Andrew Turley on Incremental Mature Garbage Collection Using the Train Algorithm

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Incremental Mature Garbage Collec2on Using the Train Algorithm A totally awesome paper!

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About Me Andrew Turley aturley@acm.org @casio_juarez Junior SoEware Engineer at TheLadders (we’re hiring!)

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About The Paper Incremental Mature Garbage Collec2on Using the Train Algorithm Jacob Seligmann and Steﬀen Grarup Presented at the Ninth European Conference on Object-‐Oriented Programming in 1995

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Overview Incremental Mature Garbage Collec2on Using the Train Algorithm •  Garbage Collec2on •  Mature •  Incremental •  The Train Algorithm

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Incremental Mature Garbage Collec2on Using the Train Algorithm

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Garbage Collec2on -‐ What Is Garbage? // a has a member p! a = new ExcellentClass();! ! // create an object and assign it to a.p! a.p = new VeryExcellentClass();! ! // create another object and assign it to a.p! a.p = new VeryExcellentClass();! ! !

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Garbage Collec2on – What Is Collec2on? •  Make memory used by dead objects available for exis2ng or new objects

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Garbage Collec2on -‐ Manual vs Automa2c •  Manual – the programmer explicitly tells the system when an object is no longer being used •  Automa2c – the system automa2cally determines when an object is no longer being used – “no longer being used” == unreachable from roots

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Garbage Collec2on – Why Bother? •  Might eventually run out of memory •  Put related objects closer together in memory

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Garbage Collec2on -‐ Two Main Types •  Coun2ng – keep track of how many other objects reference an object, if the number of references reaches 0 then collect the object •  Tracing – when you run out of memory, ﬁnd all the reachable objects, collect all the other (unreachable) objects

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Garbage Collec2on -‐ Coun2ng

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Garbage Collec2on -‐ Tracing

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Garbage Collec2on -‐ When Do You Collect? Generally (naively) … •  Coun2ng -‐-‐ Collect as soon as an object becomes unreachable (count = 0) •  Tracing -‐-‐ Collect when there's no more memory leE

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Garbage Collec2on -‐ Compac2ng •  Once you get rid of dead objects, move live objects closer together •  Can improve locality •  There’s a cost

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Garbage Collec2on -‐ Considera2ons •  Throughput -‐-‐ 2me outside of gc / total running 2me •  Pause Times -‐-‐ 2me the applica2on is paused for collec2on •  Memory Overhead -‐-‐ memory used to track objects for garbage collec2on

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Incremental Mature Garbage Collec2on Using the Train Algorithm

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Mature -‐ The Weak Genera2onal Hypothesis •  “Most objects die young.”

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Mature -‐ Separate Genera2ons of Objects •  Allocate objects in one part of the heap, promote them to another part if they live long enough

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Mature -‐ Don't Need Collect All Genera2ons At Once •  By keeping the younger genera2on smaller, there are fewer objects to trace, so collec2ng this genera2on is fast

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Mature -‐ Different Algorithms For Different Genera2ons •  Younger genera2ons are small and unstable •  Older genera2ons are larger and stable •  Make different 2me and space tradeoffs

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Mature -‐ Remembered Sets •  Record references between genera2ons, use old-‐to-‐young as roots when tracing •  Collector doesn’t need to scan Old when collec2ng Young

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Mature -‐ You S2ll Pay A Price •  If the old genera2on is large, collec2ng it can take ... a very long 2me

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Incremental Mature Garbage Collec2on Using the Train Algorithm

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Incremental -‐ Don’t Do Everything At Once •  Only collect a subset of the heap at a 2me •  Reduces pause 2mes

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Incremental -‐ Picking Subsets •  Collect related objects at the same 2me (Baker, 1977) •  How do we ﬁgure out which objects are "related"?

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Incremental Mature Garbage Collec2on Using the Train Algorithm

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Train Algorithm -‐ Originally Incremental Collec2on of Mature Objects Richard Hudson and Eliot Moss

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Train Algorithm -‐ A Real Implementa2on Jacob Seligmann and Steﬀen Grarup Goal: non-‐disrup2ve GC for interac2ve systems

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Train Algorithm -‐ Target Plalorm •  Mjølner (me-‐'ol-‐near) BETA System •  Basically an IDE wrinen in and suppor2ng the BETA programming language •  Sun SPARC IPX 4/50, 40 Mhz, 32MB RAM

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Train Algorithm -‐ Structure Of The Heap •  Young Genera2on (Infant Object Area) -‐-‐ semi-‐ space copying collector •  Old Genera2on (Mature Object Area) -‐-‐ the Train Algorithm (previously used mark-‐sweep) •  Large Value Area -‐-‐ free lists, periodic compac2on

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Train Algorithm -‐ Structure Of The Mature Space •  Collec2ons of ﬁxed-‐size areas into which objects are copied •  ﬁxed-‐size area = car •  collec2on of cars = train •  Cars are ordered in trains, trains are ordered by crea2on 2me

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The Train Algorithm SEE THE HANDOUT

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Train Algorithm -‐ Tenuring Strategy "Objects promoted from younger genera2ons may be stored in any train except the one currently being collected, or one or more new trains may be created to hold them."

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Train Algorithm -‐ Evacua2on Strategy •  How are live objects moved out of the car being collected? •  Evacuate to the last car of the ﬁrst train seen

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Train Algorithm – Example 1

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Train Algorithm – Example 2

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Train Algorithm – Example 3

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Train Algorithm – Example 4

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Train Algorithm – Example 5

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Train Algorithm – Example 6

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Train Algorithm -‐ Fu2le Collec2ons •  A “fu2le collec2on” is a collec2on that does not result in an evacua2on, nor does it reclaim objects

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Train Algorithm -‐ Why It Works •  Groups of connected objects end up in the same train •  Live objects are moved to later trains, dead objects stay put and eventually their train is collected •  “It’s like the train is headed for a cliﬀ, and live objects get pulled into other cars or other trains by their friends.” – Franco Barbeite

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Train Algorithm -‐ Remembered Sets •  Cars have remembered sets that contain informa2on about all references residing outside the car poin2ng into it – Only need to remember reference from higher numbered to lower numbered cars •  Cars have remembered sets that contain references to the young genera2on

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Train Algorithm -‐ Invoca2on Frequency •  Trade oﬀ between keeping the mature space rela2vely garbage-‐free and not was2ng too much 2me moving live objects •  "Acceptable Garbage Percentage" and upper limit on number of young genera2on collec2ons between old genera2on collec2ons •  Garbage Ra2o is es2mated aEer each train collec2on by keeping track of the size of data directly tenured in each train (see thesis)

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Train Algorithm -‐ Popular Objects •  All Mjølner BETA programs have an environment object which is referenced by lots of other objects •  Store this object in a special car with no remembered set, when this car comes up for collec2on move it to a new train •  Chea2ng! (not really general purpose)

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Train Algorithm -‐ Results •  We won! •  Reduced max and average pause 2mes (3.21s vs 0.12s and 1.71s vs 0.04s in the editor) •  Minimal impact on GC overhead (pointer book keeping, etc) •  20% overall increase in old genera2on collec2on 2me •  Small increase in storage requirements (larger remembered sets, train accoun2ng) •  Slight increase in garbage overhead (uncollected garbage) •  Popular object treatment was a really good idea (30% worse without) •  Improved locality

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Train Algorithm -‐ And Then What? •  Was added to version 1.3.1 of the Hotspot JVM in 1998 – Lots of work by Alex Garthwaite •  Was later deprecated because of issues with cycle detec2on and high overhead with respect to throughput •  The ideas live on in the G1 (garbage ﬁrst) collector

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Why I Love This Paper •  Trains! •  Engineering over theory •  The Train Algorithm is a good illustra2on of how ﬁendishly diﬃcult garbage collec2on can be

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WELL ACTUALLY