we'd already have done it and solved the problem ages ago. For this reason, we also need the second idea: we need to get into the mindset of tolerating a lot more heterogeneity. Very briefly, it means pushing radically downwards our notion of “runtime” so that most of the typical implementation decisions of an object-oriented runtime, such as dispatch mechanisms, introspection mechanisms and object layout, are actually user-level decisions in such a system, but still recognisable as the abstractions they represent. In other words, our system can descriptively extract extract latent object abstractions from the contexts in which they emerge in existing systems, given descriptions of these latent abstractions. This contrasts with traditional runtimes, in which the object abstraction constructed by the runtime implementor in a way that is prescriptive. And hey presto, we are back to my VMIL 2011 workshop paper: we already have a very powerful descriptive technology, in the form of debugging infrastructure for native code; our task is to bend it to this new purpose. So, end of rant for today.
[/research] permanent link contact
