Garbage Collection: Theory and Practice

Android’s VM relies upon an automatic memory management scheme; this cornerstone of the programming paradigm on which Java is based on has been a part of the Android ecosystem since its inception. For anybody unfamiliar with the concept, a quick explanation is that a programmer is neither in charge of having to either manually to reserve (allocate) physical memory nor freeing this memory up by himself once he is done with it. This is an important distinction to lower level programming languages where manual memory management is the norm. Of course, the advantage of automatic management is that it saves the developer from having to worry about memory management. The disadvantage is that the developer also no longer has control, and is at the whim of the system to correctly manage things in an optimal manner.

Android and Dalvik have traditionally suffered a lot from Dalvik’s garbage collection (GC) mechanism. Each time an application needed memory to be allocated and the heap (a space of memory dedicated to that app) would not be able to accommodate that allocation, the GC would fire up.

A GC’s job is to traverse the heap, enumerate all objects allocated by the application, mark all reachable objects (meaning, mark all objects that are still in use), and to free up whatever is left remaining.

In Dalvik’s case, this process resulted in two pauses; one during the enumeration phase, and one during the marking phase. A pause in this sense is that all code execution is halted for the totality of the application on all its threads. If the pauses are too big, this would result in dropped frames in the rendering of the application, which in turn resulted in jank and stuttering in the user experience.

Google claims the average duration of these pauses went up to 54ms on a Nexus 5 device, which results in a minimum of 4 dropped frames on average each time the GC would activate.

In my own experience and investigating, this number can blow up dramatically depending upon the application. For example, the official FIFA app is a poster child case in which garbage collection can run wild;

07-01 15:56:14.275: D/dalvikvm(30615): GC_FOR_ALLOC freed 4442K, 25% free 20183K/26856K, paused 24ms, total 24ms

07-01 15:56:16.785: I/dalvikvm-heap(30615): Grow heap (frag case) to 38.179MB for 8294416-byte allocation

07-01 15:56:17.225: I/dalvikvm-heap(30615): Grow heap (frag case) to 48.279MB for 7361296-byte allocation

07-01 15:56:17.625: I/Choreographer(30615): Skipped 35 frames! The application may be doing too much work on its main thread.

07-01 15:56:19.035: D/dalvikvm(30615): GC_CONCURRENT freed 35838K, 43% free 51351K/89052K, paused 3ms+5ms, total 106ms

07-01 15:56:19.035: D/dalvikvm(30615): WAIT_FOR_CONCURRENT_GC blocked 96ms

07-01 15:56:19.815: D/dalvikvm(30615): GC_CONCURRENT freed 7078K, 42% free 52464K/89052K, paused 14ms+4ms, total 96ms

07-01 15:56:19.815: D/dalvikvm(30615): WAIT_FOR_CONCURRENT_GC blocked 74ms

07-01 15:56:20.035: I/Choreographer(30615): Skipped 141 frames! The application may be doing too much work on its main thread.

07-01 15:56:20.275: D/dalvikvm(30615): GC_FOR_ALLOC freed 4774K, 45% free 49801K/89052K, paused 168ms, total 168ms

07-01 15:56:20.295: I/dalvikvm-heap(30615): Grow heap (frag case) to 56.900MB for 4665616-byte allocation

07-01 15:56:21.315: D/dalvikvm(30615): GC_FOR_ALLOC freed 1359K, 42% free 55045K/93612K, paused 95ms, total 95ms

07-01 15:56:21.965: D/dalvikvm(30615): GC_CONCURRENT freed 6376K, 40% free 56861K/93612K, paused 16ms+8ms, total 126ms

07-01 15:56:21.965: D/dalvikvm(30615): WAIT_FOR_CONCURRENT_GC blocked 111ms

07-01 15:56:21.965: D/dalvikvm(30615): WAIT_FOR_CONCURRENT_GC blocked 97ms

07-01 15:56:22.085: I/Choreographer(30615): Skipped 38 frames! The application may be doing too much work on its main thread.

07-01 15:56:22.195: D/dalvikvm(30615): GC_FOR_ALLOC freed 1539K, 40% free 56833K/93612K, paused 87ms, total 87ms

07-01 15:56:22.195: I/dalvikvm-heap(30615): Grow heap (frag case) to 60.588MB for 1331732-byte allocation

07-01 15:56:22.475: D/dalvikvm(30615): GC_FOR_ALLOC freed 308K, 39% free 59497K/96216K, paused 84ms, total 84ms

07-01 15:56:22.815: D/dalvikvm(30615): GC_FOR_ALLOC freed 287K, 38% free 60878K/97516K, paused 95ms, total 95ms

Above is a log extract of the first few seconds after launching the application. The garbage collector was invoked for a total of 9 times, causing the application to freeze for a total of 603ms, and a total of 214 dropped frames. Most of the pauses are caused by requests for memory allocation, as described by the “GC_FOR_ALLOC” tag in the log entries.

What ART promises and provides is a huge revamp of the whole garbage collection system, to virtually eliminate cases as the above. Here’s a comparison of the same situation under ART:

07-01 16:00:44.531: I/art(198): Explicit concurrent mark sweep GC freed 700(30KB) AllocSpace objects, 0(0B) LOS objects, 792% free, 18MB/21MB, paused 186us total 12.763ms

07-01 16:00:44.545: I/art(198): Explicit concurrent mark sweep GC freed 7(240B) AllocSpace objects, 0(0B) LOS objects, 792% free, 18MB/21MB, paused 198us total 9.465ms

07-01 16:00:44.554: I/art(198): Explicit concurrent mark sweep GC freed 5(160B) AllocSpace objects, 0(0B) LOS objects, 792% free, 18MB/21MB, paused 224us total 9.045ms

07-01 16:00:44.690: I/art(801): Explicit concurrent mark sweep GC freed 65595(3MB) AllocSpace objects, 9(4MB) LOS objects, 810% free, 38MB/58MB, paused 1.195ms total 87.219ms

07-01 16:00:46.517: I/art(29197): Background partial concurrent mark sweep GC freed 74626(3MB) AllocSpace objects, 39(4MB) LOS objects, 1496% free, 25MB/32MB, paused 4.422ms total 1.371747s

07-01 16:00:48.534: I/Choreographer(29197): Skipped 30 frames! The application may be doing too much work on its main thread.

07-01 16:00:48.566: I/art(29197): Background sticky concurrent mark sweep GC freed 70319(3MB) AllocSpace objects, 59(5MB) LOS objects, 825% free, 49MB/56MB, paused 6.139ms total 52.868ms

07-01 16:00:49.282: I/Choreographer(29197): Skipped 33 frames! The application may be doing too much work on its main thread.

07-01 16:00:49.652: I/art(1287): Heap transition to ProcessStateJankImperceptible took 45.636146ms saved at least 723KB

07-01 16:00:49.660: I/art(1256): Heap transition to ProcessStateJankImperceptible took 52.650677ms saved at least 966KB

The difference between ART and Dalvik couldn’t be bigger, as the new runtime manages to only pause for a total of 12.364ms over 4 foreground GC calls and two background GC calls. The heap did not need to grow at all during the application launch, in comparison to Dalvik’s 4 increases. The number of frames dropped diminished to 63.

Obviously this is a worst-case scenario of a badly developed application, as the application even in ART’s case still managed to drop a little over a second of frames, but bad programming practices such as overloading the UI thread is something that Android has to deal with on a regular basis.

ART offloads some of the work that is usually done by the garbage collector to the application itself, this eliminates the need for the execution pause during the enumeration phase. The second pause is vastly reduced by trying to do the work before the pause - a technique called packard pre-cleaning is applied, and the pause itself is reserved to a simply checking and validation of the work done. Google promises that they have managed to reduce these pauses to an average of 3ms, a good order of magnitude lower than presently achieved by the Dalvik GC.

The introduction of a special “Large Object Space” which is separate of the main heap, but still residing in the application memory, is destined to facilitate better handling of big objects such as bitmaps. These large primitive objects would cause great problems in terms of fragmenting the heap, causing more need for a GC invocation in case a new object needed to be allocated, but no continuous memory region big enough for it would be available. The frequency of GC invocations is thus greatly reduced due to less fragmentation of the memory heap and more intelligent allocation and elimination by the runtime.

Another good example is the launching of the Hangouts application; In Dalvik we see several GC pauses due to allocation calls:

07-01 06:37:13.481: D/dalvikvm(7403): GC_EXPLICIT freed 2315K, 46% free 18483K/34016K, paused 3ms+4ms, total 40ms

07-01 06:37:13.901: D/dalvikvm(9871): GC_CONCURRENT freed 3779K, 22% free 21193K/26856K, paused 3ms+3ms, total 36ms

07-01 06:37:14.041: D/dalvikvm(9871): GC_FOR_ALLOC freed 368K, 21% free 21451K/26856K, paused 25ms, total 25ms

07-01 06:37:14.041: I/dalvikvm-heap(9871): Grow heap (frag case) to 24.907MB for 147472-byte allocation

07-01 06:37:14.071: D/dalvikvm(9871): GC_FOR_ALLOC freed 4K, 20% free 22167K/27596K, paused 25ms, total 25ms

07-01 06:37:14.111: D/dalvikvm(9871): GC_FOR_ALLOC freed 9K, 19% free 23892K/29372K, paused 27ms, total 28ms

We have a good extract of all GC cases in the above log. The explicit and concurrent GC calls are the general cleanup and maintenance calls of the GC. The for_alloc calls are cases where the memory allocator is trying to allocate something but it doesn’t fit in the heap, and a GC is launched to try to make space. In the middle we see that the heap was expanded due to fragmentation and not being able to hold a larger object. The total “dead time” ends up at 90ms. In comparison, here’s the current ART extract in the L preview:

07-01 06:35:19.718: I/art(10844): Heap transition to ProcessStateJankPerceptible took 17.989063ms saved at least -138KB

07-01 06:35:24.171: I/art(1256): Heap transition to ProcessStateJankImperceptible took 42.936250ms saved at least 258KB

07-01 06:35:24.806: I/art(801): Explicit concurrent mark sweep GC freed 85790(3MB) AllocSpace objects, 4(10MB) LOS objects, 850% free, 35MB/56MB, paused 961us total 83.110ms

We’re not quite sure on what the heap transition lines represent, but we’ll assume they’re the heap resizing mechanisms. The only GC call is after the app is launched already and comes at a minuscule 961µS. We don’t see any of the previous calls to the GC. What is interesting, is the LOS statistics. We see that we have 4 large objects at 10MB in the LOS, allocation that would have otherwise been resided in heap space now totally foregoes it, and with that, avoids the repeated for_alloc GC calls and heap fragmentation that plagued Dalvik.

The memory allocation system itself was also revamped. While ART itself provided an about 25% boost over Dalvik, Google was not happy with this and introduced a new memory allocator replacing the currently used “malloc” allocator.

This new allocator, “rosalloc” or Runs-of-Slots-Allocator, was developed in mind for the usage cases of multithreaded Java applications. The new allocator has a more fine-grained locking mechanisms which can lock on individual bins of objects instead of the whole memory allocation itself. Small objects in thread local regions are able to completely forego locking altogether.

The result is a huge improvement in allocation speed, resulting in up to a 10x gain.

The garbage collection algorithms themselves have been reworked to improve the user experience and to avoid interruptions of the application. These algorithms are still a work in progress and Google has only recently introduced a new dedicated algorithm, the “Moving garbage collector” whole sole purpose is to defragment the application heap while applications are in the background.

Introduction and Architecture 64-Bit Support and Conclusion


View All Comments

  • uhuznaa - Thursday, July 3, 2014 - link

    Setting scrolling velocity is just a decision of the developers, this is just a parameter. You can make the crappiest hardware scroll like mad. Smoothness (and getting the behaviour close to a believable, consistent physical model of inertness and friction) is really hard work that requires lots of things in the system working right to even allow trying. Android was never good (or consistently good) at that. Google has improved it with every version though. Reply
  • darkich - Thursday, July 3, 2014 - link

    "and getting the behaviour close to a believable, consistent physical model of inertness and friction"

    .. And that is exactly where Dolphin trumps everything else, at least for me.
    Sure, it's not perfect at all times and on all sides (no browser is, again) but at its best, Dolphin Jetpack is the prime example of the description you gave.. It feels like a real, oily smooth mechanism
  • jospoortvliet - Thursday, July 3, 2014 - link

    Darkich: perhaps this helps:
    'smooth' is about the dropping of frames or (in)frequently stalling the drawing. It has nothing to do with how quickly you go to the bottom of a page as the browser can simply stop drawing for 1/10th of a second and show the bottom of the page and be fastest to the bottom - yet it would not be smooth at all.

    So uhuznaa is right, scrolling speed has nothing to do with how smooth and fluid the UI is. It can be slow but never drop frames or fast but drop frames all the time.
  • sonicmerlin - Wednesday, July 2, 2014 - link

    Lol no. As long as Android continues to run the UI thread on the core thread it will never be as smooth as iOS or WP. Reply
  • uhuznaa - Thursday, July 3, 2014 - link

    I don't know if this is the reason but on my old iPhone 4 I can install 5 apps and continue to use an app at the same time without even noticing the installs going on in the background while doing the same on my Nexus 7 only leads to frustration. Same with loading lots of emails or anything else going on in the background. My Nexus always gets seizures and seems to hang for seconds when this happens. iOS seems to prioritize user interaction over everything while in Android user input seems to be treated as just another task to be handled sooner or later. Reply
  • jospoortvliet - Thursday, July 3, 2014 - link

    It is possibly part of the reason, but for what you talk about the main reason, I think, is that the Linux kernel is not good at handling I/O while maintaining interactivity. This is actually currently being taken care off but with the linux kernel in android so far behind mainline (linux is at 3.15, android at what, 3.5?) this might take a while to get fixed. Reply
  • Alexey291 - Tuesday, July 8, 2014 - link

    throw in the fact that nexus7 (2012) has a terrible nand controller and some really cheap nand chips and you have the result that you have aka stuttering slow trash device. Reply
  • npz - Tuesday, July 1, 2014 - link

    Maybe Google and other app stores in the future can do the native compilation for x86 and ARM on the server and users would download the bytecode and their native platform code; or even just the native code. Reply
  • skiboysteve - Wednesday, July 2, 2014 - link

    That's exactly what win phone does. However, its a smaller ecosystem... Would be harder for android but still possible at least for play store Reply
  • DanNeely - Wednesday, July 2, 2014 - link

    Shouldn't be any harder. You compile once/device (SoC?) and store the result for future downloads. The initial conversion would need a ton of CPU time; but they could do that in advance using spare data center capacity during slow times. Reply

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