anchor.diff:  |Average 11+^.^|*16.1%*
   |===
   
  -Almost every data set shows improvement on Java 11 over Java 8 using the G1 garbage collector. On average, the is a
  -16% improvement just by switching to Java 11. Possible reason for such an improvement can be e.g.
  +Almost every data set improves on Java 11 over Java 8 using the G1 garbage collector. On average, there's a
  +16% improvement just by switching to Java 11. A possible explanation for this improvement could be the
   http://openjdk.java.net/jeps/307[JEP 307: Parallel Full GC for G1], introduced in Java 10.
   
   image::Java8VsJava11usingParallelGC.svg[]
  
positive.code: :awestruct-layout: blogPostBase
  :awestruct-tags: [production, benchmark]
  
  Java 11 was released some time ago, while https://www.baeldung.com/java-in-2018[majority of developers stay on Java 8].
  Let’s see which one of them is faster for https://www.optaplanner.org/[OptaPlanner]. The best way to find out is
  of course running OptaPlanner benchmarks.
  This article is a follow up on https://www.optaplanner.org/blog/2014/03/20/HowMuchFasterIsJava8.html[our similar article for Java 8].
  
  == Benchmark methodology
  
  * A stable machine without any other computational demanding processes running was used. Specifically,
  2 x Intel® Xeon® CPU E5-2609 0 @ 2.4 GHz (8 cores total) with 31.3 GiB RAM memory, running RHEL 6.
  
  * For both Java versions the benchmark was run with both G1 and Parallel GC, so that we can compare also impact
  of garbage collection. Java was executed with following parameters:
  
    “-Xmx1536M -server -XX:+UseG1GC”  and “-Xmx1536M -server -XX:+UseParallelGC”, respectively.
  
  * JVMs used were Oracle Java 8:
  
    java version "1.8.0_191"
    Java(TM) SE Runtime Environment (build 1.8.0_191-b12)
  
negative.code: * Each run solves 11 planning problems with OptaPlanner. Each planning problem runs for 5 minutes and starts with a
  30 second JVM warm up which is discarded.
  
  * The benchmarks measure the number of scores 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.
  
  == Executive summary
  
  With Java 11, the average improvement is *16.1%* for G1 and *4.5%* for Parallel GC. Although Parallel GC is oriented towards
  throughput, whereas G1 focuses rather on low-latency garbage collection, the significant improvement of G1 in
  Java 11 lead to a direct <<table3,comparison of these two garbage collection algorithms>>.
  For more information about difference between various GC algorithms, please see
  https://dzone.com/articles/choosing-the-right-gc[this article].
  
  This shows that Parallel GC is still the preferred GC for OptaPlanner, as throughput is much more important
  for solving optimization problems with OptaPlanner than the latencies introduced by the GC.
  
  == Results
  
  