What is the fastest Garbage Collector in Java 8?

by Geoffrey De Smet - July 31, 2015
Optimization

OpenJDK 8 has several Garbage Collector algorithms, such as Parallel GC, CMS and G1. Which one is the fastest?
What will happen if the default GC changes from Parallel GC in Java 8 to G1 in Java 9 (as currently proposed)?
Let’s benchmark it.

Benchmark methodology

Run the same code 6 times with a different VM argument (-XX:+UseSerialGC, -XX:+UseParallelGC, -XX:+UseConcMarkSweepGC, -XX:ParallelCMSThreads=2, -XX:ParallelCMSThreads=4, -XX:+UseG1GC).
Each run takes about 55 minutes.
Other VM arguments: -Xmx2048M -server
OpenJDK version: 1.8.0_51 (currently the latest version)
Software: Linux version 4.0.4-301.fc22.x86_64
Hardware: Intel® Core™ i7-4790 CPU @ 3.60GHz
Each run solves 13 planning problems with OptaPlanner.
Each planning problem runs for 5 minutes. It starts with a 30 second JVM warm up which is discarded.
Solving a planning problem involves no IO (except a few milliseconds during startup to load the input).
A single CPU is completely saturated.
It constantly creates many short lived objects, and the GC collects them afterwards.
The benchmarks measure the number of scores that can be calculated per millisecond. Higher is better.
Calculating a score for a proposed planning solution is non-trivial:
it involves many calculations, including checking for conflicts between every entity and every other entity.

To reproduce these benchmarks locally, build optaplanner from source
and run the main class
GeneralOptaPlannerBenchmarkApp.

Benchmark results

Executive summary

For your convenience, I’ve compared each Garbage Collector type to the default in Java 8 (Parallel GC).

The results are clear: That default (Parallel GC) is the fastest.

Raw benchmark numbers

Relative benchmark numbers

Garbage Collector	Cloud balancing 200c	Cloud balancing 800c	Machine reassignment B1	Machine reassignment B10	Course scheduling c7	Course scheduling c8	Exam scheduling s2	Exam scheduling s3	Nurse rostering m1	Nurse rostering mh1	Sport scheduling nl14
`-XX:+UseSerialGC`	121211	102072	239278	54637	10821	14370	17095	10130	7389	6667	2234
`-XX:+UseParallelGC` (default Java 8)	126248	107991	282055	59944	10919	14517	17843	10564	7459	6676	2228
`-XX:+UseConcMarkSweepGC`	123150	106889	255775	59087	10142	13180	16346	9903	6738	6018	2142
`-XX:ParallelCMSThreads=2`	128591	106992	279968	59406	10530	13621	16591	10082	7148	6319	2276
`-XX:ParallelCMSThreads=4`	124415	104328	276401	58234	10738	13918	16952	10072	7180	6320	2270
`-XX:+UseG1GC` (default Java 9?)	97146	83952	259981	39522	9803	11965	15195	9410	5961	4985	2062
Dataset scale	120k	1920k	500k	250000k	217k	145k	1705k	1613k	18k	12k	4k

Should Java 9 default to G1?

There is a proposal to make G1 the default Garbage Collector in OpenJDK9 for servers.
My first reaction is to reject this proposal:

G1 is 17.60% is slower on average.
G1 is consistently slower on every use case for every dataset.
On the biggest dataset (Machine Reassignment B10), which dwarfs any of the other datasets in size,
G1 is 34.07% is slower.
If the default GC differs between developer machines and servers, then developer benchmarks become less trustworthy.

On the other hand, there are a few nuances to note:

G1 focuses on limiting GC pauses, instead of throughput.
For these use cases (with heavy calculations) GC pause length mostly doesn’t matter.
This was an (almost) single threaded benchmark.
Further benchmarking with multiple solvers in parallel or multithreaded solving might influence results.
G1 is recommended for a heap size of at least 6 GB.
This benchmark used a heap size of only 2 GB and even that size is only needed for the biggest dataset (Machine Reassignment B10).

Heavy calculations is just one of the many things that OpenJDK is used for: it’s just 1 stakeholder in this community wide debate.
If other stakeholders (such as web services) prove otherwise, maybe it’s worth changing the default GC.
But show me the benchmarks on real projects first!

Conclusion

In Java 8, the default Garbage Collector (Parallel GC) is generally the best choice for OptaPlanner use cases.

Garbage Collector	Average	Cloud balancing 200c	Cloud balancing 800c	Machine reassignment B1	Machine reassignment B10	Course scheduling c7	Course scheduling c8	Exam scheduling s2	Exam scheduling s3	Nurse rostering m1	Nurse rostering mh1	Sport scheduling nl14
`-XX:+UseSerialGC`	-4.05%	-3.99%	-5.48%	-15.17%	-8.85%	-0.90%	-1.01%	-4.19%	-4.11%	-0.94%	-0.13%	+0.27%
`-XX:+UseParallelGC` (default Java 8)	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%
`-XX:+UseConcMarkSweepGC`	-6.23%	-2.45%	-1.02%	-9.32%	-1.43%	-7.12%	-9.21%	-8.39%	-6.26%	-9.67%	-9.86%	-3.86%
`-XX:ParallelCMSThreads=2`	-2.67%	+1.86%	-0.93%	-0.74%	-0.90%	-3.56%	-6.17%	-7.02%	-4.56%	-4.17%	-5.35%	+2.15%
`-XX:ParallelCMSThreads=4`	-2.94%	-1.45%	-3.39%	-2.00%	-2.85%	-1.66%	-4.13%	-4.99%	-4.66%	-3.74%	-5.33%	+1.89%
`-XX:+UseG1GC` (default Java 9?)	-17.60%	-23.05%	-22.26%	-7.83%	-34.07%	-10.22%	-17.58%	-14.84%	-10.92%	-20.08%	-25.33%	-7.45%
Dataset scale		120k	1920k	500k	250000k	217k	145k	1705k	1613k	18k	12k	4k

Author

Geoffrey De Smet
OptaPlanner lead
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