PowerPoint Template - William & Mary Computer Science

Transcript PowerPoint Template - William & Mary Computer Science

Influence of Program Inputs on the Selection of Garbage Collectors

Feng Mao, Eddy Zheng Zhang and Xipeng Shen

The College of William and Mary

Introduction

 GC determines efficiency of  Memory manage collection time  Data locality  Various garbage collectors  Perform differently on different applications 2

GC Selection

 Selecting the best garbage collector for an execution  Application-specific selection [Fitgerald & Tarditi: ISMM’00, Soman et al.: ISMM’04, Singer et al.: ISMM’07]  Selecting a GC for each application  Based on offline profilings 3

Influence of Inputs

 An important but under-explored dimension  Determine the robustness of profiling-based selection  Preliminarily covered previously [ Soman et al.: ISMM’04, Singer et al.: ISMM’07] • Few inputs per application • Different observations 4

Objective

A comprehensive understanding of the influence of inputs on the selection of garbage collectors.

Overview

 A systematic measurement  1580 inputs   10 programs 316,000 executions   5 garbage collectors 4 heap size ratios  A statistical analysis to address indeterminism 6

Overview

 Findings  Top collectors vary across inputs  Cross-input consistency exists  Heap size ratio matters  Heap size ratio is predictable 7

Outline

 Measurement  Methodology  Statistical performance analysis     Top collectors vary across inputs Cross-input consistency exists Heap size ratio matters Heap size ratio is predictable 8

Measurement

 Environment  Intel Xeon E5310  Linux 2.6.9

 Jikes RVM 2.9.1

 5 Garbage collectors (included in MMTK)  GenCopy, GenMS, MarkSweep, RefCount, SemiSpace 9

Heap Size Ratio

r = heap size min possible heap size   4 heap size ratios: 1, 2, 4, 8 The min possible heap size differs across applications, and inputs 10

Benchmarks Benchmark Min heap size (MB) Compress Db

j j

Mpegaudio Mtrt

Bloat

Fop

d j

Euler

MoDyn

MonteCarlo

Search

20-98 16-31 16-20 15-49 22-23 72-86 16-55 18-21 39-74 21-21 Number of inputs 18 100 30 100 976 224 14 15 30 8

Metrics

 End-to-end execution time  Including start-up time  No replay  Challenge  Non-determinism in performance • JIT compilation • Thread scheduling • Noises from environment  Average time? Min time? Max time?

Statistical Analysis

   Thanks to Georeges et al.

[OOPSLA’07] 10 repetitive runs Compute confidence interval   Student’s t-distribution 90%-confidence interval • means the interval contains the true running time with 90% probability Interval overlap => Not significantly different in performance 13

Example

GC1

22 20.5 23.5

{ 22, 22.1, 21.9, 22.2, 21.8 } 21.2

19.7 22.8

GC2

(s)

Overlap => {21.1, 20.8, 20.7, 20.7, 22.8} Not significantly different in performance

(s)

Outline

 Measurement  Methodology  Statistical performance analysis    

Top collectors vary across inputs

Cross-input consistency exists Heap size ratio matters Heap size ratio is predictable 15

Top Sets of GC

 A top set of collectors for an execution contains   The collectors performing the best Their confidence intervals overlap with one another 16

Variations of Top Sets {GC2} {GC3} {GC2, GC3}

Mtrt in Detail

Implication

 Risk of profiling-based GC selection 19

Outline

 Measurement  Methodology  Statistical performance analysis     Top collectors vary across inputs

Cross-input consistency exists

Heap size ratio matters Heap size ratio is predictable 20

Coverage of a collector

# of inputs that GC i is a top collector total number of inputs 21

Coverage

Risk of Using Top Collector

Implication

 Profiling on a spectrum of inputs and select the top collector.

Is it enough?

Outline

 Measurement  Methodology  Statistical performance analysis     Top collectors vary across inputs Cross-input consistency exists

Heap size ratio matters

Heap size ratio is predictable 25

Coverage Changes

Implication

 Profiling on many inputs

and multiple heap size ratios

 Select the top collector for each heap size ratio r = heap size min possible heap size input sensitive 27

Outline

Cross-Input Pred.

 Machine learning technique < input 1 , minSize 1 > ... ...

Regression Trees minSize = f (input)

Details in our paper.

Prediction Acc.

Benchmark Compress

Mpegaudio

Mtrt

Bloat

Fop

Euler

MoDyn

MonteCarlo

Search

Average GC1 99.8

98.1

100 86.1

99.9

98.2

91.3

98.6

98.9

100 97.1

GC2 99.8

97.4

98.1

90.5

100 97.2

92.7

99.0

99.1

100 97.4

GC3 100 98.2

96.3

87.4

99.7

96.6

91.4

98.1

99.4

100 96.7

GC4 100 97.0

96.0

90.5

99.4

97.7

90.4

99.3

99.5

100 97.4

GC5 99.9

98.2

96.8

90.7

99.9

98.3

93.9

98.6

99.3

100 97.5

Conclusions

   But heap size ratio is input-sensitive.

Cross-input adaptation is necessary for GC selection.

 Top garbage collector consistent across inputs for a fixed heap size ratio.

The promise is suggested by the predictability of min heap size ratios.

Acknowledgement

   Steve Blackburn Anonymous reviewers NSF CSR & CCF 32

Questions?

Feng Mao [email protected]

The College of William and Mary Mar 2009

Cluster Intervals

Execution time

Set1:{3, 1 } Top set Set2:{2} Set2:{4, 5}

1 2 3 4 5 Confidence interval for each GC 34

FQA

  The practical use of this technique ?

Profiling overhead and input coverage?

Diagram

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