Monday 13 April 2015

On Arrays.fill, Intrinsics, SuperWord and SIMD instructions

{This post turned rather long, if you get lazy feel free to skip to the summary}
Let's start at the very beginning, a very good place to start... My very first post on this blog was a short rant on intrinsics, and how they ain't what they seem. In that post I made the following statement:
"intrinsic functions show up as normal methods or native methods"
Which is correct. An intrinsic function is applied as a method substitution. A method call will appear in the code and the compiler will replace it's apparent source-level implementation with a pre-cooked implementation. In some cases intrinsics are sort of compilation cheats, the idea being that some bits of functionality are both very important (i.e. worth while optimizing) and can benefit from a hand crafted solution that will be better than what the compiler can achieve. The end result can be in one of a few flavours:
  1. Method call replaced with a call to a JVM runtime method: E.g. System.arrayCopy is replaced with a call to a method stub generated by the runtime for all array types. This method call is not a JNI call, but it is a static method call that is not inlined.
  2. Method call replaced with one or more instructions inlined: E.g. Unsafe.getByte/compareAndSet/Math.max
  3. Method call replaced with compiler IR implementation: E.g. java.lang.reflect.Array.getLength
  4. A mix of the above: E.g. String.equals is partially implemented in IR, but the array comparison is a call to a method stub.
The intrinsics are all set up in vmSymbols.hpp and if you look, you'll see Arrays.fill is NOT on the list. So why am I talking about Chewbacca? Because it is something like an intrinsic...

The Arrays.fill SIMD Opportunity

Arrays.fill is the Java memset (fills an array with a given value), and just like System.arrayCopy (memcpy in C lingo) is worth the effort to optimize and offers the same kind of opportunity. What opportunity might that be, you ask? the opportunity to use SIMD (Single Instruction Multiple Data) instructions when the underlying CPU offers them (I assume for the sake of discussion AVX enabled CPUs i.e. since Sandy Bridge, I find this listing of intel intrinsics useful to explain and sort through the available instructions). These instructions allow the CPU to operate on up to 256 bit (512 bit soon) chunks of data, thus transforming 32 byte sized MOV instructions into a single wide MOV instruction (E.g. the intel C instrinsic  _mm256_storeu_si256 or the corresponding instruction vmovdqu). SIMD instructions are good for all sorts of operations on vectors of data, or arrays, which is why the process of transforming element by element operations into SIMD instructions is also referred to as vectorization.
The actual assembly stub is generated dependent on CPU and available instruction set. For x86 the code is generated by the macroAssembler_x86.cpp, and the observant digger into the code will find it makes use of the widest memory instructions it can identify the processor is capable of. Wider is better baby! If you are not morbidly curious about what the implementation looks like, skip the next wall of assembly and you'll be back in Java land shortly.
Here's what the assembly boils down to when UseAVX>=2/UseSSE>=2/UseUnalignedLoadStores=true:

Roughly speaking the algorithm above is:
  1. Fill up an XMM register with the intended value
  2. Use the XMM register to write 64 byte chunks (2 vmovdqu) until no more are available
  3. Write leftover 32 byte chunk (skipped if no matching leftovers)
  4. Write leftover 8 byte chunks (skipped if no matching leftovers)
  5. Write leftover 4 bytes (skipped if no matching leftovers)
  6. Write leftover 2 bytes (skipped if no matching leftovers)
  7. Write leftover 1 bytes (skipped if no matching leftovers)
It ain't nice, but we do what we gotta for performance! There are variations of the above described across the internets as the done thing for a memset implementation, this might seem complex but is pretty standard... anyway, moving right along.

The Arrays.fill 'intrinsic'

Arrays.fill is different from System.arrayCopy because, as it's absence from vmSymbols suggests, it's not a method substitution kind of intrinsic (so technically not an intrinsic). What is it then? Arrays.fill is a code pattern substitution kind of compiler shortcut, basically looking for this kind of loop:
And replacing it with a call into the JVM memset implementation (I recently learnt the same thing is done by GCC as well, see code to assembly here). The pattern matching bit is done in loopTransform.cpp. This feels enough like an intrinsic grey area that the method doing the pattern match and replace is called intrinsify_fill.
Pattern matching makes this optimization potentially far more powerful than method substitution as the programmer doesn't have to use a special JDK method to convey meaning, they can just express their meaning in code and the compiler 'knows' that this simple loop means 'fill'. Compare that with System.arrayCopy where rolling your own leads to performance that is much worse than that offered by the intrinsic.
Let's prove me right (my favourite thing, beats kittens and all that crap), here's a JMH (see the JMH reference page for more JMH info/examples) benchmark comparing Arrays.fill to a hand rolled fill, and System.arrayCopy to handrolled array copy:
And the results are (Oracle JDK8u40/i7-4770@3.40GHz/Ubuntu, array is 32K in size)?
ArrayFill.fillBytes                561.540 ± 10.814 ns/op
ArrayFill.manualFillBytes          557.901 ± 5.255  ns/op
ArrayFill.manualReversedFillBytes 1017.856 ± 0.425  ns/op
ArrayFill.copyBytes               1300.313 ± 13.482 ns/op
ArrayFill.manualCopyBytes         1477.442 ± 13.030 ns/op

We can verify that the call out to the JVM fill method happens for fillBytes/manualFillBytes by printing out the assembly:

So what have we learnt so far:
  • Use System.arrayCopy, it is better than your handrolled loop. But surprisingly not hugely better, hmmm.
  • You don't have to use Arrays.fill, you can roll your own and it works the same. Notice the call out to the fill method. But...
  • Don't get too creative rolling your own. If you get too funky (like filling the array backwards) it'll fall apart and the 'intrinsic' won't happen. But do note that the reverse fill still has some of that good SIMD stuff going, we'll get to that in a sec.

Are The Other Types Filling The Love?

It all sounds great don't it? Let's see how this pans out for other types. We'll be filling an array of 32KB. To be uniform across data types that means a 16K chars/shorts array, an 8K ints/floats array and a 4K array of longs. I added an 8K array of objects, which is the same size for compressed oops on the Oracle JVM (reference size is 4 bytes, same as an int).
The JMH benchmark code is as you'd expect:
Here's some reasonable expectations:
  • If no optimizations are present, wider writes are more efficient. It follows that the longFill would be the fastest. But...
  • Given a clever compiler the fill loop is replaced with the widest writes possible, so there should be no significant difference. But the fill optimization does not cover double/long/object arrays, so we might expect longFill to be the worst performer.
  • An objects array is not that different from an int array, so performance should be similar. Sure there's a write barrier, but it need only be done once per card (not once for the whole array as I thought initially, god bless Shipilev and PrintAssembly), so that's an extra byte write per card of elements filled. A card is per 512 bytes, each element is 4 bytes, so that's one card per 128 elements. Given there is no fill method implemented for it we may expect it to be slightly worse than the longFill.
  • We should not rely on expectations, because performance is best measured.
As you'd expect the results are somewhat different than the expectations (Oracle JDK8u40/i7-4770@3.40GHz/Ubuntu):
ArrayFill.fillBytes     561.540 ± 10.814  ns/op
ArrayFill.fillChars     541.901 ±  4.833  ns/op
ArrayFill.fillShorts    532.936 ±  4.508  ns/op
ArrayFill.fillInts      543.165 ±  3.788  ns/op
ArrayFill.fillFloats    537.599 ±  2.323  ns/op
ArrayFill.fillLongs     326.770 ±  3.267  ns/op
ArrayFill.fillDoubles   346.840 ±  5.786  ns/op
ArrayFill.fillObjects  4388.242 ± 11.945  ns/op

Say WOT?
For bytes/chars/shorts/ints/floats Arrays.fill performs very similarly. This much is as expected from the second point above. But filling an array of longs/doubles is better than the others. The funny thing is, there's no fill function implemented for the long array, how come it is so darn quick? Also, why does the objects fill suck quite so badly when compared with the rest (I will not be addressing this last question! I refuse! this post is too fucking long as it is!)?
This is what happens when we turn off the OptimizeFill flag:
ArrayFill.fillBytes    1013.803 ± 0.227  ns/op
ArrayFill.fillChars     323.806 ± 3.879  ns/op
ArrayFill.fillShorts    323.689 ± 4.499  ns/op
ArrayFill.fillInts      326.336 ± 1.559  ns/op
ArrayFill.fillFloats    319.449 ± 2.048  ns/op
ArrayFill.fillLongs     328.692 ± 3.282  ns/op
ArrayFill.fillDoubles   345.035 ± 6.362  ns/op
ArrayFill.fillObjects  4397.130 ± 7.161  ns/op

Strange innit? now we got char/int/long arrays all performing similarly. In fact, with the exception of the byte array, everything is better than it was with the optimization.

Superword to the rescue! 

Turns out the JIT compiler is clued up on the topic of SIMD parallelisation by way of Superword Level Parallelism (see the original paper here):
In some respects, superword level parallelism is a restricted form of ILP (Instruction Level Parallelism). ILP techniques have been very successful in the general purpose computing arena, partly because of their ability to find parallelism within basic blocks. In the same way that loop unrolling translates loop level parallelism into ILP, vector parallelism can be transformed into SLP. This realization allows for the parallelization of vectorizable loops using the same basic block analysis. As a result, our algorithm does not require any of the complicated loop transformations typically associated with vectorization. In fact, vector parallelism alone can be uncovered using a simplified version of the SLP compiler algorithm.
Superword level parallelism is defined as short SIMD parallelism in which the source and result operands of a SIMD operation are packed in a storage location.
Vector parallelism is a subset of superword level parallelism.
The Hotspot compiler implements SLP optimizations in superword.cpp and you are invited to dive into the implementation if you like. I'm going to focus on it's impact here, and to do that I only need to know how to turn it on and off (core competency for any software person). It's on by default, so above results are what happens when it is on, here's what life looks like when it is off too (so -XX:-OptimizeFill -XX:-UseSuperWord):
ArrayFill.fillBytes   8501.270 ±  2.896  ns/op
ArrayFill.fillChars   4286.161 ±  4.935  ns/op
ArrayFill.fillShorts  4286.168 ±  3.146  ns/op
ArrayFill.fillInts    2152.489 ±  2.653  ns/op
ArrayFill.fillFloats  2140.649 ±  2.587  ns/op
ArrayFill.fillLongs   1105.926 ±  2.228  ns/op
ArrayFill.fillDoubles 1105.820 ±  2.393  ns/op
ArrayFill.fillObjects 4392.506 ± 11.678  ns/op

Life is revealed in all it's sucky splendour! This is what happens when the compiler shows you no love... did I say no love? hang on, things can get a bit worse.

Detour: Unsafe? We don't serve you kind here

To all the Unsafe fans, I got some sad news for y'all. Unsafe 'fill' loops are not well loved by the compiler. This is the price of stepping off the beaten path I guess. Consider the following benchmark:
The results are:
ArrayFill.unsafeFillOffheapBytes  9742.621 ±  2.270  ns/op
ArrayFill.unsafeFillOnHeapBytes  12640.019 ±  1.977  ns/op
ArrayFill.fillBytes(for reference) 561.540 ± 10.814 ns/op

The Unsafe variant do not enjoy the 'fill' pattern matching magic, nor do they get the SuperWord optimizations. What can you do? For this kind of thing you should use the Unsafe.setMemory method instead:
With the result:
ArrayFill.unsafeSetOffheapBytes   1259.281 ± 21.294  ns/op
ArrayFill.unsafeSetOnHeapBytes    1275.158 ± 27.950  ns/op
Not quite there, still ~2x worse (why? how come it doesn't just call the bytes fill method? a bit of digging shows it ends up calling the underlying platform's memset...) but beats being 20-25x worse like the handrolled method is.

Summary and Musings

It's the circle of life!
So what did we learn:
  • There's another kind of 'intrinsic' like optimization, which uses pattern matching to swap a block of code rather than a method. This is employed for memset like memory fill loops (in particular Arrays.fill) intrinsicfication. It's not an intrinsic technically, but you know what I fucking mean. 
  • System.arrayCopy/Arrays.fill implementations utilize SIMD instructions to improve their efficiency. These instructions are not available in plain Java, so some compiler intervention is required.
  • The JIT compiler is also able to use SuperWord Level Parallelism to derive SIMD code from 'normal' sequential code.
  • In the case of Arrays.fill, it looks like the SuperWord optimized code is faster than the fill specialized implementation for all types except bytes (on the system under test)
  • If you use Unsafe you will be excluded from these optimizations.
So I look at this process and I imagine history went something like this:
We want to use SIMD instructions, but the JIT compiler isn't really clever enough to generate them by itself. Memset implementations are rather specialized after all. Let's make life a bit easier for the compiler by creating an intrinsic. We'll even go the extra mile and make an effort to automatically identify opportunities to use this intrinsic, so now it's not really an intrinsic any more. The Arrays.fill optimization is available on Oracle JDK6u45 (the oldest I keep around, maybe it was there a while before that) and on that JVM it is twice as fast as the SLP generated code.
Over time, SLP gets better and eventually the compiler is now good enough to optimize the fill loop by itself and beat the specialized method. That is an awesome thing. We just need to remove the training wheels now.
And there's a final punch line to this story. Memset/Memcpy are such common and important opportunities for optimization, so Intel has decided to offer an assembly 'recipe' for them and save everyone the effort in writing them:
3.7.6 Enhanced REP MOVSB and STOSB operation (ERMSB)
Beginning with processors based on Intel microarchitecture code name Ivy Bridge, REP string operation using MOVSB and STOSB can provide both flexible and high-performance REP string operations for soft- ware in common situations like memory copy and set operations. Processors that provide enhanced MOVSB/STOSB operations are enumerated by the CPUID feature flag: CPUID:(EAX=7H, ECX=0H):EBX.ERMSB[bit 9] = 1. - [From the Intel Optimization Manual(September 2014)]
From the manual it seems that this method of implementing memcpy/memset can perform well, but like anything else, YMMV (the intel manual discussion of the performance differences is in itself interesting both on the results and the methodology level). One obvious advantage of this method is that it results in much much smaller code that should be trivial to inline into callers. This will however put the SuperWord method at a slight disadvantage, and the tide will change again.
[UPDATE 14/03/2015: It seems the good folks of Oracle have considered and rejected the use of REP MOVSB for array copy.]
Thanks go to the kind reviewers Peter 'Massive' Hughes, Darrach and the Shipster


  1. Nitsan

    Thanks for the wonderful post... Very timely (I've been looking into SIMD compiler optimizations after I saw Mike Barkers video where he compared C and Java implementations of SimpleBinaryEncoding

    (where @ about 35:15 he noted that the Java JIT compiler (by default) uses SIMD under the covers, whereas C requires you to "opt in" with compiler flags)

    "Java is fast by default"


    1. The JVM is prety cool on that score, yes.
      I'm not sure how well GCC handles vectorization compared to C2, that would make an interesting topic. The thing that bothers people with Java and SIMD is that the compiler can't always figure out what they want to do. In C they could use SIMD intrinsics or assembly, in Java they are stuck.

  2. Great post (again) ! I made benchmarks regarding arrays.fillObject some 2-4 years ago and found the fastest way to null an object array was to system.arraycopy from a static empty object array. Do you know since which release the "pattern driven" optimization is present in JDK ?

    (from fast-serialization):

    public static void clear(Object[] arr, int arrlen) {
    int count = 0;
    final int length = EmptyObjArray.length;
    while (arrlen - count > length) {
    System.arraycopy(EmptyObjArray, 0, arr, count, length);
    count += length;
    System.arraycopy(EmptyObjArray, 0, arr, count, arrlen - count);

    1. The pattern matching replacing thingy is in the JVM since 1.6, but it doesn't cover Object[]/long[]/double[] so really is not a help for what you're trying to do.
      If you want a hacky way of clearing the array using memset you can use Unsafe.setMemory on the array and set the elements to 0 (so something like: UNSAFE.setMemory(arrayB, UNSAFE.arrayBaseOffset(Object[].class), length*UNSAFE.arrayIndexScale(Object[].class), 0);). Since you're nulling it the card marking is irrelevant anyhow (well, unless you use G1GC? or some other GC algo where it matters?). This is a terrible idea so please don't do it.

    2. Thanks for not-advice. Hm .. i also use this technique to clear int arrays, can't remember if I also benchmarked those or just assumed it would also be faster (like with Object arrays). Smells like I can safe zem nanos :-)

  3. Follow up a quick and dirty test measuring performance of clearing int[] and Object[] arrays:

    clear int array using Arrays.fill : 500ms
    clear int array using System.arraycopy of static empty array : 700ms
    clear Object array using Arrays.fill : 3280ms (!!)
    clear int array using System.arraycopy of static empty array : 700ms

    so indeed the trickery does only payoff for object arrays.

    (bench source

    1. ARGG ! fell into autoboxing trap by Arrays.fill( Object[], 0 ).
      Correct number for Object[] clear with Arrays.fill is 1390 !

    2. why, oh why would you not use JMH?

    3. time. just a five minute thingy inline .. results are stable and give a sufficient impression.

    4. Right tool for the job, takes a second to knock up and you get all the tooling for free. Once you get in the habit you'll thank me.

  4. Great reading.

    Do you know if similar SIMD optimizations are implemented for summing arrays?

    for(int i = 0; i<LENGTH; i++) { result[i] = a[i] + b[i]; }

    1. In an ideal world (with AVX2 support) you can have the above transformed into a non-destructive 3 register vectorized instruction. The JIT compiler won't do that for you, but I think it would vectorize it into a wide copy and increment in place.
      The thing to do is write some JMH code around it and look at the generated assembly with perfasm.
      Let me know how it went :-)


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