Why is 2 * (i * i) faster than 2 * i * i in Java?(stackoverflow.com) |
Why is 2 * (i * i) faster than 2 * i * i in Java?(stackoverflow.com) |
In my experience, loop unrolling should basically never be done except in extremely degenerate cases; I remember not long ago someone I know who also optimises Asm remarking "it should've died along with the RISC fad". The original goal was to reduce per-iteration overhead associated with checking for end-of-loop, but any superscalar/OoO/speculative processor can "execute past" those instructions anyway; all that unrolling will do is bloat the code and work against caching. Memory bandwidth is often the bottleneck, not the core.
Not true. Like many such optimizations, loop unrolling can be useful because it makes downstream loads constant.
For example:
float identity[4][4];
for (unsigned y = 0; y < 4; y++)
for (unsigned x = 0; x < 4; x++)
identity[y][x] = y == x ? 1 : 0;
... do some matrix math ...
(You might ask "who would write this code?" As Schemers say: "macros do.")
See LLVM's heuristics: http://llvm.org/doxygen/LoopUnrollPass_8cpp.html#ad7c38776d7...
To expand on this point - in the more prosaic world of C++ - this sort of code comes about all the time in templated code. For example, the above loop you posted might have been found in something like:
``` template <unsigned N, unsigned M> class Matrix { static Matrix Identity() { ... } }
auto m = Matrix<4, 4>::Identity();The other major source of these sorts of constants leading to DCE oppportunities is inlining. Consider a more classical, matrix implementation that is not templated and doesn't lift its dimensions into the type:
``` class Matrix { unsigned n; unsigned m; static Matrix Identity(unsigned n, unsigned m) { ... } }
// Somewhere else
Matrix m = Matrix::Identity(4, 4);Here, the inlining of the call to `Identity` at the call-site will turn the `n` and `m` in the body of `Identity` into the constant 4.
If I had to make an educated guess - inlining typically generates these (i.e. partial evaluation, constant folding, an DCE) situations most often in compilers. An incredible amount of information can flow from caller to callee when you specialize the callee for that call-site.
This is also how compilers do things, but it is only that we schemers can see the intermediate result much easier using simple source->source transformations.
In most cases on modern systems, small loops should remain compact as possible, to stay in the uop cache. The "for" loop overhead (the inc, cmp, and jmp instructions) effectively execute in parallel. Modern systems are highly out-of-order and the for-loop overhead is virtually nil.
For small loops, unrolling is the most important of all, since loop carried dependency chains are dense, and the loop overhead is a high fraction of the overall work.
It is easy to get a 2x speedup by unrolling a small loop, and even larger speedups are not uncommon.
So this "unrolling rarely helps" idea is just as much of a myth as "unrolling never helps". The main problem with unrolling is that the compilers usually don't do it intelligently - usually loops are unrolling if some kind of threshold is met, depending on compiler options - but this always happens in kind of a feed-forward way, rather thank a feed-back way, which would involve unrolling the loop and analyzing the benefit and costs after further optimization passes.
There must be so many examples of bubble sort where quicksort would be better, and other code patterns which can be identified and replaced with something orders of magnitude faster.
> the reason the optimizer does what you see is undefined behavior due to signed integer overflow
Yes undefined behavior gives the optimizer the right in this case to transform the code into anything, including a nonsense answer, or a trap instruction. But the optimizer did not; it produced the right answer under 2's complement arithmetic.
EDIT: It seems that clang doesn't support #pragma GCC optimize, so it's a no-op in that snippet. It doesn't change the result though. If you pass -fwrapv flag to clang, it will be optimized in exactly the same way.
Just to be clear, undefined behavior means the standard allows implementations to do what they they feel is the right thing to do under that scenario, and the outcome will still comply with the standard.
Why? Is this optimisation particularly difficult to implement? Or is it just missed low-hanging fruit? It sure looks easy (like: rearrange expressions to keep the expression tree shallow and left-branching to avoid stack operations).
This is why there is a real commercial jvm market with azul.
The person who answered the multiple question dove into byte code...but also answered questions on Angular.
I am unworthy...and this is what impostor syndrome looks like.
https://codegolf.stackexchange.com/questions/11880/build-a-w...
https://copy.sh/life/?pattern=TetrisOTCAMP.mc
OH MY GOD!
My eyes are watering and I can’t stop deeply chuckling at the sheer collaborative esoteric audacity
“Pessimization”
“Diabolical incompetence”
graal:
[info] SoFlow.square_i_two 10000 avgt 10 5338.492 ± 36.624 ns/op // 2 *\sum i * i
[info] SoFlow.two_i_ 10000 avgt 10 6421.343 ± 34.836 ns/op // \sum 2 * i * i
[info] SoFlow.two_square_i 10000 avgt 10 6367.139 ± 34.575 ns/op // \sum 2 * (i * i)
regular 1.8:
[info] SoFlow.square_i_two 10000 avgt 10 6393.422 ± 27.679 ns/op
[info] SoFlow.two_i_ 10000 avgt 10 8870.908 ± 35.715 ns/op
[info] SoFlow.two_square_i 10000 avgt 10 6221.205 ± 42.408 ns/op
[info] 0x000000011433ec03: mov %r8d,%ecx
[info] 0x000000011433ec06: shl %ecx ;*imul {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_two_i_@15 (line 41)
[info] 0x000000011433ec08: imul %r8d,%ecx ;*imul {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_two_i_@17 (line 41)
[info] 0x000000011433ec0c: add %ecx,%r9d ;*iadd {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_two_i_@18 (line 41)
[info] 0x000000011433ec0f: lea 0x5(%r11),%r8d ;*iinc {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_two_i_@20 (line 40)
[info] 0x000000010e2918bb: imul %r8d,%r8d ;*imul {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_square_i_two@15 (line 32)
[info] 0x000000010e2918bf: add %r8d,%ecx ;*iadd {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_square_i_two@16 (line 32)
[info] 0x000000010e2918c2: lea 0x3(%r11),%r8d ;*iinc {reexecute=0 rethrow=0 return_oop=0}
[info] ; - add.SoFlow::test_square_i_two@18 (line 31)
The compiler should detect that the two expressions are strictly equivalent and generate whatever code it believes is the fastest.
Any idea why it is this way?
https://www.youtube.com/watch?v=_cFwDnKvgfw
There are also other tools like JITWatch.
https://github.com/AdoptOpenJDK/jitwatch/wiki/Videos-and-Sli...
I’m not sure it does overflow differently but I would expect overflow to behave consistently as written, and not be dependent on optimization, is that not the case?
Since the early days of Java, OEM vendors targeting embedded targets do support AOT compilation, with possible PGO feedback.
Some vendors like IBM, also provide similar capabilities on their regular Java toolchains.
And Maxime finally graduated as Graal/Substrate, which is also another way of compiling Java.
But all in all, everyone is transitioning to the benefits of bytecode as intermediate executable format.
Even some cool LLVM optimizations, like ThinLTO, are only possible thanks to using bytecode.
You have the old JIT, replaced by RyuJIT on .NET 4.6 and .NET Core.
Then .NET Native, which does AOT compilation via the same backend as Visual C++.
Followed by Mono's JIT/AOT implementation.
Windows/Windows Phone 8.x used a Bartok derived compiler for the MDIL format.
Same applies to Java though, as the answer only goes through what Hotspot does, but there are many other JIT/AOT compilers for Java as well.
Requirements to Consider for Go 2 Error Handling
https://gist.github.com/networkimprov/961c9caa2631ad3b95413f...
(My cup of bitterness doth overflow.)
fn main() {
let a: i8 = 125;
let b: i8 = 3;
let c: i8 = (a + b) / 2;
let d: i8 = b + ((a - b) / 2);
println!("{} {}", c, d);
}
Here's another example using floating point numbers:
fn main() {
let mut total1: f32 = 0.0;
let mut total2: f32 = 0.0;
let mut counter1: f32 = 0.0;
let mut counter2: f32 = 100.0;
for _ in 0 .. 10001 {
total1 += counter1;
total2 += counter2;
counter1 += 0.01;
counter2 -= 0.01;
}
println!("{} {}", total1, total2);
}
Whereas for int ops, equality works within the limits of the design.
In short equality means something different in fp by design. For int, it means what we think it means within its limits. When we overflow, then things get screwy.
However, the compiler ('s optimization step) is not magic and produces suboptimal code sometimes. Back when C was young, this was frequently the case (1970's and 1980's), so dropping into assembly to hand-code performance critical sections is just what people did, in order to get software to run smoothly.
Thankfully this is largely no longer the case, however it does still happen.
In Java's case, the JVM runs on top of multiple different architectures which makes optimization even more complicated.
Low-level instruction generation and optimization is just one topic under the umbrella of compiler design, which is a huge (and fascinating!) discipline to get into.
"An overview of the PL.8 compiler"
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.453...
Notice the architecture, quite similar to the layers and compiler phases used in modern compiler toolchains like LLVM.
The secret sauce, if one can call it as such, was that PL.8 had a richer type system, and the System/370 was a bit beefier than most platforms adopting C compilers.
I agree that compilers are not always perfect, but in this particular case the two expressions are trivially equivalent from the associativity of the multiplication so the distinction had to be intentional.
But as gnuvince pointed out, the two expressions are not equivalent when you consider integer overflow.
Also what good will it bring? What if the canonical expression triggers the slow path? Now you have no means to change it into the fast version.
Further, in the case of floating point operations, operation order matters for rounding. And with integer operations, the actual form used can be important for preventing overflow (of intermediate results).
It simplified the code a lot and the optimizer was a lot faster than having to do it all myself at expansion time. I lazily just generate about 30 lines of code for a simple loop that in the end sometimes even is unrolled to the final reault due to guiles optimizer and partial evaluation.
1) This is not the forum to bring it up
2) Given its warts it gets FAAAARRRRR too much attention IMHO
Sorry for snark.
<smallest possible positive number> * 0.5 * 2
When evaluated like that:
(<SPPN> * 0.5) * 2
the result is 0 * 2 = 0
And when evaluated the other way around:
<SPPN> * (0.5 * 2)
the result is <SPPN> * 1 = <SPPN>
double sppn = Double.MIN_VALUE;
double first = sppn * (0.5 * 2);
double second = (sppn * 0.5) * 2;
System.out.println(sppn);
System.out.println(first);
System.out.println(second);These days I find myself telling people that benchmark numbers don’t matter on their own. It’s important what models you derive from those numbers. Refined performance models are by far the noblest and greatest achievement one could get with the benchmarking — it contributes to understanding how computers, runtimes, libraries, and user code work together. --Aleksey Shipilёv https://shipilev.net/blog/2014/nanotrusting-nanotime/
That's a bit of an obscure comment, but I keep coming back to it as I learn about performance work and benchmarking.
That’s the real world implication of undefined behaviour.
Floating point arithmetic is different, but Java gives itself wiggle room by not exactly specifying many results unless you choose "strict math". That's not UB though: it's just a range of possible outcomes.
Java can't have UB in the C/C++ sense, since it would break the security sandbox. It certainly has things without specifically defined values, such as hashCode() and what happens under data races isn't entirely deterministic, but it doesn't approach UB in the C/C++ sense.
Seems that not checking for overflow and not being able to assume there is no overflow, would give the worst of both worlds (slower because of lack of some optimizations but still not safe against overflow like C#’s “checked”).
I thought a deref of a possibly overflown value was what could risk security, ie so long as all array indices and similar are range checked then nothing bad can happen?
I'm saying that it is uncommon. For example, the example you gave works fine! Most examples work fine since you can do most of the same type of transformations. The exceptions are largely operations where the upper bits affect the lower bits. Division is one, and right shift is another, but nearly all the other bitwise and math operations do not have this property.
The other place where signed-wrapping-is-UB is often used for optimization is in things like loop bounds. Given something like:
for (int i = start; i < end; i++)
In Java, the compiler couldn't take advantage of that. However, the impact isn't as big since in any loop that accesses arrays based on the index, bounds checks have to be made, and a typical pattern is to do some up front bounds checks [1] which can guarantee than the main body of the loop can then be run without additional checks: and this subsumes the checks that would be needed due to wrapping signed values anyways. So basically you have to do those checks anyways in many cases.
> A branch like “if a+1 < a” seems like it could under a clever compiler
Sure, but these cases aren't very interesting for comparing the effectiveness of the signed overflow optimizations. It's a case where the optimization breaks the (wrongly expressed) intent of the programmer, so the difference is only between a fast, broken program and a slower, perhaps correct one.
Presumably in the signed-overflow-is-UB world, such a check will have to re-written in a different way (which might even end up slower).
> Seems that not checking for overflow and not being able to assume there is no overflow, would give the worst of both worlds (slower because of lack of some optimizations but still not safe against overflow like C#’s “checked”).
Not exactly - it's just a different point on the spectrum. On the one side you have overflow is UB, which leads to some (fairly limited) optimization opportunities, but also more unexpected results and broken programs, and on the far other size you have overflow is an always-caught checked error. Java is somewhere in the middle: overflow is well-defined (it wraps), which gets you most of the speed of the UB approach (no checking needed, only a few relatively minor optimizations are lost) without the unexpected results of UB - but you still have broken programs when overflow actually occurs (unless it was unexpected). See also Rust's strategy here which is interesting.
> I thought a deref of a possibly overflown value was what could risk security, ie so long as all array indices and similar are range checked then nothing bad can happen?
When "nothing bad can happen" from the point of view of the JVM, i.e., the security sandbox isn't broken, you can't access arbitrary memory, violate the type system, interfere with unrelated code, etc. Of course, overflowing an index and then accessing into the array could still do plenty of "bad things" depending on the higher level semantics of the program, since you are now executing an unexpected code path. You might return sensitive data to an attacker, whatever.
---
[1] More details: https://wiki.openjdk.java.net/display/HotSpot/RangeCheckElim...
At least, I haven't seen todays (2018) compilers cut dependency chains on without a #pragma omp reduce, or other assistance from the programmer.
I've unrolled loops myself to good sucess. But it isn't as easy as some people think it is.
Without knowing about dependency chains or ILP, small loops are often best left in smaller compact form. You leverage the branch predictor and minimize uop cache usage. I'd argue the typical program benefits from compact loops more.
That's nothing specific to unrolling though: it applies to all optimizations which trade off size and speed.
About dependency chains and ILP: of course the compiler is in the perfect places to be aware of all of this. They have detailed machine models updated carefully as new CPUs come out (in fact, some of the earliest details about new CPU models often comes from compiler commits from insiders where hardware details are necessarily leaked).
A compiler could certainly statically analyze a loop using an approach similar to Intel IACA or OASCA [1], and then unroll the loop a few times and run the analysis again and see if it improves. So it doesn't need any kind of sophisticated analysis, just try-and-measure. Of course, compilers don't actually work like this. One of reasons it is not so easy is that optimizations is done in layers, against a machine independent IR, and you might not be able to carefully evaluate the impact of unrolling until some later time, possibly as late the machine-dependent instruction emission. This issue is pervasive across many compiler optimizations, and leads to many cases where a compiler generates bad code where a human wouldn't.
---
Integer-based code probably can (theoretically) be optimized by a good enough compiler I haven't thought of all overflow / underflow conditions, but most "normal" code, consisting of adds, subtracts, and AND/OR/XORs, probably can be broken up as needed by the compiler.
But floating-point code MUST execute in precisely the order that the programmer specifies. In general, (A+B)+C does NOT equal A + (B+C) for floats.
For integer-code, divison is definitely order-specific, while multiplication might be order-specific in overflow conditions (I haven't 100% thought this through yet...). But in any case, the compiler can probably split up operations to take advantage of ILP as long as the results output the same bits.
Which isn't true for some operations (ie: floating point add)
--------
In any case, the "#pragma omp reduce" gives the compiler assurance from the programmer that the ordering does not matter. I think that's why something like that could benefit from parallelism a bit better.
He probably has actual experience with branch prediction. He probably dabbled or had experience with angular in other jobs (he worked at google apparently, so maybe there).
He'll most likely be stumped if you provide a graphic problem that a graphic designer with a few years of experience would solve in an instant, or an ML problem for a data scientist with similar experience.
That doesn't mean he isn't extremely smart. He most likely is (it takes a lot of brain to do these things), but the fact that you can't tell branch prediction problems even though you had some computer architecture class in the past is irrelevant.
Can’t tell if clever joke, or typo
https://stackoverflow.com/users/485343/rustyx
He answered a question on ticking clock in Angular.
> leads to some (fairly limited) optimization opportunities,
Ok, I was just mistaken in my belief that integer overflow shenanigans was a major contributor to how a modern compiler optimized e.g. loops.
> you can't access arbitrary memory, violate the type system, interfere with unrelated code
Right. I was considering the sandbox in the sense only of process security rather than program/type soundness.
Yes, there is some impact but I think it's large, at least based on looking at a lot of assembly, and going over the typical examples of where it helps. In the cases it does help, it could make a big difference on a loop, let's say 2x the speed, but these aren't all that common.
> Right. I was considering the sandbox in the sense only of process security rather than program/type soundness.
Usually those two things end up tightly bound together: it's hard to impossible to enforce a sandbox if the user can escape the type system.