Rethinking Garbage Collection

Rifat Shahriyar

Who Am I? •  Asst. Prof., Dept. of CSE, BUET •  PhD from Australian National University

(ANU) •  Dissertation title –  High Performance Reference Counting and

Conservative Garbage Collection •  Supervised by –  Steve Blackburn (ANU) –  Kathryn McKinley (MSR)

2

Java Virtual Machine

3

The Birth of GC (1960)

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Today Garbage collection is ubiquitous

•  GC algorithms –  Tracing and Reference counting

•  GC implementations –  Exact and Conservative

✔ Tracing and Exact in all highly engineered, high performance systems ✘ Reference counting and conservative only

in non-performance critical settings

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GC Fundamentals Algorithmic Components

Allocation Reclamation

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Identification

Bump Allocation

Free List

`

Tracing (implicit)

Reference Counting (explicit)

Sweep-to-Free

Compact

Evacuate

3 1

Mark-Compact [Styger 1967] Bump allocation + trace + compact

GC Fundamentals Canonical Garbage Collectors

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`

Sweep-to-Free

Compact

Evacuate

Mark-Sweep [McCarthy 1960] Free-list + trace + sweep-to-free

Semi-Space [Cheney 1970] Bump allocation + trace + evacuate

Generational [Ungar 1984]

•  Most objects die young

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Nursery space

Mature space

Immix [Blackburn and McKinley 2008]

0

•  Contiguous allocation into regions –  256B lines and 32KB blocks –  Objects span lines but not blocks

•  Simple mark phase – Mark objects and containing regions

•  Free unmarked regions •  Recycled allocation and defragmentation

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block

line

recyclable lines object mark line mark

Down for the Count? Getting Reference Counting

Back in the Ring ISMM’12

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Tracing [McCarthy1960]

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A

D

B

E

C

F ✗ ✗

Roots

Reference Counting [Collins 1960]

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1

1

1

1 2

1 2

0

1

✗

Roots

Why Reference Counting?

Advantages ✔  Immediacy ✔ Object local ✔ Basic RC is easy Disadvantages ✘  Cycles ✘  Performance

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Problem

✔ One of the two fundamental GC algorithms ✔ Many advantages ✘ Neglected by performance-conscious VMs So how much slower is it?

Can we get RC back in the ring?

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30%

Optimizing RC

•  Limited bit count –  Use just few bits, fix o/f with backup tracing

•  Elision of new object counts –  Only do RC work if object survives the first GC

•  Born as dead –  Avoid free-list work for short lived objects

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16

New RC ≈ MS

Optimized RC vs. MS

-20% 0%

20% 40% 60% 80%

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Old RC New RC

Summary •  Old RC –  30% slower than MS –  40% slower than production

•  New RC –  Limited bit count –  Optimization for new objects

•  Performance – Matches MS –  Still 10% slower than production

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40%

10%

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< 2012 2012

Taking Off the Gloves with Reference Counting Immix

OOPSLA’13

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Why So Slow?

10% 10%

-3%

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Total Mutator

GC

Looking a Little Deeper…

10% 9%

32%

7% 4%

28%

-2% -3% -2% -3% -3%

1%

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RC MS SS Immix 20

Time Instructions Retired

L1 D Cache Misses

Looking a Little Deeper…

10% 9%

32%

7% 4%

28%

-2% -3% -2% -3% -3%

1%

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RC MS SS Immix 21

Time Instructions Retired

L1 D Cache Misses

Free List

Bump Pointer

Goal & Challenge

•  Goal –  Object-local collection –  Excellent mutator locality –  Copying to eliminate fragmentation

•  Immix provides opportunistic copying ✔ Same mutator locality as contiguous allocator

•  However, RC is inherently local –  References to an object generally unknown –  but copying must redirect all references

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RC Immix

✔ Combines RC and Immix ✔ Line/block reclamation ✔ Line live object count with object reference count

✔ Exploit Immix’s opportunistic copy ✔ Observe new objects can be copied by first GC ✔ Observe old objects can be copied by backup GC

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Total time

3% faster then Gen Immix, +6% worst case, -21% best case 24

-30%

-20%

-10%

0%

10%

20%

30%

40%

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RC RC Immix

Summary •  RC Immix –  Object-local collection –  Excellent mutator locality –  Copying with RC

•  Great performance –  Outperforms fastest production

•  Transforms RC

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RC 2013

RC Immix

-3%

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Questions?