A golden rule of performance optimization

Of course, one shouldn't optimize what doesn't need optimization, so one should use profiler to see what's slow. At least YourKit allows for two kinds of performance profiling: sampling and tracing. The first one reports which code takes most time, and the second one also includes invocation counts for each method. And the rule is:

One should look at both sampling profiles and the ones with invocation counts.

Sampling profiles show you where a program is slow. Tracing profiles show why it's slow. It's quite often that I see some innocent things in the snapshots, like getting a value from a hash table. This doesn't mean that I should optimize hash table, it means that it's called zillions of times. So I should step back several stack frames and look carefully at the algorithm, trying to decrease its time complexity. Invocation count information helps to find the exact abstraction level I should pay attention to.

Sadly one can't use only tracing profiles, even if they also report the time each method takes. It's like quantum mechanics: the tracing overhead affects the time reported too significantly, and one can't trust it anymore.

In some cases it's obvious where the algorithm is inefficient, and sampling snapshots are enough. But increasingly often I find it to be not the case.

Yes, it's all well-known. And it's been about 15 years ago that I first read (and agreed) that algorithm optimization gives much better results than low-level one. But to learn something and to realize it, understand it deeply, are completely different things.

Data flow analysis

I do many things in IntelliJ IDEA. One of the most interesting of them is data flow inspection, also known to the world under a somewhat strange name Constant Conditions & Exceptions. It analyzes your Java code, checks how you assign variables and what you write in if conditions about them, and issues warnings like Condition is always true or This method call can produce NullPointerException. The analysis it performs is quite advanced. Sometimes, it requires some thinking to understand that a warning is actually correct. Users even complain it's too intelligent (e.g. 1, 2, 3)!

I didn't maintain this inspection code from the very beginning, only in the last several years. During this time, I did several improvements, not counting numerous bug fixes. I like this inspection. I like even its bugs, because it's fun fixing them. So if you see a bug, please report it, I'll be grateful for this new fun!

Closures

I've supported anonymous classes, whose analysis should be aware of what happens outside the class. For example, o cannot be null inside Runnable and no warnings should be reported:

o = getSomeObject();
if (o != null) {
    new Runnable() {
      public void run() {
        o.doSomething();
      }
    };
}

Repeated invocations

The inspection used to warn on the following code:

@Nullable File getFile() {...}
...
if (getFile() != null) return getFile().getName();

It's perfectly correct, but quite inconvenient. Correct because getFile is marked as @Nullable, it's a method and its result may change between invocations. Even if it was a mutable field named file, it could be reassigned from another thread. But it's an inconvenient warning because most of the code out there isn't subject to these conditions. And I don't feel like making users change their perfectly valid code by extracting a variable out of getFile() just to make a tool happy.

So now IDEA produces no possible NPE warning on such calls. What's complicated about this fix is that it also doesn't presuppose getFile() being not-null. So if you write code targeted to mutable environment, and check the null-ness of the file again in case it's changed, this second if (getFile() != null) check is not reported as an always true condition. What happens inside is that after any method call (getFile in this case) IDEA marks this expression as having an unknown value, and doesn't track any possible constant conditions involving it.

Tracking other objects

I've also supported data flow on final fields of other instances, not only this. And on non-final fields. And on getter method calls on other objects. And on expression chains comprised of all of the above. The main catch is to wisely forget the facts you know about all those expressions when it's time (e.g. on qualifier reassignment).

Contracts

More recently, I've added declarative method contract support to the inspection. It was quite easy. What was hard, was not to generate any excessive warnings because of a contract. If you call a method transform(o) where transform has a contract of null->null, then IDEA analyzes the method call as o == null ? null : transform(o). And whenever IDEA sees that you compare a variable to null, it unsurprisingly starts suspecting that it might actually be null! So here's the discrepancy: with such a contract, IDEA actually sees the comparison with null, but the user doesn't. It's just a normal method call, but its argument mysteriously is considered nullable after the call. As I've said, it was hard to do and took me more than a week of thinking, but I did it finally!

It remains to add checking that the contract corresponds to what the method is actually doing, and to infer contracts automatically. But it's quite non-trivial, and I'm still trying to understand how it could be implemented.

Value is always constant

if (var==null) { ... someMethod(var) ... }

Quite probably, passing to a method a variable which is known to be null, is a bug and the user intended something else. Even if it's not, writing null instead of var may make the code clearer. So the inspection reports such uses of variables with values known to be null, true or false and suggests to replace them with constants.

Too complex?

Finally, the thing I did yesterday and I'm really proud of. There's a problem: IDEA marks some methods as being too complex to analyze. This warning irritates people and not everyone understands it means you don't get any NPE warnings inside that method. The warning appears in two cases:

• when it just takes too long to analyze the method
• when the number of possible variable states is too large

Having a profiler, the first cause is relatively easy to fix (and is actually quite rare). The second one is harder.

So, there's too many data flow states. Each state is basically a set of assertions about variables and their values. IDEA tracks these states for each point in your code, and each point may correspond to many states. For example, a nullable variable is represented by two states: in one of them it's null, in the other it's not-null. If you call a method on this variable, both states are considered, and if in any of them the variable is null (which is true) the NPE warning is issued.

For simplicity let's assume that all assertions are either in form variable==value or variable!=value.

So, how to get many-many different states? It's very easy. You may have, say, 10 variables. Just compare each of them with some constant value, and here you are:

//1 state here
if (var1 == 0) { print("var0"); }
//2 states here: var1==0 and var1!=0
if (var2 == 0) { ... }
//4 states here:
//var1==0 && var2==0, var1==0 && var2!=0
//var1!=0 && var2==0, var1!=0 && var2!=0
...
if (var10 == 0) {...}
//1024 states here, too complex :(

But it need not be so sad! It's not too complex for people to understand, why should it be so for the IDE? The 2 states after the first if statement actually are complementary, they only differ in their assertions about var1. In fact, they can safely be replaced with just one state with no assertions about var1.

And that's the basic idea. At each point where different control flow branches join, all the states are gathered and checked if they can be merged. This has reduced the number of states dramatically, at least for the samples of "too complex" methods in our code base that I've looked at. Hence, many methods are not too complex anymore, users are not annoyed and get useful highlighting instead. Profit!

One issue is that determining which states can be merged is quite computationally expensive at the moment, and it doesn't merge all the states it could. But I'm working on it.

Java JIT and code cache issues

If you have a lot of Java code, you may run out of code cache used by JIT to compile frequently used code. When this happens, you can get an OutOfMemoryError or (if you're lucky) just a message in the console that JIT is stopped and the rest of the newly loaded code will be interpreted, so, slow.

You can increase the code cache size by specifying -XX:ReservedCodeCacheSize=xxx, but you'll run into the same problem after you add more code.

You can enable -XX:+UseCodeCacheFlushing to deal with that. It's supposed to make some cleanup when the code cache gets full, to free space for new method compilation.

Today I learned that it's not as good as it sounds, by reading some 25M of JIT logs on IntelliJ IDEA process (JDK 1.7 64-bit). The code cache got full, it was flushed, which really freed up some decent amount of code cache space. That's OK. There was a problem though. After this event all the JIT compilation was stopped. So the compiled code that happened to be flushed away was interpreted now, no matter how hot-spottish it really is.

This is quite noticeable. For example, code completion and goto-class navigation suddenly become very slow after a day of work. They match lots of names against your typed prefix, and unfortunately some of this matching code gets deoptimized and becomes visible in the CPU snapshots.

What to do? Wait for Oracle to fix this. And for now we increased code cache size for 64-bits JVMs.

Maybe we should also take a closer look at the logs and try to reduce the hotspot count. Just look at the methods that are compiled at runtime and try calling them less frequently to prevent them occupying code cache at all. This could also improve the overall performance.

Parser rewrite completed

In June I started rewriting the parser because there were too many hacks in there. I've just finished: all the tests now pass, that were passing before that decision. Hurray!

It took about 4 months. I hoped it would be faster. Perhaps it could be, if I wasn't procrastinating so much. Now there's a lot less hacks as there's a much stronger support for syntactic hierarchy. Hopefully, adding each new test sentences won't take so long now. Although fixing the last several tests really took several weeks which isn't very promising :)

The plan now is to try to speedup things a little by not trying all the semantic alternatives after each and every word. And then to add new sentences. I have quite a few of them enqueued for parsing and translation.

How I do natural language parsing without combinatorial explosion (almost)

TL;DR. One big parser is made of many little composable parsers, one for each language construct. That's great but has some issues with syntactic hierarchy.

Overview

How does one parse a highly ambiguous natural language text? Simply. One just runs several parsers in parallel and chooses the most semantically plausible analysis.

Many words have several alternatives, and avoiding exponential analysis forking is important. The parsers that run in parallel are not for the whole text, they're highly specialized. Each of them only parses a specific construction. When an adverb looks for a head verb, it doesn't care that the noun it encounters instead can be either nominative or accusative. It even doesn't care of the verb's gender, number, finiteness, whatever. Those things are managed by other constructions.

Forking still happens, but in manageable quantities. Example: Russian, Mother loves daughter. Both mother and daughter can be nominative or accusative, so this sentence is globally ambiguous (although it has a preferred Subject-Verb-Object reading). So there are two constructions, nom and acc which both have two noun-verb pairings: mother loves and loves daughter. Needless to say, these variants are mutually exclusive.

Basic parsing algorithm

Parsing works as follows. Each word specifies the constructions in which it can participate, and which role it can play in those constructions. Formally, it contributes a number of mites — pieces of constructions with some attributes attached. Mites can be marked as contradictory, e.g. mother can participate as a noun in nom or acc constructions, but not in both.

Parsing state also consists of mites. When applying a contribution, the new mites are added to the state. Besides, the mites that were already there, now have an opportunity to analyze the whole contribution and generate yet more mites to add. The newly added mites form the new parsing state which is ready for the new word to come.

The generation of new mites based on the contribution is called enrichment. Most often it's just a unification. Example:

• you have mother which defines nom and acc mites with noun attribute (contradicting each other)
• you meet loves which defines nom and acc mites with head attribute (not contradicting)
• then you can just unify the mites with the same construction and get nom and acc mites with both noun and head defined (also contradicting).

Sometimes word order is important, so you only unify mites that come in a specific order. For example, prepositions can only come before their dependent noun, so some conditional logic is needed for enrichment in this case. When parsing sequences (A and B), yet more complex enrichments are used. They merge whole series of mites that both A and B contribute.

Semantics

Each construction has a meaning function associated with it. That means, each mite can contribute semantic relations. Example: nom construction links its head and noun with arg1 predicate. Of course, this can only happen when both head and noun are defined, i.e. both the verb and the noun have occurred.

The current meaning of the whole parsing state is produced by choosing a subset of compatible mites and combining their meanings. This choosing is a complex process with several (contradictory) guidelines:

• maintain status quo: once a mite is chosen, keep it
• if a chosen mite gets unified, try to choose the unification result
• for every word contribution, the order is important: first mites listed have higher priority (that's how we get SVO reading in mother loves daughter — nom is just preferred over acc)
• prefer complete mites over incomplete; nom with both head and noun defined is definitely better than a mite with only one attribute.

It can still happen that a chosen subset makes no sense semantically, while another one would be more plausible. To account for this, on each iteration the parser also checks some alternatives and chooses the more plausible ones. That's a bit inefficient place and I'm thinking of ways to make it more smart.

Hierarchy

I've tried to live with all this above but without any syntactic hierarchy, and that proved to be very inconvenient. Now I have some kind of hierarchy.

Every parsing state has a frontier. That's mites that appeared most recently: either generated directly by the previous word or from enrichment process based on this generation. These mites will always have a possibility of enriching new word contributions.

Some of the frontier mites have a special structural capability. They can expose an earlier parsing state so that mites from its frontier will also be called for enrichment. 

Example: after mother loves daughter there is a frontier having the mites related to daughter. That's fine if the next word is that starting a relative clause describing the daughter. But if the next word is strongly, then it should be linked to the verb loves which is not in the frontier. That's why one of the frontier constructions, namely the unified acc having both head and noun, will expose the earlier parsing state with loves in the frontier. Correct enrichment and unification of adverb construction will then be possible.

The topmost element in the hierarchy (usually verb) exposes an empty parsing state. So do mites that want to disallow free unification and control everything: commas, conjuncts, prepositions.

Dealing with visibility ambiguity

There may be several mites in the frontier exposing different earlier parsing states. That results in a visibility ambiguity. Not that I have a great recipe for this, but I have a way to deal with such circumstances.

If different mites expose different states, those mites should be mutually exclusive. So once you have chosen some non-contradicting mites for semantic evaluation, the previous state is defined unambigously.

Given the "status quo" guideline in the choosing algorithm, this results in structurally serial parsing. Once you've chosen one structural analysis of many, you're likely to stay on this parsing route. Luckily, so far I haven't encountered too many structural fork places. Just the comma.

But still, there are moments when you've chosen a wrong route and it needs to be corrected. This implies the parser should be able to do two things:

1. One should detect that the current route is wrong. It's not simple, because a seemingly wrong analysis may be easily become correct once the next word arrives. Right now, I have a dumb ad-hoc code that analyses all the mites, unified or not. It detects things like "we're parsing a comma-separated sequence, but we haven't yet met a second member, but here's a participle that's usually surrounded by commas. Maybe we should have chosen the participle-parsing route".
2. After we've realized our route is wrong, we should switch to a correct one as quickly as possible. Right now I use the way that was the easiest to implement, but absolutely psycholinguiscally implausible. I just go back in parsing state, try to choose mites with different structural properties and reparse everything after it. So far it works but I'm desperately thinking of something more efficient.

That's how my parser currently works, generally.

Groovy highlighting, continued

I was wrong. I thought that ignoring method argument types was enough to avoid complex data flow with cyclic dependencies. I absolutely forgot about method call qualifier. It can be complex and contain many local variable references which may easily lead to cyclic dependencies. Too many method calls have qualifiers, and ignoring all of them would render the argument-based type inference feature.

I tried to decrease the performance slowdown by caching the things that are invoked during resolve but don't depend on other local variables and thus don't lead to cyclic dependencies and are actually cached. For example, the list of all non-local declarations visible from a particular reference in code. That gave some speedup, but not enough for me.

So I remembered that once there was a completely different type inference algorithm. It was control-flow-based, it was smart, it inferred the local variable types for the whole method at once. It walked the control flow graph and incrementally refined the variable types. I had abandoned it because it had three issues:

1. Each iteration used the previously computed types which were stored in a global thread-local variable. Not beautiful.
2. The results based on these partially-computed types were cached elsewhere and used by other highlighting threads which led to spurious errors in the editor.
3. Sometimes whole-method inference is not needed and slow, e.g. when you're searching for usages and want to resolve just one reference as quickly as possible.

Now I had to give a second thought about abandoning that algorithm. OK, it had all those problems, but at least it was fast. So why not try to solve those problems another way?

1. Non-beautiful global thread-local state. That's easy, you just have to pass this state around to all the methods that deal with type inference and resolve. Easy, yes, but quite tiresome. After about two hours of non-intellectual parameter propagation, when my changelist exceeded 50 files, I decided I can live with this global thread-local state and reverted everything.
2. The problem of caching. It's actually easy as well. There's not many places whose caching leads to multi-threading problems, so it's easy to introduce yet another indirection level there and cache those results in the same global thread-local state during type inference.
3. Finally, the "find usages will be slow again" problem. One can analyze the variable dependency graph on each other and infer the types for a strongly-connected subcomponent of that graph. So far I haven't done this though.

But I have done everything else! I've restored the algorithm and adapted it to the new feature set, which was tricky. Now Groovy type inference in IntelliJ IDEA doesn't introduce any cyclic dependencies caused by local variables and is not exponential. Seems fast enough on my cases, but of course, there's always something to improve in that area. In fact, for some code it's a bit slower now, I have to investigate that.

Recursion, caching and Groovy highlighting

It's hard to write anything (e.g. my parser) when it takes 20 seconds for IntelliJ IDEA to highlight a not very large Groovy file. And after you rename anything, you wait the same amount of time while it's optimizing imports. Today I finally decided to investigate the issue. Most of the highlighting time is spent in reference resolution: an IDE must know what every identifier refers to.

Recursion

As it happens, nowadays most performance problems in reference resolution are caused by cyclic dependencies. That means, resolving one reference requires resolving another one, and that one in turn requires the first one. As we use data flow to determine the variable types in Groovy, this is easy to encounter when you have a loop:

def a = new A()
def b = new B()
while (...) {
  a = b.nextA()
  ...
  b = a.nextB()
}

In a = b.nextA() one should first determine the type of b, then find a nextA method on that type and finally use its return type for a. But we should take into account that this might be not a first iteration of the loop, therefore b might be assigned in the very bottom of the loop body, so its type should be taken from there. And there it depends on the type of a defined in the same assignment that we are looking at.

That's just impossible to figure out. So if we discover such a cyclic dependency, we give up on inferring this particular expression type and use other assignments available. In this case a would just have the type A even if b.nextB() actually returns some AImpl.

Technically, it's just a stack overflow prevention. We maintain the list of expressions that we are currently calculating the type of. If we're asked for a type of such an expression, we just return null. The caller is ready for that.

Caching

Things get more complicated because we also cache the type of each expression. The problem is, we shouldn't cache null returned by stack overflow prevention functionality. Moreover, we shouldn't cache anything that depends on that null. Because in the end the type of a will not be null, it'll be A. And anything that depends on it will have a normal type based on A. If we cached an incomplete type, another highlighting thread would come and use it, and highlight a wrong error.

That's why endless recursion prevention and caching should know about each other. In IDEA, we have RecursionManager class which does precisely that.

As a result, if we have lots of cyclic dependencies, we don't cache lots of things. In fact, RecursionManager tries to memoize some calculation results that don't directly lie on a cyclic dependency. And this memoization is what makes this class really complex. It speeds up things quite a bit, but still, the best solution is not to create cyclic dependencies unless one really needs to.

Back to Groovy

So I went commenting out various parts of my Groovy code and putting a breakpoint on return null in RecursionManager. And here's what I found.

There were a couple of plain bugs which also led to cyclic dependencies: incorrect control flow for for-in loops, and too wide search scope when resolving a super class name.

Some Groovy AST transformations (e.g. @TupleConstructor) add constructors with parameters based on the class properties. Property list retrieval requires traversing all class methods and fields. Constructors are also methods in IDEA's terminology, so the transformation handler depends on itself. Fixed in the handler by carefully querying only the physical fields and methods, without running any extenders.

Finally, a new feature in IDEA 12. Each time a variable is passed to a method, IDEA checks the corresponding formal parameter type and narrows the inferred variable type accordingly if needed. Example:

def s = ...
if ("abc".contains(s)) ...
// now IDEA knows that s is a String

Unfortunately to resolve a method one should know the argument types. Moreover, after resolving one should also map arguments to parameters which is quite non-trivial in Groovy given all those default parameter values, named arguments. One should choose a correct overload. If a method is parameterized, then one argument type may be inferred from another. And we are right now calculating and refining the type of one of the arguments. Another cyclic dependency, quite a nasty one.

The best solution I have invented so far is to restrict this feature. Only allow cases when we can unambiguously resolve the method and map arguments to parameters without knowing argument types. This rules out methods with overloads, with default arguments, with generic parameters. Most of the methods I've ever encountered don't have it all anyway.

That's how I spent about 7 hours today. With all those changes (mostly with the last one) it now takes 3 seconds to highlight a file (instead of 20). Still not ideal, but bearable. And I can finally relax, learn some new things about Model Thinking and sleep.

UPD. Continued.