I think this shift away from JITs is not a fluke but was inevitable, in fact the big question is why it has taken so long (probably industry inertia). The benefits were always less than advertised, the costs higher than anticipated. More importantly though, the inherent performance characteristics of JIT compilers don't match up well with most real world systems, and the shift to mobile has only made that discrepancy worse. Although JITs are not going to go away completely, they are fading into the sunset of a well-deserved retirement.
Advantages of JITs less than promisedI remember reading the copy of the IBM Systems Journal on Java Technology back in 2000, I think. It had a bunch of research articles describing super amazing VM technology with world-beating performance numbers. It also had a single real-world report from IBM's San Francisco project. In the real world, it turned out, performance was a bit more "mixed" as they say. In other words: it was terrible and they had to do an incredible amount of work for the system be even remotely usable.
There was also the experience of the New Typesetting System (NTS), a rewrite of TeX in Java. Performance was atrocious, the team took it with humor and chose a snail as their logo.
One of the reasons for this less than stellar performance was that JITs were invented for highly dynamic languages such as Smalltalk and Self. In fact, the Java Hotspot VM can be traced in a direct line to Self via the Strongtalk system, whose creator Animorphic Systems was purchased by Sun in order to acquire the VM technology.
However, it turns out that one of the biggest benefits of JIT compilers in dynamic languages is figuring out the actual types of variables. This is a problem that is theoretically intractable (equivalent to the halting problem) and practically fiendishly difficult to do at compile time for a dynamic language. It is trivial to do at runtime, all you need to do is record the actual types as they fly by. If you are doing Polymorphic Inline Caching, just look at the contents of the caches after a while. It is also largely trivial to do for a statically typed language at compile time, because the types are right there in the source code!
So gathering information at runtime simply isn't as much of a benefit for languages such as C# and Java as it was for Self and Smalltalk.
Significant CostsThe runtime costs of a JIT are significant. The obvious cost is that the compiler has to be run alongside the program to be executed, so time compiling is not available for executing. Apart from the direct costs, this also means that your compiler is limited in the types of analyses and optimizations it can do. The impact is particularly severe on startup, so short-lived programs like for example the TeX/NTS are severely impacted and can often run slower overall than interpreted byte-code.
In order to mitigate this, you start having to have multiple compilers and heuristics for when to use which compilers. In other words: complexity increases dramatically, and you have only mitigated the problem somewhat, not solved it.
A less obvious cost is an increase in VM pressure, because the code-pages created by the JIT are "dirty", whereas executables paged in from disk are clean. Dirty pages have to be written to disk when memory is required, clean pages can simply be unmapped. On devices without a swap file like most smartphones, dirty vs. clean can mean the difference between a few unmapped pages that can be swapped in later and a process getting killed by the OS.
VM and cache pressure is generally considered a much more severe performance problem than a little extra CPU use, and often even than a lot of extra CPU use. Most CPUs today can multiply numbers in a single cycle, yet a single main memory access has the CPU stalled for a hundred cycles or more.
In fact, it could very well be that keeping non-performance-critical code as compact interpreted byte-code may actually be better than turning it into native code, as long as the code-density is higher.
Machines got fasterOn the low-end of performance, machines have gotten so fast that pure interpreters are often fast enough for many tasks. Python is used for many tasks as is and PyPy isn't really taking the Python world by storm. Why? I am guessing it's because on today's machines, plain old interpreted Python is often fast enough. Same goes for Ruby: it's almost comically slow (in my measurements, serving http via Sinatra was almost 100 times slower than using libµhttp), yet even that is still 400 requests per second, exceeding the needs of the vast majority of web-sites including my own blog, which until recently didn't see 400 visitors per day.
Successful hybridsThe technique used by Squeak: interpreter + C primitives for heavy lifting, for example for multi-media or cryptography has been applied successfully in many different cases. This hybrid approach was described in detail by John Ousterhout in Scripting: Higher-Level Programming for the 21st Century: high level "scripting" languages are used to glue together high performance code written in "systems" languages. Examples include Numpy, but the ones I found most impressive were "computational steering" systems apparently used in supercomputing facilities such as Oak Ridge National Laboratories. Written in Tcl.
What's interesting with these hybrids is that JITs are being squeezed out at both ends: at the "scripting" level they are superfluous, at the "systems" level they are not sufficient. And I don't believe that this idea is only applicable to specialized domains, though there it is most noticeable. In fact, it seems to be an almost direct manifestation of the observations in Knuth's famous(ly misquoted) quip about "Premature Optimization":
Experience has shown (see , ) that most of the running time in non-IO-bound programs is concentrated in about 3 % of the source text.For the 97%, a scripting language is often sufficient, whereas the critical 3% are both critical enough as well as small and isolated enough that hand-tuning is possible and worthwhile.
[..] 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 soft- ware 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.
There is no doubt that the grail of efficiency leads to abuse. Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.
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. It is often a mistake to make a priori judgments about what parts of a program are really critical, since the universal experience of programmers who have been using measurement tools has been that their intuitive guesses fail. After working with such tools for seven years, I've become convinced that all compilers written from now on should be designed to provide all programmers with feedback indicating what parts of their programs are costing the most; indeed, this feedback should be supplied automatically unless it has been specifically turned off.
(Most programs are probably only run once; and I suppose in such cases we needn't be too fussy about even the structure, much less the efficiency, as long as we are happy with the answers.) When efficiencies do matter, however, the good news is that usually only a very small fraction of the code is significantly involved.
I agree with Ousterhout's critics who say that the split into scripting languages and systems languages is arbitrary, Objective-C for example combines that approach into a single language, though one that is very much a hybrid itself. The "Objective" part is very similar to a scripting language, despite the fact that it is compiled ahead of time, in both performance and ease/speed of development, the C part does the heavy lifting of a systems language. Alas, Apple has worked continuously and fairly successfully at destroying both of these aspects and turning the language into a bad caricature of Java. However, although the split is arbitrary, the competing and diverging requirements are real, see Erlang's split into a functional language in the small and an object-oriented language in the large.
Unpredictable performance modelThe biggest problem I have with JITs is that their performance model is extremely unpredictable. First, you don't know when optimizations are going to kick in, or when extra compilation is going to make you slower. Second, predicting which bits of code will actually be optimized well is also hard and a moving target. Combine these two factors, and you get a performance model that is somewhere between unpredictable and intractable, and therefore at best statistical: on average, your code will be faster. Probably.
While there may be domains where this is acceptable, most of the domains where performance matters at all are not of this kind, they tend to be (soft) real time. In real time systems average performance matters not at all, predictably meeting your deadline does. As an example, delivering 80 frames in 1 ms each and 20 frames in 20 ms means for 480ms total time means failure (you missed your 60 fps target 20% of the time) whereas delivering 100 frames in 10 ms each means success (you met your 60 fps target 100% of the time), despite the fact that the first scenario is more than twice as fast on average.
I really learned this in the 90ies, when I was doing pre-press work and delivering highly optimized RIP and Postscript processing software. I was stunned when I heard about daily newspapers switching to pre-rendered, pre-screened bitmap images for their workflows. This is the most inefficient format imaginable for pre-press work, with each page typically taking around 140 MB of storage uncompressed, whereas the Postscript source would typically be between 1/10th and 1/1000th of the size. (And at the time, 140MB was a lot even for disk storage, never mind RAM or network capacity.
The advantage of pre-rendered bitmaps is that your average case is also your worst case. Once you have provisioned your infrastructure to handle this case, you know that your tech stack will be able to deliver your newspaper on time, no matter what the content. With Postscript (and later PDF) workflows, you average case is much better (and your best case ridiculously so), but you simply don't get any bonus points for delivering your newspaper early. You just get problems if it's late, and you are not allowed to average the two.
Eve could survive and be useful even if it were never faster than, say, Excel. The Eve IDE, on the other hand, can't afford to miss a frame paint. That means Imp must be not just fast but predictable - the nemesis of the SufficientlySmartCompiler.I also saw this effect in play with Objective-C and C++ projects: despite the fact that Objective-C's primitive operations are generally more expensive, projects written in Objective-C often had better performance than comparable C++ projects, because the Objective-C's performance model was so much more simple, obvious and predictable.
When Apple was still pushing the Java bridge, Sun engineers did a stint at a WWDC to explain how to optimize Java code for the Hotspot JIT. It was comical. In order to write fast Java code, you effectively had to think of the assembler code that you wanted to get, then write the Java code that you thought might net that particular bit of machine code, taking into account the various limitations of the JIT. At that point, it is a lot easier to just write the damn assembly code. And more vastly more predictable, what you write is what you get.
Modern JITs are capable of much more sophisticated transformations, but what the creators of these advanced optimizers don't realize is that they are making the problem worse rather than better. The more they do, the less predictable the code becomes.
The same, incidentally, applies to SufficentlySmart AOT compilers such as the one for the Swift language, though the problem is not quite as severe as with JITs because you don't have the dynamic component. All these things are well-intentioned but all-in-all counter-productive.
ConclusionAlthough the idea of Just in Time Compilers was very good, their area of applicablity, which was always smaller than imagined and/or claimed, has shrunk ever further due to advances in technology, changing performance requirements and the realization that for most performance critical tasks, predictability is more important than average speed. They are therefore slowly being phased out in favor of simpler, generally faster and more predictable AOT compilers. Although they are unlikely to go away completely, their significance will be drastically diminished.
Alas, the idea that writing high-level code without any concessions to performance (often justified by misinterpreting or simply just misquoting Knuth) and then letting a sufficiently smart compiler fix it lives on. I don't think this approach to performance is viable, more predictability is needed and a language with a hybrid nature and the ability for the programmer to specify behavior-preserving transformations that alter the performance characteristics of code is probably the way to go for high-performance, high-productivity systems. More on that another time.
What do you think? Are JITs on the way out or am I on crack? Should we have a more manual way of influencing performance without completely rewriting code or just trusting the SmartCompiler?