scalar replacement of arrays affected by minor changes to surrounding code
Vitaly Davidovich
vitalyd at gmail.com
Wed Sep 18 11:43:30 UTC 2019
EA is pretty flakey, at least in C2. It’s impressive when it works, but
there’re lots of scenarios where it doesn’t, unfortunately. I’m not even
certain how much, if any, effort is directed at improving it given Graal’s
presence. By the way, you should try your benchmark/tests on it and see
how it fares.
That said, have you tried your test with a more canonical forward loop (ie
i = 0; i < arr.length; i++)? It’s a bit silly and might make no difference,
particularly since loop structure is usually canonicalized, but stranger
things have happened.
Another thing worth trying is disabling loop unrolling. I don’t recall the
exact details, but there’s some optimization pass ordering issue between EA
and loop unrolling, which may cause missing scalar replacement.
But beyond the intellectual curiosity angle behind your question, you
really shouldn’t rely on EA to recover performance (or GC pressure) in
critical scenarios. It’s more like a nice bonus optimization: great if it
happens, Ok if not.
On Mon, Sep 16, 2019 at 6:08 PM Govind Jajoo <gjajoo+java at gmail.com> wrote:
> hi Eric,
>
> We're operating well within the default limit of
> -XX:EliminateAllocationArraySizeLimit
> and as shown in the tests, escape analysis is able to identify and elide
> the array allocations for hand-unrolled loops. What we're trying to figure
> out is why a loop or an object wrapper is affecting this optimization?
> We've tried with and without the ... args, but creating a temporary array
> instead and it makes no difference (Examples checked in to the github
> repo).
>
> Are you suggesting that this optimization is not supported in presence of
> loops?
>
> Thanks,
> Govind
>
>
> On Mon, Sep 16, 2019 at 11:40 PM Eric Caspole <eric.caspole at oracle.com>
> wrote:
>
> > Hi Govind,
> > When you use ... to pass parameters and receive the array, the array
> > must be created to pass the parameters, so it is expected to get some
> > allocation and GCs. You can see it in the bytecode for your loopSum:
> >
> > public void loopSum(org.openjdk.jmh.infra.Blackhole);
> > descriptor: (Lorg/openjdk/jmh/infra/Blackhole;)V
> > Code:
> > 0: aload_1
> > 1: iconst_2
> > 2: newarray int
> > 4: dup
> > 5: iconst_0
> > 6: invokestatic #6 // Method next:()I
> > 9: iastore
> > 10: dup
> > 11: iconst_1
> > 12: invokestatic #6 // Method next:()I
> > 15: iastore
> > 16: invokestatic #2 // Method loop:([I)I
> > 19: invokevirtual #7 // Method
> > org/openjdk/jmh/infra/Blackhole.consume:(I)V
> > 22: return
> >
> > If you want to reduce the object allocation maybe you can tweak your
> > code to not pass arguments by ...
> > Regards,
> > Eric
> >
> >
> > On 9/16/19 11:19, Govind Jajoo wrote:
> > > Hi team,
> > >
> > > We're seeing some unexpected behaviour with scalar replacement of
> arrays
> > > getting affected by subtle changes to surrounding code. If a newly
> > created
> > > array is accessed in a loop or wrapped inside another object, the
> > > optimization gets disabled easily. For example when we run the
> following
> > > benchmark in jmh (jdk11/linux)
> > >
> > > public class ArrayLoop {
> > > private static Random s_r = new Random();
> > > private static int next() { return s_r.nextInt() % 1000; }
> > >
> > > private static int loop(int... arr) {
> > > int sum = 0;
> > > for (int i = arr.length - 1; i >= 0; sum += arr[i--]) { ; }
> > > return sum;
> > > }
> > >
> > > @Benchmark
> > > public void loopSum(Blackhole bh) {
> > > bh.consume(loop(next(), next()));
> > > }
> > > }
> > >
> > > # JMH version: 1.21
> > > # VM version: JDK 11.0.4, OpenJDK 64-Bit Server VM, 11.0.4+11
> > > ArrayLoop.loopSum avgt 3
> 26.124
> > ±
> > > 7.727 ns/op
> > > ArrayLoop.loopSum:·gc.alloc.rate avgt 3
> 700.529
> > ±
> > > 208.524 MB/sec
> > > ArrayLoop.loopSum:·gc.count avgt 3
> 5.000
> > > counts
> > >
> > > We see unexpected gc activity. When we avoid the loop by "unrolling" it
> > and
> > > adding the following to the ArrayLoop class above
> > >
> > > // silly manually unrolled loop
> > > private static int unrolled(int... arr) {
> > > int sum = 0;
> > > switch (arr.length) {
> > > default: for (int i = arr.length - 1; i >= 4; sum +=
> > arr[i--])
> > > { ; }
> > > case 4: sum += arr[3];
> > > case 3: sum += arr[2];
> > > case 2: sum += arr[1];
> > > case 1: sum += arr[0];
> > > }
> > > return sum;
> > > }
> > >
> > > @Benchmark
> > > public void unrolledSum(Blackhole bh) {
> > > bh.consume(unrolled(next(), next()));
> > > }
> > >
> > > #
> > > ArrayLoop.unrolledSum avgt 3
> > > 25.076 ± 1.711 ns/op
> > > ArrayLoop.unrolledSum:·gc.alloc.rate avgt 3
> ≈
> > > 10⁻⁴ MB/sec
> > > ArrayLoop.unrolledSum:·gc.count avgt 3
> > ≈
> > > 0 counts
> > >
> > > scalar replacement kicks in as expected. Then to try out a more
> realistic
> > > scenario representing our usage, we added the following wrapper and
> > > benchmarks
> > >
> > > private static class ArrayWrapper {
> > > final int[] arr;
> > > ArrayWrapper(int... many) { arr = many; }
> > > int loopSum() { return loop(arr); }
> > > int unrolledSum() { return unrolled(arr); }
> > > }
> > >
> > > @Benchmark
> > > public void wrappedUnrolledSum(Blackhole bh) {
> > > bh.consume(new ArrayWrapper(next(), next()).unrolledSum());
> > > }
> > >
> > > @Benchmark
> > > public void wrappedLoopSum(Blackhole bh) {
> > > bh.consume(new ArrayWrapper(next(), next()).loopSum());
> > > }
> > >
> > > #
> > > ArrayLoop.wrappedLoopSum avgt 3
> > > 26.190 ± 18.853 ns/op
> > > ArrayLoop.wrappedLoopSum:·gc.alloc.rate avgt 3
> > > 699.433 ± 512.953 MB/sec
> > > ArrayLoop.wrappedLoopSum:·gc.count avgt 3
> > > 6.000 counts
> > > ArrayLoop.wrappedUnrolledSum avgt 3
> > > 25.877 ± 13.348 ns/op
> > > ArrayLoop.wrappedUnrolledSum:·gc.alloc.rate avgt 3
> > > 707.440 ± 360.702 MB/sec
> > > ArrayLoop.wrappedUnrolledSum:·gc.count avgt 3
> > > 6.000 counts
> > >
> > > While the LoopSum behaviour is same as before here, even the
> UnrolledSum
> > > benchmark starts to show gc activity. What gives?
> > >
> > > Thanks,
> > > Govind
> > > PS: MCVE available at https://github.com/gjajoo/EA/
> > >
> >
>
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