I'm writing an algorithm where efficiency is very important.
Sometimes I also want to track the algorithm behaviour by calling some "callback" functions.
Let's say my code looks like this:
public float myAlgorithm(AlgorithmTracker tracker) {
while (something) { // millions of iterations
doStuff();
if (tracker != null) tracker.incrementIterationCount(); // <--- How to run the if only once?
doOtherStaff();
}
}
How do I prevent to execute the if statement a million times? Does the compiler see that tracker is never reassigned? If it's null on the first check, it will always be. If it's not, it will never be.
Ideally I would like to tell the compiler to build my code in such a way so that if tracker is null (at runtime) it would run with the same performance as
while (something) { // millions of iterations
doStuff();
doOtherStaff();
}
I thought of two solutions:
I could write two versions of myAlgorithm, one with the calls and one without them, but that would lead to lots of code duplication.
I could extract AlgorithmTracker to an interface and create a fake empty tracker with empty functions. Still, I don't know if the compiler would optimize the calls away.
For most CPU architectures you don't have to worry about the optimization that you want to apply because that particular optimization is part of most contemporary CPU's. it is called branch prediction and current CPU's are very good at this.
on an average every 6th instruction executed by a CPU is a branch, and if for every branch CPU had to wait and evaluate the branch condition it would make the execution lot slower.
Branch prediction and Speculative execution
So when faced with a branch, without evaluating the branch condition, the CPU starts executing (Speculative execution) a path which it thinks is highly likely to be correct and on a later stage when result of branch condition becomes available, CPU checks if it is executing the correct path.
if the path picked by CPU is consistent with the result of branch condition, then CPU knows that it's executing correct path and hence it keeps going at 100% speed, otherwise it will have to flush out all the instructions that it executed speculatively and start with the correct path.
But how does CPU know which path to pick?
enter the branch predictor subsystem of a CPU. in its most basic form it would store the information about the past behavior of a branch, for example if a branch is not being picked for some time then its likely that it would not be picked now.
This is a simple explanation and a real branch predictor is going to be quite complex.
So how effective are these branch predictors ?
Given that at their core the branch predictors are just pattern matching machines, if your branch shows a predictable pattern then you can rest assured that branch predictor will get it right.
but if your branch shows no pattern at all then branch predictor is not going to help you, worst yet it would hamper your code execution because of all the wrong predictions.
How your code is going to work out with the branch predictors?
In your case value of branch control variable never changes, so the branch is either going to be picked every iteration of the loop or it is never going to be picked.
This clearly shows a pattern which even the most basic of the branch predictors can discern. which means your code will practically execute as if condition is not there, because after a first few iterations the branch predictor will be able to pick the path with 100% accuracy.
To know more read this great so thread
Fun fact: this particular optimization is the reason for CPU vulnerabilities such as specter and meltdown
lots of code duplication
Lots of code duplication means there's lots of code. So how could one simple null check influence the performance?
Hoisting null checks out of loops is a very trivial optimization. There's no guarantee that it gets done, but when the JIT can't do it, the CPU can still perfectly predict the branch outcome.(*) So the branch will cost something like ΒΌ cycle as current CPU's are capable of executing say 4 instruction per cycle.
As already said, there's some branching anyway as the JIT has to do the null check. So most probably the net win from this premature optimization is zero.
(*) The prediction can get spoiled by tons of other branches evicting you branch from the predictor (sort of cache). But then your poor branch was just one of many and you needn't care.
I could write two versions of myAlgorithm...but that would lead to lots of code duplication"
Yes, this may be a way to optimize performance and one of rare cases when DRY doesn't work. Another example of such RY technique - the loop unrolling (if your compiler didn't do it :)). Here, the code duplication is the cost you pay for better performance.
But, as for your particular IF, you see, the condition isn't changed in the loop and CPU branch prediction should work quite well. Make good/correct performance tests (with JMH, for example) and something tells me that you will not see any difference with such pico(even not micro)-optimization, the result may be even worse, since there are much-much more important things that may affect the overall performance. Just a few of such ones:
the most efficient compiler optimization is inlining (https://www.baeldung.com/jvm-method-inlining). If your code transformation brakes inlining, think twice and measure the result performance properly
memory allocation and, therefore, GC pauses in the main/critical path of the application may also be an important thing. Reuse mutable objects if required (pooling).
cache misses. Make sure you access the memory sequentially as much as possible. A canonical example - you replace LinkedList by ArrayList to iterate through and your performance becomes much better
etc. etc.
So, don't worry about this particular IF at all.
Performance optimization is a very large and very interesting area. Take care about RIGHT things and make make make correct perf tests... And always think about appropriate algorithms/collections, remember about classical big O.
Related
I'm aware that, for good practice, StringBuilder should be initialised with a capacity value of the expected content. Otherwise, increasing the size after compilation is going to be an expensive operation.
My question is, if we don't know the expected size, how should one go about it? Is there a standard value/way to avoid expensive operations under the hood?
If not, is there potentially a way of alarming/logging in the code if the capacity is bigger than the value given upon initialisation?
I'm aware that, for good practice, StringBuilder should be initialised with a capacity value of the expected content. Otherwise, increasing the size after compilation is going to be an expensive operation.
This is a wildly incorrect statement. It is very bad practice to do this. Even if you know exactly how large it'll be.
If I see this code:
StringBuilder sb = new StringBuilder(in1.length() + in2.length() * 3 + loaded ? suffixLen : 0);
Then this is an additional thing to worry about, test, and keep up to date. I would assume if all this is present that for whatever reason somebody did some performance testing and actually figured out that this saves a worthwhile chunk of cycles, and somehow, in a fit of idiocy, neglected to write an enlightening comment and link to the JMH or profiler result analysis to verify this conclusion.
So, I'd either painstakingly attempt to manually analyse precisely if the calculation is still correct after an update to this code, or, I'd fix the problem and add the tests (and then be utterly befuddled, when, of course, the profile review shows this code is utterly inconsequential), or, I'd go through the considerable trouble of writing an assert based test case that will run the entire operation and then verify at the end that the size calculation done at the top is, in fact, correct.
I don't think you fully grasp why the hyperbolic premature optimization is the root of all evil statement is so popular.
Here's the problem. 99% of the system's resources are spent on 1% of the code. That's not an exaggeration; in fact, that is likely understating the issue.
Developer time is not infinite, and even if it was, the programmer's ability to comprehend code and focus on the relevant parts, is limited because, in the end, they are humans. Spending additional code that needs to be parsed and understood by human eyeballs and brains is therefore bad if the code does something irrelevant. We're literally talking about the same order of magnitude as you throwing a glass of water into the ocean down by the seashore in europe and then watching the water levels rise in manhattan. Beyond any and all ability to measure, and utterly incomprehensive. Bold does not do sufficient justice to how little it matters. Even if this code runs 100 million times a day for 15 years, it amounts to perhaps 5 cents in IAAS deployment costs total over that entire decade and a half, and that's if there is even a performance impact, which often there isn't because modern VMs, GCs, OSes, and CPU architectures get up to some crazy shenanigans.
Furthermore, the system optimizes. Optimizers, such as JVM hotspot engines, are in the end pattern matching machines. They find commonly used patterns and recognize how to run them as efficient as possible. By writing code in ways that nobody else does, it is highly unlikely that code is going to actually outperform the common (idiomatic) case. Most likely because it just doesn't matter, and even if it does, because the idiomatic case gets optimized much more readily.
Here is a trivial example:
List<String> someList = new ArrayList<String>();
for (int i = 0; i < 10000; i++) someList.add(someRandomString);
String[] arrayForm = someList.toArray(new String[0]);
Here you may go: Huh, well, we can optimize this code a little bit and pass new String[10000] instead; this saves the system from having to allocate an admittedly small object (a 0-size string array).
You would be wrong. The above code, with the new String[0], is in fact faster. How can that be? Optimizers, with pattern matching. They recognize the pattern and realize that the system can create a new array of the requisite size, not zero it out, and then run the code that fills it. Whereas the optimization patterns do not include the new String[reqSize] variant where the system could in theory also realize it can allocate the array and then omit zeroing out the array (which the JVM spec guarantees, which merely means the spec guarantees you can never observe that it wasn't zeroed out; it doesn't actually mean the JVM must zero it out, that's where the pattern optimization of not doing so is coming from). However, it doesn't do that - not common enough, and somewhat more complicated.
I'm not saying that new StringBuilder() is neccessarilyt faster than new StringBuilder(knownSize). I'm saying it:
99.9% of the time literally does not make one iota of difference. Not a single nanosecond - the speedup is entirely theoretical: No performance test of any stripe can detect the difference. If a tree falls in the forest, and all that.
You have no idea when that 0.1% of the time even is, or if it's not actually straight up never - 0%. Between a CPU that caches and pipelines (did you know modern CPUs cannot access memory? At all? I bet you didn't. The basic von neumann model of how CPUs work? Totally misleads you if you try to performance analyse machine code if you do that) - VMs, garbage collectors (did you know that garbage is free but live objects are expensive? Re-using an object is in fact more expensive that creating a ton of fast garbage.. depending on many factors, of course this too is an oversimplification. That's the real point: This is an intractable thing; you cannot just look at code and jump to conclusions about performance) - you stand no chance to know what's 'faster'.
The only right move is to write code as simple and as clean as you can ('clean' defined as: When you look at it, you jump to conclusions, and these conclusions are correct. It is easy to adjust in the face of changing requirements, and flexible in how it connects to the rest of the codebase). IF (big if!) real life situations result in a performance issue, you first run a profiler so you know the 1% of the code that is in any way relevant, and then you go ham on that, with JMH benchmarks and all sorts of performance experiments to optimize the heck out of it. If your code is clean, that's great, because almost always this requires adjusting how the code that calls into the 'hot path' or where the code flows to out of the 'hot path' - and the cleaner your code the easier that will be.
Needless performance optimization almost invariably reduces flexibility, and makes code harder to understand.
Hence, objectively, micro-optimizing like this just makes your code slower and buggier for literally no benefit. Not even a tiny, almost immeasurable one.
Hence, the advice is silly. The only correct call is new StringBuilder() - no pre-configured size. The one and only excuse you have to write new StringBuilder(presetCapacity) is if there's a lengthy comment that immediately precedes it that lays out in a lot of detail, or links to a ticket, the exact performance study done to indicate this indeed fixes a real performance issue and how to recreate that study, and on what schedule it should be revisited.
I just came across this article: Compute the minimum or maximum of two integers without branching
It starts with "[o]n some rare machines where branching is expensive...".
I used to think that branching is always expensive as it often forces the processor to clear and restart its execution pipeline (e.g. see Why is it faster to process a sorted array than an unsorted array?).
This leaves me with a couple of questions:
Did the writer of the article get that part wrong? Or was this article maybe written in a time before branching was an issue (I can't find a date on it).
Do modern processors have a way to complete minimal branches like the one in (x < y) ? x : y without performance degradation?
Or do all modern compilers simply implement this hack automatically? Specifically, what does Java do? Especially since its Math.min(...) function is just that ternary statement...
Did the writer of the article get that part wrong? Or was this article maybe written in a time before branching was an issue (I can't find a date on it).
The oldest comment is 5 years old, so it's no hot news. However, unpredictable branching is always expensive and so was it 5 years ago. In the meantime, it just got worse as modern CPUs can do much more per cycle and a mispredicted branch therefore cost more work.
But in a sense, the writer is right. The majority of CPUs is not found in our PCs and servers, but in embedded devices, where the situation differs.
Do modern processors have a way to complete minimal branches like the one in (x < y) ? x : y without performance degradation?
Yes and no. AFAIK Math.max gets always translated as a conditional move, which means no branching. You own max may or may not use it, depending on statistics the JVM collected.
There's no silver bullet. With predictable outcomes, branching is faster. Finding out exactly, what pattern the CPU recognizes, is hard. The JVM simply looks at how often a branch gets takes and uses a magic threshold of about 18%. See my own question and answer for details.
Or do all modern compilers simply implement this hack automatically? Specifically, what does Java do? Especially since its Math.min(...) function is just that ternary statement...
It's actually a compiler intrinsic. Whenever the JITc sees this very method called, it handles it specially. When you copy the method, it gets no special treatments.
In this case, the intrinsic is not very useful, as it's something what gets heavily optimized anyway. For methods like Long#numberOfLeadingZeros, the intrinsic is essential, as the code is rather long and slow and modern CPUs get do it in a single cycle.
Generally speaking, is there a significant differance in the processing speeds of these two example segments of code, and if so, which should complete faster? Assume that "processA(int)" and "processB(int)" are voids that are common to both examples.
for(int x=0;x<1000;x++){
processA(x);
processB(x);
}
or
for(int x=0;x<1000;x++){
processA(x);
}
for(int x=0;x<1000;x++){
processB(x);
}
I'm looking to see if I can speed up one of my programs and it involves cycling through blocks of data several times and processing it differant ways. Currantly, it runs a seperate cycle for each processing method, meaning a lot of cycles get run in total, but each cycle does very little work. I was thinking about rewriting my code so that each cycle incorporates every processing method; in other words, much fewer cycles, but each cycle has a heavier workload.
This would be a very intensive rewrite of my program stucture. So unless it would give me a significant performance boost, it won't be worth the trouble.
The first case will be slightly faster then the second case because a for loop in and of itself has an effect on performance. However, the main question you should ask yourself is this: will the effect be of significance to my program? If not, you should opt for clear and readable code.
One thing to remember in such a case is that the JVM (Java Virtual Machine) does a whole lot of optimisation, and in your case the JVM can even get rid of the for-loop and rewrite the code into 1000 successive calls to processA() and processB(). So even if you have two for loops, the JVM can get rid of both, making your program more optimal than even your first case.
To get a basic understanding of method calls, cost, and the JVM, you can read this short article:
https://plumbr.eu/blog/how-expensive-is-a-method-call-in-java
Absolutely, fusing the 2 loops will be faster. (Some compilers do this automatically as an optimization.) How much faster? That depends on how many iterations the loops are running. Unless the number of iterations is very high, you can expect that the improvement will be minimal.
The single loop case will contain fewer instructions so will run faster.
But, unless processA and processB are very quick functions, such a substantial refactoring would give you negligible performance gain.
If this is production code, you should also take care as there may be side effects. You should make alterations in the context of a unit testing framework testing the code in question. (In C++ for example x may be passed by reference and could be modified by the functions! Of course Java has no such hazard but there may be other reasons why all processA functions have to run before all processB functions and the program comments may not make this clear).
The first piece of code is faster, because it only has one for loop.
If, however, you need to do something AFTER processA() has been executed n times and before processB()'s loop starts, then the second option would be ideal.
When conducting performance testing of java code, you want to test JIT compiled code, rather than raw bytecode. To cause the bytecode to be compiled, you must trigger the compilation to occur by executing the code a number of times, and also allow enough yield time for the background thread to complete the compilation.
What is the minimum number of "warm up" executions of a code path required to be "very confident" that the code will be JIT compiled?
What is the minimum sleep time of the main thread to be "very confident" that the compilation has completed (assuming a smallish code block)?
I'm looking for a threshold that would safely apply in any modern OS, say Mac OS or Windows for the development environment and Linux for CI/production.
Since OP's intent is not actually figuring out whether the block is JIT-compiled, but rather make sure to measure the optimized code, I think OP needs to watch some of these benchmarking talks.
TL;DR version: There is no reliable way to figure out whether you hit the "steady state":
You can only measure for a long time to get the ball-park estimate for usual time your concrete system takes to reach some state you can claim "steady".
Observing -XX:+PrintCompilation is not reliable, because you may be in the phase when counters are still in flux, and JIT is standing by to compile the next batch of now-hot methods. You can easily have a number of warmup plateaus because of that. The method can even recompile multiple times, depending on how many tiered compilers are standing by.
While one could argue about the invocation thresholds, these things are not reliable either, since tiered compilation may be involved, method might get inlined sooner in the caller, the probabilistic counters can miss the updates, etc. That is, common wisdom about -XX:CompileThreshold=# is unreliable.
JIT compilation is not the only warmup effect you are after. Automatic GC heuristics, scheduler heuristics, etc. also require warmup.
Get a microbenchmark harness which will make the task easier for you!
To begin with, the results will most likely differ for a JVM run in client or in server mode. Second of all, this number depends highly on the complexity of your code and I am afraid that you will have to exploratively estimate a number for each test case. In general, the more complex your byte code is, the more optimization can be applied to it and therefore your code must get relatively hotter in order to make the JVM reach deep into its tool box. A JVM might recompile a segment of code a dozen of times.
Furthermore, a "real world" compilation depends on the context in which your byte code is run. A compilation might for example occur when a monomorphic call site is promoted to a megamorphic one such that an observed compilation actually represents a de-optimization. Therefore, be careful when assuming that your micro benachmark reflects the code's actual performance.
Instead of the suggested flag, I suggest you to use CompilationMXBean which allows you to check the amount of time a JVM still spends with compilation. If that time is too high, rerun your test until the value gets stable long enough. (Be patient!) Frameworks can help you with creating good benchmarks. Personally, I like caliper. However, never trust your benchmark.
From my experience, custom byte code performs best when you immitate the idioms of javac. To mention the one anecdote I can tell on this matter, I once wrote custom byte code for the Java source code equivalent of:
int[] array = {1, 2, 3};
javac creates the array and uses dup for assigning each value but I stored the array reference in a local variable and loaded it back on the operand stack for assigning each value. The array had a bigger size than that and there was a noteable performance difference.
Finally, I recommend this article before writing a benchmark.
Not sure about numbers, but when doing speed tests what I do is:
Run with the -XX:-PrintCompilation flag
Warm up the JVM until there are no more compilation debug messages generated and, if possible, timings become consistent
Assume I have a loop (any while or for) like this:
loop{
A long code.
}
From the point of time complexity, should I divide this code in parts, write a function outside the loop, and call that function repeatedly?
I read something about functions very long ago, that calling a function repeatedly takes more time or memory or like something, I don't remember it exactly. Can you also provide some good reference about things like this (time complexity, coding style)?
Can you also provide some reference book or tutorial about heap memory, overheads etc. which affects the performance of program?
The performance difference is probably very minimal in this case. I would concentrate on clarity rather than performance until you identify this portion of your code to be a serious bottleneck.
It really does depend on what kind of code you're running in the loop, however. If you're just doing a tiny mathematical operation that isn't going to take any CPU time, but you're doing it a few hundred thousand times, then inlining the calculation might make sense. Anything more expensive than that, though, and performance shouldn't be an issue.
There is an overhead of calling a function.
So if the "long code" is fast compared to this overhead (and your application cares about performance), then you should definitely avoid the overhead.
However, if the performance is not noticably worse, it's better to make it more readable, by using a (or better multiple) function.
Rule one of performance optmisation: Measure it.
Personally, I go for readable code first and then optimise it IF NECESSARY. Usually, it isn't necessary :-)
See the first line in CHAPTER 3 - Measurement Is Everything
"We should forget about small efficiencies, say about 97% of the time:
premature optimization is the root of all evil." - Donald Knuth
In this case, the difference in performance will probably be minimal between the two solutions, so writing clearer code is the way to do it.
There really isnt a simple "tutorial" on performance, it is a very complex subject and one that even seasoned veterans often dont fully understand. Anyway, to give you more of an idea of what the overhead of "calling" a function is, basically what you are doing is "freezing" the state of your function(in Java there are no "functions" per se, they are all called methods), calling the method, then "unfreezing", where your method was before.
The "freezing" essentially consists of pushing state information(where you were in the method, what the value of the variables was etc) on to the stack, "unfreezing" consists of popping the saved state off the stack and updating the control structures to where they were before you called the function. Naturally memory operations are far from free, but the VM is pretty good at keeping the performance impact to an absolute minimum.
Now keep in mind Java is almost entirely heap based, the only things that really have to get pushed on the stack are the value of pointers(small), your place in the program(again small), and whatever primitives you have local to your method, and a tiny bit of control information, nothing else. Furthermore, although you cannot explicitly inline in Java(though Im sure there are bytecode editors out there that essentially let you do that), most VMs, including the most popular HotSpot VM, will do this automatically for you. http://java.sun.com/developer/technicalArticles/Networking/HotSpot/inlining.html
So the bottom line is pretty much 0 performance impact, if you want to verify for yourself you can always run benchmarking and profiling tools, they should be able to confirm it for you.
From a execution speed point of view it shouldn't matter, and if you still believe this is a bottleneck it is easy to measure.
From a development performance perspective, it is a good idea to keep the code short. I would vote for turning the loop contents into one (or more) properly named methods.
Forget it! You can't gain any performance by doing the job of the JIT. Let JIT inline it for you. Keep the methods short for readability and also for performance, as JIT works better with short methods.
There are microptimizations which may help you gain some performance, but don't even think about them. I suggest the following rules:
Write clean code using appropriate objects and algorithms for readability and for performance.
In case the program is too slow, profile and identify the critical parts.
Think about improving them using better objects and algorithms.
As a last resort, you may also consider microoptimizations.