hj-hadoop an optimized mapreduce runtime for multi-core systems yunming zhang advised by: prof. alan...
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HJ-HadoopAn Optimized MapReduce
Runtime for Multi-core Systems
Yunming ZhangAdvised by: Prof. Alan Cox and Vivek SarkarRice University
Social Informatics
Slide borrowed from Prof. Geoffrey Fox’s presentation
Big Data Era
20 PB/day
100 PB media
120 PB cluster
Bing ~ 300 PB
2008
2012
2011
2012
Slide borrowed from Florin Dinu’s presentation
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MapReduce Runtime
Job Starts
Map
…….
Reduce
JobEnds
Figure 1. Map Reduce Programming Model
Map
MapReduce
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Hadoop Map Reduce
• Open source implementation of Map Reduce Runtime system– Scalable– Reliable– Available
• Popular platform for big data analytics
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Habanero Java(HJ)
• Programming Language and Runtime Developed at Rice University
• Optimized for multi-core systems– Lightweight async task– Work sharing runtime– Dynamic task parallelism– http://habanero.rice.edu
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Kmeans
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Kmeans
Topics
To be Classified Documents
Kmeans is an application that takes as input a large number of documents and try to classify them into different topics
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Machines …
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Topics
Machines …
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Topics
Topics
Machines …
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Topics
Topics
Topics
Machines …
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
Duplicated In-memory Cluster Centroids
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics 1x
Topics 1x
Topics 1x
Topics 1x
Machines …
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Memory Wall
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
160
180
200
KMeans Throughput Benchmark
Hadoop
Topics data size (MB) with 4KB/topic
Num
ber o
f top
ics/
Tim
e in
min
We used 8 mappers from 30 -80 MB, 4 mappers for 100 – 150 MB, 2 mappers for 180 – 380 for sequential Hadoop.
cluster size(MB)
HadoopFull GC calls
30 3,54250 4,39070 5,18680 1,108,888
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Memory Wall
• Hadoop’s approach to the problem– Increase the memory available to each Map Task
JVM by reducing the number of map tasks assigned to each machine.
Kmeans using Hadoop
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To be classified documents Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
…. Machines …
Topics 2x
Topics 2x
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Memory Wall
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
160
180
200
KMeans Throughput Benchmark
Hadoop
Topics data size (MB) with 4KB/topic
Num
ber o
f top
ics/
Tim
e in
min
Decreased throughput due to reduced number of map tasks per machine
We used 8 mappers from 30 -80 MB, 4 mappers for 100 – 150 MB, 2 mappers for 180 – 380 for sequential Hadoop.
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HJ-Hadoop Approach 1
To be classified documents
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Computation Memory
Machine1Map task in a JVM
Topics 4x
Machines …
Dynamic chunking
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HJ-Hadoop Approach 1
To be classified documents
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Computation Memory
Machine1Map task in a JVM
Topics 4x
Machines …
Dynamic chunking
NoDuplicated In-memory Cluster Centroids
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Results
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
160
180
200
KMeans Throughput Benchmark
HJ-HadoopHadoop
Topics data size (MB) with 4KB/topic
Num
ber o
f top
ics/
Tim
e in
min
We used 2 mappers for HJ-Hadoop
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Results
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
160
180
200
KMeans Throughput Benchmark
HJ-HadoopHadoop
Topics data size (MB) with 4KB/topic
Num
ber o
f top
ics/
Tim
e in
min
We used 2 mappers for HJ-Hadoop
Process 5x Topics efficiently
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Results
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
160
180
200
KMeans Throughput Benchmark
HJ-HadoopHadoop
Topics data size (MB) with 4KB/topic
Num
ber o
f top
ics/
Tim
e in
min
We used 2 mappers for HJ-Hadoop
4x throughput improvement
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HJ-Hadoop Approach 1
To be classified documents
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Computation Memory
Machine1Map task in a JVM
Topics 4x
Machines …
Dynamic chunking
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HJ-Hadoop Approach 1Only a single thread reading input
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Computation Memory
Machine1Map task in a JVM
Topics 4x
Machines …
Dynamic chunking
Kmeans using Hadoop
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Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Topics
Topics
Topics
Machines …
Four threads reading input
HJ-Hadoop Approach 2
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Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Machines …
Four threads reading input
HJ-Hadoop Approach 2
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Computation Memory
Machine 1Map task in a JVM
slice1
slice2
slice3
slice4
slice5
slice6
slice7
slice8
….
Topics
Machines …
Four threads reading input
NoDuplicated In-memory Cluster Centroids
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Trade Offs between the two approaches
• Approach 1– Minimum memory overhead– Improved CPU utilization with small task granularity
• Approach 2– Improved IO performance– Overlap between IO and Computation
• Hybrid Approach– Improved IO with small memory overhead– Improved CPU utilization
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Conclusions
• Our goal is to tackle the memory inefficiency in the execution of MapReduce applications on multi-core systems by integrating a shared memory parallel model into Hadoop MapReduce runtime– HJ-Hadoop can be used to solve larger problems
efficiently than Hadoop, processing process 5x more data at full throughput of the system
– The HJ-Hadoop can deliver a 4x throughput relative to Hadoop processing very large in-memory data sets
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