This was a presentation made for a paper titled, "Garbage collection auto tuning for java map reduce on multi-cores". This was a work done for the Advanced Virtualization Techniques" module.

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Presentation By:
Pradeeban Kathiravelu
INESC-ID Lisboa
Instituto Superior Técnico,
Universidade de Lisboa
Garbage Collection Auto-Tuning for
Java MapReduce on Multi-Cores
Jeremy Singer George Kovoor Gavin Brown Mikel Luján
University of Glasgow
jeremy.singer@glasgow.ac.uk
kovoor.george@gmail.com
University of Manchester
firstname.lastname@manchester.ac.uk

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Agenda
Introduction

Motivation

Contributions

Evaluation

Scalability

GC Impact

GC Auto Tuning

Related Work

Conclusions

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Introduction

MRJ, A MapReduce Java Framework

for multi-core architectures

Use of memory management auto-
tuning techniques

based on machine learning.

MRJ performance within 10% of
optimal

On 75% of the benchmark tests.

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Why GC Auto Tuning?

MRJ end-user cannot be expected to
perform expert analysis to determine

GC activity reducing MRJ
performance.

How to improve the JVM
configuration.

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Motivation

Efficient adaptation to benchmark-specific
or heap-size-specific anomalies.

Could be installed by the system
administrator

automatically enabled for users that do not
have sufficient permissions to change JVM
parameters.

Enable rapid deployment of MRJ on new
multi-core architecture layouts

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Contributions

A Scalable Java fork/join framework
for MapReduce (MRJ), on a commodity
multi-core platform.

A comprehensive study on the
impact of Java runtime garbage
collection (GC) on MRJ

An auto-tuning approach to optimize
GC for MRJ.

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MRJ

Same application interface as Hadoop.

Only map() and reduce() to be defined.
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Abstracts away all the details of the
parallelization, runtime scheduling, ..

Focus on the application logic.

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Evaluation

Scalability evaluation on a four-core,
hyperthreaded Intel Core i7 processor

Using standard MapReduce
benchmarks.

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Scalability Study

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Scalability of grep
Scalability of grep degrades with increasing numbers of
processors, for small heap sizes

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GC Overhead
GC overhead increases with the number of
processors, more significantly for small heap sizes

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Relative GC Performance

Input Dependent

Application performance different inputs.

Small → Serial.

Medium, Large → Parallel and Concurrent.

Different Heap Sizes.

Application Dependent

Parallel >> Serial & Concurrent ??

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sm: concurrent > parallel ?

sm: Search for a word in an input file.

Death rate = Total garbage collected
Total execution time

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GC Auto Tuning Performance
(relative to optimal policy)

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GC Auto Tuning Performance
(relative to default policy)

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Related Work

The original work on MapReduce [13, 14]
applies to compute-clusters.

Ranger et al. describe the first application of
MapReduce to multi-core processors [31].

Conventional memory management
techniques do not scale to large multi-core
environments [40].

Application of machine learning to Java
runtime performance auto-tuning is a
growing trend [26, 39].

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Conclusions

MRJ: A Java-based framework for MapReduce parallelism

Targets conventional multi-core architectures.

Speedups of up to 6x the default GC policy

10% geometric mean speedup over all benchmarks
with the largest input data sets.

Scalable performance

With increasing # of threads to the underlying Java
fork/join pool

Machine-learning GC auto-tuning policy improving the
runtime performance

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Thank you! Questions?Thank you! Questions?

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Selected References
[13] J. Dean and S. Ghemawat. MapReduce: simplified data processing on large clusters. In Proceedings of
the 6th symposium on operating systems design and implementation, pages 137–150, 2004.
[14] J. Dean and S. Ghemawat. MapReduce: simplified data processing on large clusters. Communications
of the ACM, 51(1):107–113, 2008.
[26] F. Mao and X. Shen. Cross-input learning and discriminative prediction in evolvable virtual machines.
In Proceedings of the 7th
annual IEEE/ACM International Symposium on Code Generation and
Optimization, pages 92–101, 2009.
[31] C. Ranger, R. Raghuraman, A. Penmetsa, G. Bradski, and C. Kozyrakis. Evaluating mapreduce for
multi-core and multiprocessor systems. In Proceedings of the 13th International Symposium on High
Performance Computer Architecture, pages 13–24, 2007.
[39] C. Zhang and M. Hirzel. Online phase-adaptive data layout selection. In ECOOP 2008 Object-Oriented
Programming, pages 309–334, 2008.
[40] Y. Zhao, J. Shi, K. Zheng, H. Wang, H. Lin, and L. Shao. Allocation wall: a limiting factor of Java
applications on emerging multi-core platforms. ACM SIGPLAN Notices, 44(10):361–376, 2009.