Which algorithm is used by garbage collector

Phase 3. Concurrent Mark. This phase is very much similar to that of CMS: it simply walks the object graph and marks the visited objects in a special bitmap. To ensure that the semantics of snapshot-at-the beginning are met, G1 GC requires that all the concurrent updates to the object graph made by the application threads leave the previous reference known for marking purposes. This is achieved by the use of the Pre-Write barriers not to be confused with Post-Write barriers discussed later and memory barriers that relate to multithreaded programming.

Their function is to, whenever you write to a field while G1 Concurrent Marking is active, store the previous referee in the so-called log buffers, to be processed by the concurrent marking threads. Phase 4. This is a stop-the-world pause that, like previously seen in CMS, completes the marking process. For G1, it briefly stops the application threads to stop the inflow of the concurrent update logs and processes the little amount of them that is left over, and marks whatever still-unmarked objects that were live when the concurrent marking cycle was initiated.

This phase also performs some additional cleaning, e. Phase 5. This final phase prepares the ground for the upcoming evacuation phase, counting all the live objects in the heap regions, and sorting these regions by expected GC efficiency.

It also performs all the house-keeping activities required to maintain the internal state for the next iteration of concurrent marking. Last but not least, the regions that contain no live objects at all are reclaimed in this phase. Some parts of this phase are concurrent, such as the empty region reclamation and most of the liveness calculation, but it also requires a short stop-the-world pause to finalize the picture while the application threads are not interfering.

The logs for such stop-the-world pauses would be similar to:. In case when some heap regions that only contain garbage were discovered, the pause format can look a bit different, similar to:. After Concurrent Marking has successfully completed, G1 will schedule a mixed collection that will not only get the garbage away from the young regions, but also throw in a bunch of Old regions to the collection set.

A mixed Evacuation pause does not always immediately follow the end of the concurrent marking phase. There is a number of rules and heuristics that affect this. For instance, if it was possible to free up a large portion of the Old regions concurrently, then there is no need to do it.

There may, therefore, easily be a number of fully-young evacuation pauses between the end of concurrent marking and a mixed evacuation pause.

The exact number of Old regions to be added to the collection set, and the order in which they are added, is also selected based on a number of rules. These include the soft real-time performance goals specified for the application, the liveness and gc efficiency data collected during concurrent marking, and a number of configurable JVM options.

The process of a mixed collection is largely the same as we have already reviewed earlier for fully-young gc, but this time we will also cover the subject of remembered sets. Remembered sets are what allows the independent collection of different heap regions.

For instance, when collecting region A,B and C, we have to know whether or not there are references to them from regions D and E to determine their liveness.

But traversing the whole heap graph would take quite a while and ruin the whole point of incremental collection, therefore an optimization is employed. As shown in the illustration below, each region has a remembered set that lists the references pointing to this region from the outside.

These references will then be regarded as additional GC roots. Note that objects in Old regions that were determined to be garbage during concurrent marking will be ignored even if there are outside references to them: the referents are also garbage in that case. What happens next is the same as what other collectors do: multiple parallel GC threads figure out what objects are live and which ones are garbage:.

And, finally, the live objects are moved to survivor regions, creating new if necessary. The now empty regions are freed and can be used for storing objects in again.

To maintain the remembered sets, during the runtime of the application, a Post-Write Barrier is issued whenever a write to a field is performed. If the resulting reference is cross-region, i. To reduce the overhead that the Write Barrier introduces, the process of putting the cards into the Remembered Set is asynchronous and features quite a number of optimizations.

But basically it boils down to the Write Barrier putting the dirty card information into a local buffer, and a specialized GC thread picking it up and propagating the information to the remembered set of the referred region.

In the mixed mode, the logs publish certain new interesting aspects when compared to the fully young mode:. This should give one a sufficient basic understanding of how G1 functions. There are, of course, still quite some implementation details that we have left out for brevity, like dealing with humongous objects. All things considered, G1 is the most technologically advanced production-ready collector available in HotSpot. On top of that, it is being relentlessly improved by the HotSpot Engineers, with new optimizations or features coming in with newer java versions.

As we have seen, G1 addressed a wide range of problems that CMS has, starting from pause predictability and ending with heap fragmentation. Given an application not constrained by CPU utilization, but very sensitive to the latency of individual operations, G1 is very likely to be the best available choice for HotSpot users, especially when running the latest versions of Java.

However, these latency improvements do not come for free: throughput overhead of G1 is larger thanks to the additional write barriers and more active background threads. The only viable way to select the right GC algorithm and settings is through trial and errors, but we do give the general guidelines in the next chapter. We have outlined all of the production-ready algorithms in HotSpot that you can just take and use right away. It is aimed for large multi-core machines with large heaps, the goal is to manage heaps of GB and larger with pauses of 10ms or shorter.

We are not going to go into the implementation details before the new algorithm is released as production-ready, but it also builds upon many of the ideas already covered in earlier chapters, such as concurrent marking and incremental collecting. It does a lot of things differently, however. It does not split the heap into multiple generations, instead having a single space only. This allows it to get rid of card tables and remembered sets.

It also uses forwarding pointers and a Brooks style read barrier to allow for concurrent copying of live objects, thus reducing the number and duration of pauses.

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Log In Continue Monitoring. Outofmemoryerror Java Garbage Collection handbook. Handbook menu What Is Garbage Collection? Why Should I Care? What is the Solution? GC Algorithms: Implementations Now that we have reviewed the core concepts behind GC algorithms, let us move to the specific implementations one can find inside the JVM. MyExecutableClass This option makes sense and is recommended only for the JVM with a couple of hundreds megabytes heap size, running in an environment with a single CPU.

Measured in seconds. This time it is indicating that this was a Minor GC. Allocation Failure — Cause of the collection. In this case, the GC is triggered due to a data structure not fitting into any region in the Young Generation. DefNew — Name of the garbage collector used. This cryptic name stands for the single-threaded mark-copy stop-the-world garbage collector used to clean Young generation.

As Serial Garbage Collector always uses just a single thread, real time is thus equal to the sum of user and system times. For this collection the same DefNew collector was run as before and it decreased the usage of the Young Generation from K to 0. Notice that JVM reports this incorrectly due to buggy behavior and instead reports the Young Generation as being completely full. This collection took 0.

Tenured — Name of the garbage collector used to clean the Old space. The name Tenured indicates a single-threaded stop-the-world mark-sweep-compact garbage collector being used. As seen, no garbage was collected in Metaspace during the event. PSYoungGen — Name of the garbage collector used, representing a parallel mark-copy stop-the-world garbage collector used to clean the Young generation.

In this particular case 8 threads were used. Note that due to some activities not being parallelizable, it always exceeds the ratio by a certain amount. In this case we can see the event started right after the previous Minor GC finished. Using this approach, GC cycle times can be considerably reduced. The G1 collector is a parallel, concurrent, and incrementally compacting low-pause garbage collector. This approach involves segmenting the memory heap into multiple small regions typically Each region is marked as either young generation further devided into eden regions or survivor regions or old generation.

This allows the GC to avoid collecting the entire heap at once, and instead approach the problem incrementally. It means that only a subset of the regions is considered at a time.

G1 keep tracking of the amount of live data that each region contains. This information is used in determining the regions that contain the most garbage; so they are collected first.

Just like other algorithms, unfortunately, the compacting operation takes place using the Stop the World approach. You can configure the pauses duration e. Garbage-First GC will do its best to meet this goal with high probability but not with certainty, that would be hard real-time due to OS level thread management.

Subscribe to get new post notifications, industry updates, best practices, and much more. Directly into your inbox, for free. A Concurrent collector performs Garbage Collection work concurrently with the applications own execution.

Parallel Collector uses multiple cores to perform garbage collection, I guess. In multiple interview, I had been asked to tell about different GC available in market and which one is defaulted with which java version.

Here i got all the answer. A blog about Java and its related technologies, the best practices, algorithms, interview questions, scripting languages, and Python. About Me. Contact Us. Privacy policy. Guest Posts. Secure Hash Algorithms. Best Way to Learn Java.

How to Start New Blog. Skip to content. Mark and sweep It is initial and very basic algorithm which runs in two stages: Marking live objects — find out all objects that are still alive. Removing unreachable objects — get rid of everything else — the supposedly dead and unused objects. Normal deletion — Normal deletion removes unreferenced objects to free space and leave referenced objects and pointers. The memory allocator kind of hashtable holds references to blocks of free space where new object can be allocated.

It is often reffred as mark-sweep algorithm. Normal Deletion — Mark and Sweep Deletion with compacting — Only removing unused objects is not efficient because blocks of free memory is scattered across storage area and cause OutOfMemoryError , if created object big enough and does not find large enough memory block. It is often reffred as mark-sweep-compact algorithm. Indicates that you want to solely use occupancy as a criterion for starting a CMS collection operation.

Use the parameter to specify how long the CMS is allowed to wait for young collection. Sets the number of parallel threads you want used for stop-the-world phases. This allows System. Till Java 8, we have seen Parallel GC as default garbage collector.

Java 9 onwards, G1 is the default garbage collector on and bit server configurations. Size of the heap region.

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