connectivity-based garbage collection martin hirzel university of colorado at boulder collaborators:...

Connectivity-BasedGarbage Collection

Martin HirzelUniversity of Colorado at Boulder

Collaborators: Amer Diwan, Michael Hind, Hal Gabow, Johannes Henkel, Matthew Hertz

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Garbage Collection Benefits

Garbage collection leads to simpler• Design no complex deallocation protocols

• Implementation automatic deallocation

• Maintenance fewer bugs

Benefits are widely accepted

• Java, C#, Python, …

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Garbage Collection:Haven’t we solved this problem yet?• For a state-of-the-art garbage collector:

– time ~14% of execution time– space 3x high watermark– pauses 0.8 seconds

• Can reduce any one cost

• Challenge: reduce all three costs

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Example Heap

Boxes: heap objects

Arrows: pointers

Long box: stack + global variables

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Thesis

1. Objects form distinct data structures

2. Connected objects die together

3. Garbage collectors can exploit 1. and 2. to reclaim objects efficiently

stack +globals

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Experimental Infrastructure

JikesRVM Research Virtual Machine– From IBM Research– Written in Java– Application and runtime system share heap

Good garbage collection even more important

Benchmarks– SPECjvm98 suite and SPECjbb2000– Java Olden suite– xalan, ipsixql, nfc, jigsaw

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Outline

• Garbage Collector Design Principles

• Family of Garbage Collectors

• Design Space Exploration

• Pointer Analysis for Java

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Garbage Collector Design Principles

“Do partial collections.”

Don’t collect the full heap every time

Shorter pause times

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“Predict lifetime based on age.”

Generational hypothesis:Most objects die young

Generational garbage collection:– Partition by age– Collect young objects

most often

Low time overhead

That’s the state of the art.

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young generation old generation

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Generational GC Problems

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young generation old generation

Regular full collections Long peak pause

Old-to-young pointers Need bookkeeping

~37.5% long-lived objects Pig in the python

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“Collect connected objects together.”Likelihood that two objects die at the same time:

Connectivity Example Likelihood

Any pair 33.1%

Weakly connected 46.3%

Strongly connected 72.4%

Direct pointer 76.4%

o2o1 ?

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“Focus on objects with few ancestors.”

Shortlived objects are easy to collect

LifetimeMedian number of ancestor objects

Short 2 objects

Long 83,324 objects

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“Predict lifetime based on roots.”

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Lifetime

Objects reachable … Short Long

indirectly from stack 25.6% 16.2%

only directly from stack 32.9% 0.8%

from globals 4.0% 20.5%

Total 62.5% 37.5%

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For details, see our [ISMM’02] paper.

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Outline





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CBGC Family of Garbage Collectors:

Connectivity-Based Garbage Collection

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• Do partial collections.• Collect connected

objects together.• Predict lifetime based

on age.• Focus on objects with

few ancestors.• Predict lifetime based

on roots.

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Family of Garbage Collectors

Components of CBGC

Before allocation:1. Partitioning

Decide into which partition to put each object

Collection algorithm:2. Estimator

Estimate dead + live objects for each partition

3. ChooserChoose “good” set of partitions

4. Partial collectionCollect chosen partitions

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Find fine-grained partitions, where

• Partition edgesrespect pointers

• Objects don’t move between partitions

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Partitioning Problem

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Pointer analysis• Type-based [Harris]

– o1 may point to o2 if o1 has a field of atype compatible to o2

• Constraint-based [Andersen]– We will discuss this

later in the talk

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Partitioning Solutions

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Estimator Problem

For each partition guess

dead – Objects that can be

reclaimed– Pay-off

live– Objects that must be

traversed– Cost

3 dead + 3 live

1 dead + 2 live

2 dead + 0 live

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Estimator Solutions

Heuristics• Connected objects die

together• Most objects die

young• Objects reachable

from globals live long• The past predicts the

future

3 dead + 3 live

1 dead + 2 live

2 dead + 0 live

p1

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Chooser Problem

Pick subset of partitions• Maximize total dead

• Minimize total live

• Closed under predecessor relation

No bookkeeping for external

pointers

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7 dead + 5 live

3 dead + 3 live

1 dead + 2 live

2 dead + 0 live

2 dead + 2 live

stack +globals

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Chooser Solutions

Optimal algorithm based on network flow [TR]

Simpler, greedy algorithm

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7 dead + 5 live

3 dead + 3 live

1 dead + 2 live

2 dead + 0 live

2 dead + 2 live

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Partial Collection Problem

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Look only at chosen partitions

Traverse reachable objects

Reclaim unreachable objects

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Partial Collection Solutions

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Generalize canonical full-heap algorithms

• Mark and sweep[McCarthy’60]

• Semi-space copying[Cheney’70]

• Treadmill[Baker’92]

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Outline





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Design Space Exploration

Questions

How good is a naïve CBGC?

How good could CBGC be in 20 years?

How well does CBGC do in a JVM?

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Simulator MethodologyGarbage collection simulator (under GPL)

– Uses traces of allocations and pointer writesfrom our benchmark runs

Simulator advantages– Easier to implement variety of collector algorithms– Know entire trace beforehand:

can use that for “in 20 years” experiments

Simulator disadvantages– No bottom-line performance numbers

Currently adding CBGC to JikesRVM

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How good is a naïve CBGC?

Cost in time

Cost in space

Pause times

Full-heapSemi-space

copying

CBGC-naïve• Type-based

partitioning [Harris]• Heuristics

estimator

AppelCopying

generational

jack xalan jbb javac jack xalan jbb javac jack xalan jbb javac1.72

0

0

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0.87

0.22

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Cost in time

Cost in space

Pause times

Full-heapSemi-space

copying

CBGC-oraclesPartitioning

and estimatorbased on trace

AppelCopying

generational

jack xalan jbb javac jack xalan jbb javac jack xalan jbb javac


How good could CBGC be in 20 years?1.72

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0.87

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CBGC with oracles beats Appel– We did not find a “performance wall”– CBGC has potential

The performance gap between CBGC with oracles and naïve CBGC is large

– Research challenges


How good could CBGC be in 20 years?

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How well does CBGC doin a Java virtual machine?

Implementation in progress

Need a pointer analysis for the partitioning

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Outline





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Pointer Analysis for Java

Which analysis do we need?

jack xalan jbb javac jack xalan jbb javac jack xalan jbb javac jack xalan jbb javac jack xalan jbb javac

Cost in time

Full-heap CBGC AppelSemi-space

copyingType-based partitioning

[Harris]

Type-based partitioning (oracles)

Allocation site partitioning (oracles)

Copying generational

[Andersen]

1.7

0

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Andersen’s Analysis

What When

Constraint generation

Model flow of pointers

Ahead-of-timecompilation

Constraint propagation

Find fixed-point solution

Ahead-of-time compilation

• Allocation-site granularity• Set-inclusion constraints• Flow and context insensitive

can’t analyze Javaahead of time!

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Andersen for all of Java

What When

Constraint generation

Model flow of pointers

• VM build and start-up• Class loading• Type resolution• Method compilation (JIT)• Execution of reflection• Execution of native code

Constraint propagation

Find next fixed-point solution

Points-to information used (before garbage collection)

Doas little as possibleas late as possible

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Correctness Properties

Can not do any better for Java!

time… …

Constraintgeneration

Constraintpropagation

If there is a pointer

then the results predict it

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Analysis Cost

Expensive, but once behavior stabilizes,costs diminish to zero

Constraint Constraint propagation

generation Eager At GC At EndSeconds Count Seconds Count Seconds Count Seconds

compress 21.4 130 3.2 5 40.4 1 67.4

db 20.1 143 3.6 5 42.9 1 71.4

mtrt 20.3 265 2.1 5 46.2 1 68.1

mpegaudio 20.6 319 2.2 5 46.1 1 66.6

jack 21.2 397 4.2 7 49.0 1 78.2

jess 22.3 733 6.8 8 49.7 1 85.7

javac 21.1 1,107 5.9 10 87.4 1 187.6

xalan 20.1 1,728 4.9 8 85.7 1 215.7

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ValidationLots of corner cases

– Dynamic class loading– Reflection– Native code

Missing any one leads to nasty bugs– CBGC relies on conservative results

We performed validation runs– Check analysis results against pointers in

heap during garbage collection

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Wrapping Up

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Related Work: Using Program Analysis for Garbage Collection

Stack allocation [ParkGoldberg’92, …]

Regions [TofteTalpin’97, …]

Liveness analysis [AgesenDetlefsMoss’98, …]

Early reclamation [Harris’99]

Thread-local heaps [Steensgaard’00, …]

Object inlining [DolbyChien’00]

Write-barrier removal [ZeeRinard’02, Shuf’02]

…

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Related Work:Pointer analyses for Java

Andersen’s analysis for “static Java”[RountevMilanovaRyder’01][LiangPenningsHarrold’01][WhaleyLam’02][LhotakHendren’03]

Weaker analyses with dynamic class loadingDOIT – [PechtchanskiSarkar’01]XTA – [QianHendren’04]Ruf’s escape analysis – [BogdaSingh’01, King’03]

Demand-driven / incremental analysis

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Other Research Interests

Accuracy of Garbage Collection[M.S.Thesis,ISMM’00,ECOOP’01,TOPLAS’02]

Profiling[FDDO’01,Patent’01a]

Dynamic Optimizations, Prefetching[PLDI’02,Patent’02b]

Future directions:

More techniques for performance improvement

Reducing bugs, improving productivity

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Contributions presented in this talk

Connectivity-based GC design principles[ISMM’02]

CBGC, a new family of garbage collectors;

Design space exploration with simulator[OOPSLA’03]

First non-trivial pointer analysis for Java[ECOOP’04 (to appear)]

http://www.cs.colorado.edu/~hirzel

connectivity-based garbage collection martin hirzel university of colorado at boulder collaborators:...

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