Hi Michael,
On 05/23/2016 03:56 PM, Michael Haupt wrote:
I've ran the unpatched version and Peter's two patches once more. The
results are attached (results.txt). They confirm Aleksey's observation.
Regarding the 03 patch (plevart3 column in the results), perfasm
output (see http://cr.openjdk.java.net/~mhaupt/8031043/perfasm.zip
<http://cr.openjdk.java.net/%7Emhaupt/8031043/perfasm.zip>) suggests
the cost is mainly accrued in ConcurrentHashMap. The same is the case
for the 02 patch (plevart2 column).
As things stand, I think we can even focus on Peter's 02 patch, as
this is the faster of his two proposals (plevart2 column in the
results), reduces the footprint, and reduces the implementation
complexity. Can anything be done to improve on its performance? (It
has slight performance slowdowns for the single-value case as well.)
I can't think of anything else short of improving performance of CHM itself.
Or replacing CHM with a "better" implementation:
http://cr.openjdk.java.net/~plevart/misc/ClassValue.Alternative2/webrev.04/
This webrev is similar to webrev.02. It's only difference is in
ClassValueMap which extends LinearProbeHashtable instead of
ConcurrentHashMap. LinearProbeHashtable is a simple implementation of a
linear-probe hash table. It's not a full Map implementation. It only
implements methods needed in ClassValue. With this implementation I get
a slight boost compared to JDK 9 ClassValue implementation for all sizes
and counts:
Benchmark (classCount) (classValueCount)
(impl) Mode Cnt Score Error Units
ClassValueBench.randomAccess 128 1 jdk9
avgt 10 9.079 ± 0.092 ns/op
ClassValueBench.randomAccess 128 4 jdk9
avgt 10 10.615 ± 0.102 ns/op
ClassValueBench.randomAccess 128 16 jdk9
avgt 10 11.665 ± 0.012 ns/op
ClassValueBench.randomAccess 128 256 jdk9
avgt 10 19.151 ± 0.219 ns/op
ClassValueBench.randomAccess 1024 1 jdk9
avgt 10 14.642 ± 0.425 ns/op
ClassValueBench.randomAccess 1024 4 jdk9
avgt 10 22.577 ± 0.093 ns/op
ClassValueBench.randomAccess 1024 16 jdk9
avgt 10 19.864 ± 0.736 ns/op
ClassValueBench.randomAccess 1024 256 jdk9
avgt 10 60.470 ± 0.285 ns/op
ClassValueBench.sequentialAccess 128 1 jdk9
avgt 10 9.741 ± 0.033 ns/op
ClassValueBench.sequentialAccess 128 4 jdk9
avgt 10 8.252 ± 0.029 ns/op
ClassValueBench.sequentialAccess 128 16 jdk9
avgt 10 7.888 ± 1.249 ns/op
ClassValueBench.sequentialAccess 128 256 jdk9
avgt 10 16.493 ± 0.415 ns/op
ClassValueBench.sequentialAccess 1024 1 jdk9
avgt 10 13.376 ± 0.452 ns/op
ClassValueBench.sequentialAccess 1024 4 jdk9
avgt 10 10.023 ± 0.020 ns/op
ClassValueBench.sequentialAccess 1024 16 jdk9
avgt 10 8.029 ± 0.178 ns/op
ClassValueBench.sequentialAccess 1024 256 jdk9
avgt 10 33.472 ± 0.058 ns/op
Benchmark (classCount) (classValueCount)
(impl) Mode Cnt Score Error Units
ClassValueBench.randomAccess 128 1 pl04
avgt 10 8.955 ± 0.055 ns/op
ClassValueBench.randomAccess 128 4 pl04
avgt 10 9.999 ± 0.017 ns/op
ClassValueBench.randomAccess 128 16 pl04
avgt 10 11.615 ± 1.928 ns/op
ClassValueBench.randomAccess 128 256 pl04
avgt 10 17.063 ± 0.460 ns/op
ClassValueBench.randomAccess 1024 1 pl04
avgt 10 12.553 ± 0.086 ns/op
ClassValueBench.randomAccess 1024 4 pl04
avgt 10 16.766 ± 0.221 ns/op
ClassValueBench.randomAccess 1024 16 pl04
avgt 10 18.496 ± 0.051 ns/op
ClassValueBench.randomAccess 1024 256 pl04
avgt 10 41.390 ± 0.321 ns/op
ClassValueBench.sequentialAccess 128 1 pl04
avgt 10 7.854 ± 0.381 ns/op
ClassValueBench.sequentialAccess 128 4 pl04
avgt 10 7.498 ± 0.055 ns/op
ClassValueBench.sequentialAccess 128 16 pl04
avgt 10 9.218 ± 1.000 ns/op
ClassValueBench.sequentialAccess 128 256 pl04
avgt 10 13.593 ± 0.275 ns/op
ClassValueBench.sequentialAccess 1024 1 pl04
avgt 10 8.774 ± 0.037 ns/op
ClassValueBench.sequentialAccess 1024 4 pl04
avgt 10 8.562 ± 0.014 ns/op
ClassValueBench.sequentialAccess 1024 16 pl04
avgt 10 7.596 ± 0.027 ns/op
ClassValueBench.sequentialAccess 1024 256 pl04
avgt 10 24.605 ± 0.143 ns/op
The footprint is even better than with CHM as LPHT does not maintain
internal Map.Entry(s).
How does this implementation compare on your hardware, Michael?
Regards, Peter
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