scalar replacement of arrays affected by minor changes to surrounding code

Govind Jajoo gjajoo+java at gmail.com
Tue Sep 17 09:33:16 UTC 2019


dl -

can you please elaborate on how the linked issue is similar?

> limitation of the current EA implementation. Objects will not be
eliminated if there is merge point in which it is undefined which object is
referenced
specifically where is the merge point in the sample code I've posted? from
what i can tell there's no ambiguity around which instance is being
referenced as there's only one.

Thanks,
Govind

On Tue, Sep 17, 2019 at 5:40 AM <dean.long at oracle.com> wrote:

> The problem sounds similar to this issue:
> https://bugs.openjdk.java.net/browse/JDK-6853701
>
> dl
>
> On 9/16/19 3:07 PM, Govind Jajoo 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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