Sunday, July 7, 2013

Tuning and benchmarking Java 7's Garbage Collectors: Default, CMS and G1

[i am not a native english speaker, so enable quirks mode pls ...]

Due to trouble in a project with long GC pauses, I just had myself a deeper look into GC details. There is not that much acessible information/benchmarks on the Web, so I thought I might share my tests and enlightments ^^. Last time I tested GC some years ago I just came to the conclusion, that allocation of any form is evil in Java and avoided it as far as possible in my code.

I will not describe the exact algorithms here as there is plenty of material regarding this. Understanding the algorithms in detail does not necessary help on predicting actual behaviour in real systems, so I will do some tests and benchmarks.

The concepts of Garbage Collection in Hotspot are explained e.g. here.
In depth coverage of algorithms and parameters can be found here. I will cover GC from an empirical point of view here.

The basic idea of multi generational Garbage Collection is, to collect newer ("younger") Objects more frequent using a "minor" (short duration/pause) Collection algorithm. Objects which survived one or more minor collections then move to the "OldSpace". The OldSpace is garbage collected by the "major" Garbage Collector. I will name them NewSpace and NewGC, OldSpace and OldGC.

The NewGC Algorithm is pretty much the same amongst the 3 Garbage Collectors HotSpot provides. The Old Generation Collector ("OldGC") makes the difference.

  • In the Default Collector, OldSpace is cleaned up using a "Stop the World" Mark-Sweep.
  • The Concurrent Mark&Sweep Collector (CMS) uses (surprise!) a concurrent implementation of the Mark & Sweep Collection. This means the running application is not stopped during major GC. However it falls back to Stop-The-World GC if it can't keep up with applications allocation (promotion, tenuring) rate.
  • The G1 is also concurrent. It segments memory into equal chunks, trying to split the Garbage Collection Process into smaller pieces by collecting segments which are likely to contain a lot of garbage first. A more detailed description can be found here. It also falls back to Full-Stop-GC  in case it can't keep up with application allocation rate. However the duration of those Full-GC pauses are of shorter duration compared to the other OldSpace collectors.

Note that the term "parallel GC" refers to collection algorithms which run multithreaded, not necessary in parallel to your application.

The Benchmark

I wrote a small program which emulates most of the stuff Garbage Collectors have problems with:

  • 4GB of statically allocated Objects which are rarely freed.
    Problem for GC: with each major GC objects must be traversed, so the larger your reference data, cache's etc., the longer major GC will need for a full traversal collection.
  • A lot of temporary Object allocation of various size and age. "Age" refers to the amount of time the Object is referenced by the application.
  • Intentional partial replacements of pseudo-static data by new objects. This way I enforce "promotion" of objects to OldSpace, as they are long lived.
This is achieved by putting a lot of objects into a HashMap, then replace a fraction of it. Additionally the latency of a ~0,5 ms operation is measured and memorized to simulate processing of e.g. Requests. This way I get a distribution of VM/GC-related pauses. The benchmark runs for 5 minutes, so long term effects like heap fragmentation are not covered by the tests.

Note that this benchmark has an insane allocation rate and object age distribution. So results and VM tuning evaluated in this post illustrate the effects of some GC settings, its not cut & paste stuff, most of the sizings used to get this allocation greedy benchmark to work are way to big for real world applications.

Real men use default settings ..

Running the benchmark with -Xms8g -Xmx8g (the bench consumes 4g static, ~1g dynamic data) yields to quite disgusting results:

Each of the spikes represents a full GC with a duration of ~15 seconds ! During the full GC, the program is stopped. One can see, that the Garbage Collecor has some automatic calibration. Initially it did a pretty good job, but after first GC things get really bad.
I've noticed this in several tests. Most of the time the automatic calibration improves things, but sometimes it's vice versa (see Enlightment #2).

Hiccups ( [interval in milliseconds] => number of occurences ).
Iterations 9324, -Xmx8g -Xms8g 
[0] 4930
[1] 4366
[2] 8
[7] 1
[14000] 1
[15000] 10
[16000] 4
[17000] 3
[18000] 1
Read like e.g '4930' requests done in 0 ms, 10 requests had a response time of 15 seconds. Throughput was 9324 requests.

CMS did somewhat better, however there were also severe program-stopping GC's. Note the >10 times higher throughput.

Iterations 115726, ‐XX:+UseConcMarkSweepGC -Xmx8g -Xms8g
[0] 52348
[1] 62317
[2] 157
[46] 7
[61] 13
[62] 127
[63] 234
[64] 164
[65] 81
[66] 55
[67] 45
[68] 42
[69] 25
[70] 28
[71] 16
[72] 11
[73] 5
[74] 12
[14000] 7
[15000] 4
[17000] 1

G1 does the best job here (still 10 inacceptable full stop GC's..)
Iterations 110052
[0] 37681
[1] 67201
[2] 1981
[3] 725
[4] 465
[5] 306
[6] 217
[7] 182
[8] 113
[9] 105
[39] 13 
(skipped some intervals of minor occurence count)
[100] 288
[200] 23
[300] 6
[1000] 11
[12000] 5
[13000] 5
Hm .. pretty bad. Now, let's have a look into the GC internals:

If OldSpace (big green one) is full, the application gets a full-stop GC. In order to avoid Full GC, we need to reduce the promotion rate to OldSpace. An object gets promoted if it is alive (aka referenced) for a longer time than NewSpace (=Eden+Survivor Spaces) holds it.

So time for 

Finding #1: 
It is key to reduce the promotion rate from young gen to OldSpace. The promotion rate to OldSpace needs to be lowered in order to avoid Full-GC's. In case of concurrent OldSpace GC (CMS,G1), this will enable collectors to keep up with allocation rate.

Tuning the Young Generation

All 3 Collectors avaiable in Java 7 will profit of a proper NewGC setup.

Promotion happens if:
  • The "survivor space" (S0, S1) is full.
    The size of Survivor vs Eden is specified with the -XX:SurvivorRatio=N. A large Eden will increase throughput (usually) and will catch ultra-short lived temporary Objects better. However large Eden means small Survivor Spaces, so middle aged Objects might get promoted too quickly to OldSpace then, putting load on OldSpace GC.
  • Survivors have survived more than -XX:MaxTenuringThreshold=N (Default 15) minor collections. Unfortunately I did not find an option to give a lower bound for this value, so one can specify a maximum here only. -XX:InitialTenuringThreshold might help, however I found the VM will choose lower values anyway in case.

The following actions will reduce promotion (by encouraging survivors to live longer in young generation)
  • Decreasing -XX:SurvivorRatio=N to lower values than 8 (this actually increases the size of survivor spaces and decreases Eden size).
    Effect is that survivors will stay for a longer time in young gen (if there is sufficient size)
    This will reduce throughput as survivors are copied with each minor GC between S0,S1.
  • Increase the overall size of young generation with -XX:NewRatio=N.
    "1" means, young generation will use 50% of your heap, 2 means it will use 33% etc. A larger young gen reduces heap size for long-lived objects but will reduce the number of minor GCs and increase the size avaiable for survivors.
  • Increase -XX:MaxTenuringThreshold=N to values > 15.
    Of course this only reduces promotion, if the survivor space is large enough. Additonally this is only an upper bound, so the VM might choose a lower value regardless of your setting (you can also try 
  • Increasing overall VM heap will help (in fact more GB always help :-) ), as this will  increase young generation (Eden+Survivor) and OldSpace size. An increase in OldSpace size reduces the number of required major GC's (or give concurrent OldSpace collectors more headroom to complete a major GC concurrent, pauseless).

It depends on the allocation behaviour of your application which of this actions will have effect.

Finding #2: 
When adjusting Survivor Ratio and/or MaxTenuringThreshold manually, always switch off auto adjustment with

Below the effects of NewSpace settings on memory sizing. Note that the sizing of NewSpace directly reduces avaiable space in OldSpace  So if your application holds a lot of statically allocated data, increasing size of young generation might require you to increase overall memory size.

Note: In practice one would evaluate the required size of NewSpace and specify it with -XX:NewSize=X -XX:MaxNewSize=X absolutelyThis way changing -Xmx will affect OldSpace size only and will not mess up absolute Survivor Space sizes by applying ratios.

Actually the Default GC is the most useful collector to use in order to profile NewSpace setup, since there is no 2cnd background collector bluring OldSpace growth.

Survivor Sizing

The most important thing is, to figure out a good sizing (absolute, not ratio) for the survivor spaces and the promotion counter (MaxTenuringThreshold). Unfortunately there is a strong interaction between Eden size and MaxTenuringThreshold: If Eden is small, then Objects are put into survivor spaces faster, additional the tenuring counter is incremented more quickly. This means if Eden is doubled in size, you probably want to decrease your MaxTenuringThreshold and vice versa. This gets even more complicated as the number of Eden GC (=minor GC) also depends on application allocation rate.

The optimal survivor size is large enough to hold middle lived Objects under full application load without promoting them due to size shortage.

  • If survivor space is too large, its just a waste of memory which could be given to Eden instead. Additionally there is a correlation between survivor size and minor GC pauses (throughput degradation, jitter) [Fixme: to be proven].
  • If survivor space is too small, Objects will be promoted to OldSpace too early even if TenuringThreshold is not reached yet.


This defines how many minor GC's an Object may survive in SurvivorSpace before getting tenured to OldSpace. Again you have to optimize this under max application load, as without load there are fewer minor GC's so the "survived"-counters will be lower. Another issue to think of is that Eden size also affects the frequency of minor GC's. The VM will handle your value as an upper bound only and will automatically use lower values if it thinks these are more appropriate.

  • If MaxTenuringThreshold is too high, throughput might suffer, as non-temporary Objects will be subject to minor collection which slows down application. As said, the VM automatically corrects that.
  • If MaxTenuringThreshold is too low, temporary Objects might get promoted to OldSpace too early. This can hamper the OldSpace collector and increase the number of OldSpace GC's. If the promotion rate gets too high, even concurrent Collecors (CMS, G1) will do a full-stop-GC.

If in doubt, set MaxTenuringThreshold too high, this won't have a significant impact on application performance in practice.
It also strongly depends on the coding quality of the application: if you are the kind of guy preferring zero allocation programming, even a MaxTenuringThreshold=0 might be adequate (there is also kind of "alwaysTenure" option). The other extreme is "return new HashBuilder().computeHash(this);"-style (some alternative VM language produce lots of short to mid-lived temporary Garbage) where a settings like '30' or higher (which most often means: keep survivors as long there is room in SurvivorSpace) might be required.

Common sign of too slow promotion (MaxTenuringThreshold too high, Survivor size too high):
(-Xmx12g -Xms12g -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=3 -XX:NewRatio=2 -XX:MaxTenuringThreshold=20)

bad survivor, TenuringThreshold setting

Initially it looks like there are no Objects promoted to OldSpace, as they actually "sit" in Survivor Space. Once the survivor spaces get filled, survivors are tenured to old Space resulting in a sudden increase of promotion rate. (5 minute chart of benchmark running with default GC, actually the application does not allocate more memory, it just constantly replaces small fractions of initially allocated pseudo-static Objects). This will probably confuse concurrent OldSpace Collectors, as they will start concurrent collection too late. Beware: Clever project manager's might bug you to look for memory leaks in your application or to plow through the logs to find out "what happened 14:36 when memory consumption all over a sudden starts to rise".

Same benchmark with better settings (-Xmx12g -Xms12g -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=4 -XX:NewSize=4g -XX:MaxNewSize=4g -XX:MaxTenuringThreshold=15):

better survivor, TenuringThreshold setting

The promotion rate now reflects the actual allocation rate of long-lived objects. Since there is no concurrent OldSpace Collector in the default GC, it looks like a permanent growth.  Once the limit is reached, a Full-GC will be triggered and will clean up unused long lived Objects. A concurrent collector like CMS, G1 will now be able to detect promotion rate and keep up with it.

Eden Size

Eden Size directly correlates with throughput as most java applications will create a lot of temporary objects.

as measured with high allocation rate application

Eden size: red: 642MB, blue: 1,88GB, yellow: 3,13GB, green: 4,39GB

As one can see, Eden size correlates with number, not the length of minor collections.

Settings used for benchmark (Survivor spaces are kept constant, only Eden is enlarged):

-XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=1 -XX:NewSize=1926m -XX:MaxNewSize=1926m -XX:MaxTenuringThreshold=40
= 642m Eden

-XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=3 -XX:NewSize=3210m -XX:MaxNewSize=3210m -XX:MaxTenuringThreshold=15
= 1,88g Eden

-XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=5 -XX:NewSize=4494m -XX:MaxNewSize=4494m -XX:MaxTenuringThreshold=12
= 3,13g Eden

-XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=7 -XX:NewSize=5778m -XX:MaxNewSize=5778m -XX:MaxTenuringThreshold=8
= 4,39g Eden (heap size 14Gb required)

Finding #3:
  • Eden size strongly correlates with throughput/performance for common java applications (with common allocation patterns). The difference can be massive.
  • Eden size, Allocation rate, Survivor size and TenuringThreshold are interdependent. If one of those factors is modified, the others need readjustment
  • Ratio-based options can be dangerous and lead to false assumptions. NewSpace size should be specified using absolute settings (-XX:NewSize=)
  • Wrong sizing can lead to strange allocation and memory consumption patterns 

Tuning OldSpace Garbage Collectors

Default GC

Default GC has a Full-Stop-GC (>15 seconds with the benchmark), so there is not much to do. If you want to run your application with Default GC (e.g. because of high throughput requirements), your only choice is to tune NewSpace GC very agressively, then throw tons of Heap to your application in order to avoid Full-GC during the day. If your system is 24x7 consider triggering a full GC using System.gc() at night if you expect the system load to be low.
Another possibility would be to even size the VM bigger than your physical RAM, so tenured Objects are written to swap disk. However you have to be sure then no Full-GC is triggered ever, because duration of Full-GC will go into the minutes then. I have not tried this.
Ofc one can improve things always by coding less memory intensive, however this is not the topic of this post.

Concurrent Mark & Sweep (CMS)

The CMS Collector does a pretty good job as long your promotion rate is not too high (which should not be the case if you optimized that as described above).
One Key setting of CMS Collector is, when to trigger a concurrent full GC. If it is triggered too late, it might not be able to finish in time and a Full-Stop-GC will happen. If you know your application has like 30% statically allocated data you might want to set this to 30% like -XX:+UseCMSInitiatingOccupancyOnly
-XX:CMSInitiatingOccupancyFraction=30. In practice I always start with a value of 0, then experiment with higher values once everything (NewSpace, OldSpace) is calibrated to operate without triggering FullGC under load.

When i copy the settings evaluated in the "NewGen Tuning" settings straight forward, the result will be a permanent Full-GC. Why ?
Because CMS requires more heap than the default GC. It seems like the same data structures just require ~20-30% more memory. So we just have to multiply NewGC settings evaluated from Default GC with 1.3.
Additionally, a good start is to let the concurrent Mark & Sweep run all the time.

So I go with (copied 2cnd NewSpace config from above and multiplied):

‐XX:+UseConcMarkSweepGC -XX:+UseCMSInitiatingOccupancyOnly
-XX:CMSInitiatingOccupancyFraction=10 -Xmx12g -Xms12g -XX:-UseAdaptiveSizePolicy
-XX:SurvivorRatio=3 -XX:NewSize=4173m -XX:MaxNewSize=4173m

The CMS is barely able to keep up with promotion rate, throughput is 300.000 which is acceptable given that cost of (in contradiction to tests above) Full-GC is included.

CMS OldSpace collection can keep up with promotion rate barely

We can see from Visual GC (an excellent jVisualVM plugin), that OldSpace size is on the edge of triggering a full GC. In order to improve throughput, we would like to increase eden. Unfortunately there is not enough  headroom in OldSpace, as a reduction of OldSpace would trigger Full-Stop-GC's.
Reducing the size of Surviver Spaces is not an option, as this would result in a higher tenured Object rate and again trigger Full GC's. The only solution is: More Memory.
Comparing throughput with the Default GC test above is not fair, as the Default GC would run into Full-Stop-GC's for sure, if the test would run for a longer time than 5 minutes.
On a side note: the memory chart of jVisual VM's (and printouts by -verbose:gc) does not tell you the full story as you cannot see the fraction of used memory in OldSpace.

Ok, so lets add 2 more Gb to be able to increase eden resulting in

-XX:+UseConcMarkSweepGC -XX:+UseCMSInitiatingOccupancyOnly
-XX:CMSInitiatingOccupancyFraction=10 -Xmx14g -Xms14g -XX:-UseAdaptiveSizePolicy
-XX:SurvivorRatio=4 -XX:NewSize=5004m -XX:MaxNewSize=5004m

(Eden size is 3,25 GB).
This increases throughput by 10% to 330.000.

blue is Heap 12g, Eden 2,4g, red  is Heap 14g, Eden 3,2g

The longest pause noted in processing were 400ms. Exact Reponse time distribution:

-XX:+UseCMSInitiatingOccupancyOnly -XX:CMSInitiatingOccupancyFraction=10
-Xmx14g -Xms14g
-XX:SurvivorRatio=4 -XX:NewSize=5004m -XX:MaxNewSize=5004m

Iterations 327175

[0] 146321
[1] 179968
[2] 432
[3] 33
[14] 1
[70] 2
[82] 1
[83] 1
[85] 2
[87] 1
[100] 3
[200] 355
[300] 44
[400] 11

Finding #4:
  • Eden size still correlates with throughput for CMS
  • Compared with the Default Hotspot GC all NewGen sizes must be multiplied by 1.3
  • CMS also requires 20%-30% more Heap for identical data structures
  • No Full-Stop-GCs at all if you can provide the necessary amount of memory (large NewSpace + HeadRoom in OldSpace)

The G1 Collector

The G1 collector is different. Each segment of G1 has its own NewSpace, so absolute values set with -XX:NewSize=5004m -XX:MaxNewSize=5004m are actually ignored (don't ask me how long i fiddled with G1 and the NewSize parameters until I got this). Additionally VisualGC does not work for G1 Collector.
Anyway, we still know that the benchmark succeeds in being one of the most greedy java programs out there, so ..
  • we like large survivor spaces and a large NewSpace to reduce promotion to OldSpace
  • we like large Eden to trade memory efficiency against throughput. Memory is cheap.
Unfortunately not much is known about the efficiency of the G1 OldSpace collector, so the only possibility is testing. If the G1 OldSpace collector is more efficient than CMS OldSpace collector, we could decrease survivor space in favour of bigger eden spaces, because with a potentially more efficient G1 OldSpace collector, we may afford a higher promotion rate.

Here we go:

-Xmx12g -Xms12g -XX:+UseG1GC -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=1 -XX:NewRatio=1 -XX:MaxTenuringThreshold=15 
Iterations 249372
(values < 100ms skipped)
[100] 10
[200] 1
[400] 4
[500] 198
[600] 15
[700] 1

well, that's pretty good as first effort. Lets try to increase decrease survivors and start concurrent G1 earlier ..

-Xmx12g -Xms12g -XX:+UseG1GC -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=2 -XX:NewRatio=1 -XX:MaxTenuringThreshold=15 -XX:InitiatingHeapOccupancyPercent=0

Iterations 233262
[100] 3
[200] 1
[400] 81
[500] 96
[600] 3
[700] 5
[800] 2
[1000] 18

err .. nope. We can see that decreasing survivor space puts more load on G1 OldSpace GC as the number of 1 second area pauses increased significantly. Just another blind shot relaxing survivors and increase G1 OldSpace load ..

-Xmx12g -Xms12g -XX:+UseG1GC -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=6 -XX:NewRatio=1 -XX:MaxTenuringThreshold=15 -XX:InitiatingHeapOccupancyPercent=0

Iterations 253293
[100] 5
[200] 1
[400] 186
[500] 2
[600] 9
[700] 5
[1000] 16

same throughput as intial trial, but larger pauses .. so I still need to look for large survivor and eden space. The only way to influence absolute NewSpace size is changing segment size (I take max size ofc).

-Xmx12g -Xms12g -XX:+UseG1GC -XX:-UseAdaptiveSizePolicy -XX:SurvivorRatio=1 -XX:NewRatio=1 -XX:MaxTenuringThreshold=15 -XX:G1HeapRegionSize=32m

Iterations 275464
[100] 37
[300] 1
[400] 5
[500] 189
[600] 16
[900] 1

Bingo ! Unfortunately all known tuning options are exploited now .. except main Heap size ..

-Xmx14g -Xms14g -XX:+UseG1GC -XX:SurvivorRatio=1 -XX:NewRatio=1 -XX:MaxTenuringThreshold=15 -XX:-UseAdaptiveSizePolicy -XX:G1HeapRegionSize=32m

Iterations 296270
[100] 58
[200] 3
[300] 2
[400] 4
[500] 160
[600] 15
[700] 11

This is slightly worse in latency and througput than a proper configured CMS. One last shot in order to figure out behaviour regarding memory size constraints (at least real peak size of the bench is round 4.5g ... sigh):

-Xmx8g -Xms8g -XX:+UseG1GC -XX:SurvivorRatio=1 -XX:NewRatio=2 -XX:MaxTenuringThreshold=15 -XX:-UseAdaptiveSizePolicy -XX:G1HeapRegionSize=32m

Iterations 167721
[100] 37
[200] 1
[300] 3
[400] 297
[500] 7
[600] 9
[700] 5
[1000] 15
[2000] 1

It seems G1 excels in memory efficiency without having to do too long pauses (CMS falls into permanent full GC if I try to run the bench with 8g heap). This can be important in memory constrained server environments and at client side. 

Finding #5:
  • Absolute settings for NewSpace size are ignored by G1. Only the ratio based options are acknowledged.
  • In order to influence eden/survivor space, one can increase/decreas segment size up to 32m. This also gave best throughput (possibly because of larger eden)
  • G1 can handle memory constraints the best without extremely long pauses
  • Latency distribution is ok, but behind CMS



  • The benchmark used is worst case ever regarding allocation rate and memory waste. So take any finding with a grain of salt, when applying GC optimizations to your program.
  • I did not drive long term tests. All tests ran for 5 minutes only. Due to the extreme allocation rate of the benchmark, 5 minute benchmark is likely aequivalent to an hour operation of a "real" program. Anyway in an application with lower allocation rate, concurrent collectors will have more time to complete GC's concurrent, so you probably never will need an eden size of 4Gb in practice :).
    I will provide long term runs in a separate post (maybe :) ).

Default GC (Serial Mark&Sweep, Serial NewGC) shows highest throughput as long no Full GC is triggered.
If your system has to run for a limited amount of time (like 12 hours) and you are willing to invest into a very careful programming style regarding allocation; keep large datasets Off-Heap, DefaultGC can be the best choice. Of course there are applications which are ok with some long GC pauses here and there.

CMS does best in pause-free low latency operation as long you are willing to throw memory at it. Throughput is pretty good. Unfortunately it does not compact the heap, so fragmentation can be an issue over time. This is not covered here as it would require to run real application tests with many different Object sizes for several days. CMS is still way behind commercial low-latency solutions such as Azul's Zing VM.

G1 excels in robustness, memory efficiency with acceptable throughput. While CMS and DefaultGC react to OldSpace overflow with Full-Stop-GC of several seconds up to minutes (depends on Heap size and Object graph complexity), G1 is more robust in handling those situations. Taking into account the benchmark represents a worst case scenario in allocation rate and programming style, the results are encouraging.

Blue:CMS, Red: Default GC, Yellow: G1

Note: G1 has the lowest number of pauses. (Default GC has been tweaked to not do >15 second Full GC during test duration)
Default GC has been tweaked to not Full GC during test duration

The Benchmark source

public class FSTGCMark {

    static class UseLessWrapper {
        Object wrapped;
        UseLessWrapper(Object wrapped) {
            this.wrapped = wrapped;

    static HashMap map = new HashMap();
    static int hmFillRange = 1000000 * 30; //
    static int mutatingRange = 2000000; //
    static int operationStep = 1000;

    int operCount;
    int milliDelayCount[] = new int[100];
    int hundredMilliDelayCount[] = new int[100];
    int secondDelayCount[] = new int[100];
    Random rand = new Random(1000);

    int stepCount = 0;
    public void operateStep() {

        if ( stepCount%100 == 0 ) {
            // enforce some tenuring
            for ( int i = 0; i < operationStep; i++) {
                int key = (int) (rand.nextDouble() * mutatingRange)+mutatingRange;
                map.put(key,  new UseLessWrapper(new UseLessWrapper(""+stepCount)));
        if ( stepCount%200 == 199 ) {
            // enforce some tenuring
            for ( int i = 0; i < operationStep; i++) {
                int key = (int) (rand.nextDouble() * mutatingRange)+mutatingRange*2;
                map.put(key,  new UseLessWrapper(new UseLessWrapper("a"+stepCount)));
        if ( stepCount%400 == 299 ) {
            // enforce some tenuring
            for ( int i = 0; i < operationStep; i++) {
                int key = (int) (rand.nextDouble() * mutatingRange)+mutatingRange*3;
                map.put(key,  new UseLessWrapper(new UseLessWrapper("a"+stepCount)));
        if ( stepCount%1000 == 999 ) {
            // enforce some tenuring
            for ( int i = 0; i < operationStep; i++) {
                int key = (int) (rand.nextDouble() * hmFillRange);
                map.put(key,  new UseLessWrapper(new UseLessWrapper("a"+stepCount)));
        for ( int i = 0; i < operationStep/2; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange);
            map.put(key, new UseLessWrapper(new Dimension(key,key)));
        for ( int i = 0; i < operationStep/8; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange);
            map.put(key, new UseLessWrapper(new UseLessWrapper(new UseLessWrapper(new UseLessWrapper(new UseLessWrapper("pok"+i))))));
        for ( int i = 0; i < operationStep/16; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange);
            map.put(key, new UseLessWrapper(new int[50]));
        for ( int i = 0; i < operationStep/32; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange);
            map.put(key, ""+new UseLessWrapper(new int[100]));
        for ( int i = 0; i < operationStep/32; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange);
            Object[] wrapped = new Object[100];
            for (int j = 0; j < wrapped.length; j++) {
                wrapped[j] = ""+j;
            map.put(key, new UseLessWrapper(wrapped));
        for ( int i = 0; i < operationStep/64; i++) {
            int key = (int) (rand.nextDouble() * mutatingRange /64);
            map.put(key, new UseLessWrapper(new int[1000]));
        for ( int i = 0; i < 4; i++) {
            int key = (int) (rand.nextDouble() * 16);
            map.put(key, new UseLessWrapper(new byte[1000000]));

    public void fillMap() {
        for ( int i = 0; i < hmFillRange; i++) {
            map.put(i, new UseLessWrapper(new UseLessWrapper(""+i)));

    public void run() {
        System.out.println("static alloc " + (Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory()) / 1000 / 1000 + "mb");
        long time = System.currentTimeMillis();
        int count = 0;
        while ( (System.currentTimeMillis()-time) < runtime) {
            long tim = System.currentTimeMillis();
            int dur = (int) (System.currentTimeMillis()-tim);
            if ( dur < 100 )
            else if ( dur < 10*100 )
            else {
        System.out.println("Iterations "+count);

    public void dumpResult() {
        for (int i = 0; i < milliDelayCount.length; i++) {
            int i1 = milliDelayCount[i];
            if ( i1 > 0 ) {
        for (int i = 0; i < hundredMilliDelayCount.length; i++) {
            int i1 = hundredMilliDelayCount[i];
            if ( i1 > 0 ) {
        for (int i = 0; i < secondDelayCount.length; i++) {
            int i1 = secondDelayCount[i];
            if ( i1 > 0 ) {
    int runtime = 60000 * 5;
    public static void main( String arg[] ) {
        FSTGCMark fstgcMark = new FSTGCMark();;


  1. Great post! Would be interesting to see what Censum makes of your GC logs and how close it gets to optimum config. Also, would love to see how this works on Zing, maybe Gil Tene would be interested?

    1. I am eager to try out Zing ofc .. looking forward to do that past my vacation :-)

  2. Great to see more research done in this area! I'll forward this to our Friends of jClarity list as we seem to have become the watering hole that GC experts and enthusiasts hang out in. If you're after an free eval copy of Censum to look at you results please let me know!

    1. That would be interesting, at least manual experimentation can easily lead to a local maximum ;-). I'll happily try around next week when I am back from vacation.

  3. VisualGC DOES work with G1 (I've used it plenty. Be sure to use the visualvm that comes with java 7 or download the latest jvisualvm directly) and G1 DOES honor Xmn and new size settings, but setting them prevents G1 from growing the new size and adapting itself properly.

    I would highly recommend using the G1 collector in the preview release of 1.7u40 - it has many more tuning options available in it (some of them are experimental opts) and the defaults are also better.

    I would also recommend turning on -XX:+PrintGCDetails with G1. After some experience, it becomes VERY easy to see what might be causing G1 to be pausing longer than you want.

    in practice, CMS is a pain in the butt to tune and you can only really tune it for one object allocation rate. g1 is very adaptive and can handle different allocation rates with ease.

    I've gotten 72 - 92 GB cache storage nodes to have a GC throughout of 99.7% with a worst-case max pause time of 230ms and an average pause time of 100ms (my target pause time) - and this is after running it for weeks at a high load (8k requests per second were hitting it)

    With cms you always run the risk of the concurrent mode failure longer GC pause because it slowly fragments it's old gen.

    1. I think i updated the JDK in-place, which seems to keep jVisualVM plugins of the previous version, so i probably had an old visual GC plugin within an up-to-date jVisuaVM. Thanks for the hint.
      Well, G1 is evolving .. anyway if I tune CMS for worst-case load (which is like 100.000 requests/messages per second), it seems to be the best non-commercial option at this time.
      I will revisit the test with a slightly modified program (maybe also incl. Zing).

      Regarding "adaptive behaviour" I am pretty pessimistic as all collectors showed "de-optimizations" in some scenarios while trying to "adapt". This is ok for most applications, but not good enough when it comes to guaranteed SLA's. The Collectors cannot and will never be able to predict future behaviour of a program reliably.

  4. I ran your test on Zing, and got the following out of the box (note that I just slapped on 30GB to avoid tuning, and that this is a shared dev system that exhibits normal system noise in the 20-40msec level):

    dev1-94% ../jHiccup/jHiccup -d 2000 -i 1000 ${JAVA_HOME}/bin/java -Xmx30g FSTMark
    static alloc 6884mb
    Iterations 129334
    [1] 3205
    [2] 87540
    [3] 35298
    [4] 2752
    [5] 332
    [6] 84
    [7] 41
    [8] 21
    [9] 11
    [10] 9
    [11] 7
    [12] 9
    [13] 4
    [14] 4
    [15] 5
    [16] 1
    [18] 1
    [20] 1
    [21] 1
    [22] 1
    [32] 1
    [100] 3
    [200] 1
    [300] 1
    [400] 1

    While these seem great compared to the others reported above (especially for no tuning), My mind goes immediately to "where are those 100-400 numbers coming from?" Checking my jHiccup logs collected during the run I see no more than 25msec noise... I'm thinking that the test itself may have some inherent multi-100msec operations in it (map resizing perhaps?). To avoid dealing with warmup and structure-resizing effects, this sort of test should probably call the run() method at least twice, letting the first run warm up and measure only on subsequent runs...

    1. These numbers look excellent latency wise, I would like to get an overall comparision chart. However I need to repeat an improved version of the test on equal hardware and OS. Your are right in suspecting test may have some inherent long operations. I started off fooling around and later on added the latency measure :-)
      I will revisit an updated test with updated JDK (G1 has new options) and Zing as soon as possible. The throughput looks somewhat inbetween, however my test machine (Gaming PC, gaming memory, etc.) has very high single thread performance so this might be the reason.

      I need a more systematic test, which creates adjustable percentage of objects of different ages, which could be easily created by adjusting the range of random numbers which are replaced, the smaller the random range, the higher the probability of object replacement (=short lived).
      Additionally there is few inter linkage inbetween the objects (flat object graph). This might favour G1. I have made tests which indicate, that G1 does not like highly linked object graphs with random referenced objects (probably due to large remembered sets of segments ?).

    2. AS you say, I wouldn't read much into the throughput number for the result I posted. It's a shared dev machine with 32 cores on an old AMD 6274 running at 1.4GHz. Throughout comparison should be done on the same system.

      BTW, you may want to use HdrHistogram ( to track your latencies and then print them at them at the end. This is exactly the use case it was built for, and It would give you a detailed, high resolution percentile distribution thing that is much finer than the coarse buckets you currently have.

    3. I checked back: you are right there are actually exactly those 6 spikes in processing (due to my '%' triggered extra processing). While this bugs me somewhat, results are still valid, as the other collectors all had >100 events in those buckets. Zing is the only VM where this is significant as it does make up 100% of all >100 ms spikes, while with other collecors it's < 1% ;-)

      I actually have HdrHistrogram installed, but i'd like to keep copy&paste ability for the bench. Also HdrHistogram is not that handy under windows .. (times have changed, i use Linux@work and windows@home, some years ago it was vice versa ..)

      btw: i also have a dual socket opteron 6274 2.4ghz hanging around here (out of curiousity). Single core performance is somewhat underwhelming, but good to test multithreaded code .. the tests were done on a i7 box.

  5. Hi, I have several questions and hope you can answer me them :)
    can you please explain why you use this kind of nesting in [1]. Why put a UseLessWrapper in another UseLessWrapper. To create referenced objects and therefor to tenure them?

    Why did you choose a String to put and not any other object type in the tenuring phase?

    In the tenuring phase you put in different ranges (e.g. [2M - 4M], [4M - 6M], [6M - 8M]) excatly the same object but with a different density. For example you put every 400th operation step an object in the key range of 6000000 - 8000000. Could you explain what the purpose of this density seperation is?

    Did you have something special in mind while choosing Dimension type to put in the hashmap?

    [1] - UseLessWrapper(new UseLessWrapper(""+stepCount)))

    1. I want to measure impact of the number of objects vs impact of "References" to Objects. So by wrapping two objects or creating random graphs of interconnected objects i can keep the number of objects constant but create different kind + amount of "inter-object-linkage".
      The Dimension Objects are just used as a Dummy for objects not containing any pointers (Dimension has only width and height primitives, no pointer to other objects).

    2. Oops, my reply relates to For the Benchmark I just wanted to simulate some connected object graph.

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