U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Matthew Hertz, Yi Feng, &...

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

Matthew Hertz, Yi Feng, & Emery Berger

Department of Computer ScienceUniversity of Massachusetts

Hippocratic Garbage Collection

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 2

Dude, where’s my performance?

Sun HotSpot JDK 1.4.1, Java Beans benchmark


Ever-Steeper Memory Hierarchy

Higher = smaller, faster, closer to CPU A real desktop machine (mine)

registers

L1 cache

L2 cache

RAM

Disk

8 integer, 8 floating-point; 1-cycle latency

8K data & instructions; 2-cycle latency

512K; 7-cycle latency

1GB; 100 cycle latency

40 GB; 38,000,000 cycle latency (!)


Program Throughput: Ideal vs. Real

492 113 103 930

100

200

300

400

500

600

700

800

900

1000

Execution Time for pseudoJBB with 60MB Heap

IdealGenCopyGenMS

Available Memory (in MB)

Exe

cuti

on T

ime (

in s

)


GC Pause Time

492 113 103 930

5

10

15

20

25

30

35

Max Garbage Collection Pause for pseudoJBB

GenCopyGenMS

Available Memory (in MB)

Avg.

GC

Pause

(in

s)

From 55 milliseconds to 30 seconds!..Can we avoid this?


Memory Prices Over TimeRAM Prices Over Time

(1977 dollars)

$0.01

$0.10

$1.00

$10.00

$100.00

$1,000.00

$10,000.00

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Year

Do

llars

per

GB

2K

8K

32K

128K

512K

2M

8M

conventional DRAM

“Soon it will be free…”


Memory Prices: Inflection Point

RAM Prices Over Time(1977 dollars)

$0.01

$0.10

$1.00

$10.00

$100.00

$1,000.00

$10,000.00

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Year

Do

llars

per

GB

2K

8K

32K

128K

512K

2M

8M

512M

1G

SDRAM,RDRAM,DDR,Chipkill

conventional DRAM


Memory Not So Free

Servers: buy 4GB,get 1 CPU free!

Sun E10000: 4GB extra = $75,000!

Fast RAM Cosmic rays…

4GB Sun RAM = ½ Ferrari Modena

“Buy more RAM” = “Ferraris for Everyone”

Desktops: most 256MB 1GB = 50% more $ Laptops = 70%, if possible


Fast DDR2 modules today: $500/GB


Outline

Motivation GC Paging Behavior Hippocratic Garbage Collection

Cooperation Bookmarking

Results Future Work


What’s really going on here?


GC Performance While Paging

RAM

Hard Disk

GC: Touch evicted page


RAM

Hard Disk


VM: brings page in-core


RAM

Hard Disk


VM: evicts different page (LRU)


RAM

Hard Disk


GC: Touches page on disk


RAM

Hard Disk


VM: Evicts another page…and so on…


Oblivious Memory Management

Garbage collection: VM-oblivious Cannot adjust to changing memory

pressure Touches non-resident pages

Virtual memory: GC-oblivious Likely to evict pages needed by GC


Outline

Motivation GC Paging Behavior Hippocratic Garbage

Collection Cooperation Bookmarking

Results Future Work


Our Inspiration

As to diseases,

make a habit of two things: to help, or at least, to do no harm.

Hippocrates


Hippocratic Garbage Collection

Goal: GC triggers no additional paging Key ideas:

Minimize space consumption Exploit page information from VM

Residency, eviction notifications Avoid touching evicted pages

Garbage collector Virtual memory manager

Evacuates pagesSelects victim pages

Page replacement

page eviction notification

victim page(s)


HC Collector Overview

Generational Focused activity in nursery: improves locality

Mark-sweep (w/o pressure) Compaction

Shrink nursery Segregated size classes

Eliminates fragmentation Reduces memory pressure

Essentially page-oriented GenMS + compaction


Segregated Size Classes

Mark objects


Segregated Size Classes

Copy into empty spaces


Cooperative Collection

GC and VM communication Before-paging callback Per-page eviction negotiation

Modify collector Manage heap memory at page

granularity GC on page eviction notification When possible, return empty pages Otherwise, process pages before

eviction: “bookmarking”


Process pages before they are evicted…

RAM

Hard Disk

Bookmarking


…by “bookmarking” target objects

RAM

Hard Disk

Bookmarking


then allow page eviction

RAM

Hard Disk

Bookmarking


process objects as normal except…

RAM

Hard Disk

Collection with Bookmarking


…ignore references to evicted pages

RAM

Hard Disk



bookmarks remember references from disk

RAM

Hard Disk



Result: no paging!

RAM

Hard Disk



Bookmarking Details

Cheap summary of connectivity One bit per object: stolen from object

header One word per page: count of pages

that hold bookmarks on this page Clear bookmarks when zero

Invoke VM-oblivious collection only when heap is exhausted Common case for all other collectors! In practice: hasn’t happened yet


Outline

Motivation GC Paging Behavior Hippocratic Garbage

Collection Cooperation Bookmarking

Results Future Work


Methodology Jikes RVM, MMTk, extended Linux

kernel “OPT + reset”

Opt-compile first, full collection,second timing run

Benchmarks: SPECjvm98, DaCapo suite (ipsixql,

jython) SPECjbb variant (pseudojbb)

Signalmem for memory pressure Dynamic (30MB, then 60MB) Static (40% of heap available)


Execution time:No Memory Pressure

Generally runs in smaller heaps, fast as GenMS


Execution Time:No Memory Pressure – pseudoJBB

CopyMS, etc. at least 6%-89% slower


Execution Time:Dynamic Memory Pressure

Startup process: 30MB, then 60MB more


Pause Time:Dynamic Memory Pressure

Startup process: 30MB, then 60MB more


Execution Time:Constant Memory Pressure

Memory sufficient to hold 40% of heap


Pause Times:Constant Memory Pressure

Memory sufficient to hold 40% of heap


Future Work

Different eviction strategies Exploit type info, fewer pointers

Richer summaries Shrink heap vs. evict pages; grow

heap Requires more VM info

Static analyses Combine with automatic heap sizing

[Yang et al., ISMM 04]


Conclusion

Hippocratic Garbage Collection Cooperates with VM Competitive when not paging

Performs up to 13% faster with small heaps

As fast as GenMS for larger heaps Greatly improves performance when

paging Throughput up to 3x greater Average pause times up to 30x smaller

than CopyMS, 70x smaller than GenMS


Backup Slides


Some Related Work

Compaction: reduce working set Linearize lists [Bobrow & Murphy] Semispace collection [Baker, Fenichel &

Yochelson]

Reduce frequency of whole-heap collection Ephemeral [Moon] & generational collectors

[Lieberman & Hewett, Ungar, Appel]

Cooperation Shrink heap [Alonso & Appel]

Give up empty pages [Cooper, Nettles et al.]


Tracking Page Value

Maintain histogram per page position Provides value to application of n pages (for any

n)

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