Michael Wrinn Research Program Director, University Research Office, Intel Corporation Jason Dai Engineering Director and Principal Engineer, Intel Corporation

1. Big Data Beyond Hadoop*:
Research Directions for the Future
Jason Dai, Engineering Director and Principal
Engineer, Software and Solutions Group
Michael Wrinn, PhD, Research Program Director,
University Research Office, Intel Labs
ACAS002

2. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on big data research
• Efficient in-memory implementations of map reduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
The PDF for this Session presentation is available from our
Technical Session Catalog at the end of the day at:
intel.com/go/idfsessionsBJ
URL is on top of Session Agenda Pages in Pocket Guide
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3. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on bit data research
• Efficient in-memory implementations of mapreduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
3

4. What is Big Data?
Big Data is data that is too big, too fast or too hard for
existing systems and algorithms to handle
• Too Big
– Terabytes going on petabytes
– Smart (not brute force) massive parallelism required
• Too Fast
– Sensor tagging everything creates a firehose
– Ingest problem
• Too Hard
– Complex analytics are required (e.g., to find patterns, trends
and relationships)
– Need to combine diverse data types (No Schema, Uncurated,
Inconsistent Syntax and Semantics)
Data should be a resource, not a load
Existing data processing tools are not a good fit
4 Samuel Madden ISTC Director and Professor EECS, MIT

5. Example: Web Analytics
Large web enterprises:
thousands of servers,
millions of users, and
terabytes per day of “click data”
Not just simple reporting:
e.g., in real time, determine what users are likely to do next, or
what ad to serve them, or
which user they are most similar to
Existing analytics systems either:
do not scale to required volumes, or
do not provide required sophistication
5 Samuel Madden ISTC Director and Professor EECS, MIT

6. Example: Sensor Analytics
Smartphone providers
tolling agencies
municipalities
insurance companies
doctors
businesses
Capturing massive streams of video,
position, acceleration, and other
data from phones and other devices
This data needs to be stored, processed,
and mined, e.g., to measure traffic, driving risk,
or medical prognosis
6 Samuel Madden ISTC Director and Professor EECS, MIT

7. Hadoop* in the Big Data Ecosystem
Era of Data Exchange
Cost-effective
Vertical
Solutions
eCommerce Healthcare Manufacturing Energy - Scientific FSI
Traditional Business Solutions New Analytics Models
Business Processing Innovation
In-Memory DB – Integrated
Analytics –Systems & Appliances
EXALYTICS
Big Data
Compute EP
Platform
Topology EX MIC
Fabric
Traditional Business Solutions Connecting to New Analytics
Models for Real-time Value Opportunities
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8. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on big data research
• Efficient in-memory implementations of map reduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
8

9. Intel Activity Landscape on Big Data
Apps Corporate Data Solution Programs for Big Data and Analytics
Services Trust Broker Location Based
Healthcare, Telco, …
(McAfee*) Service (Telmap)
Data Usage
Visualization
End user tools
Big Data HiTune* and other
tools for Hadoop
Market
Internet of
Sizing and Things / M2M
Analytics Segmentation (Intel Labs &
(with Bain) university
Distributed Machine Business Video Analytics
collaborators)
Learning Intelligence
(university and Hadoop*
Data Management collaborators) Distributed Video
and Processing Analytics
Hadoop Distribution & Service
Hadoop performance & Distributed
Data Delivery Computing Architecture Architecture (Guavus)
and Storage Platform
Microservers End-to-End Data
Security
Compression & Federated Device
Large Object Architecture
Decompression IPs Storage
Intel Intel Intel Intel Others
Architecture Software Labs IT
9

10. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on big data research
• Efficient in-memory implementations of map reduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
10

11. Algorithms, Machines, People (AMPLab)
Adaptive/Active
Machine Learning
and Analytics
Massive
and
Diverse
Data
CrowdSourcing/
Human Cloud Computing
Computation
All software released as BSD Open Source
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12. Berkeley Data Analysis System
• Mesos*: resource management platform
• SCADS: scale-independent storage systems
• PIQL, Spark: processing frameworks
SharkQuery Languages * …
Higher Hive* Pig /
PIQL …
Processing Frameworks *
MPI …
Spark Hadoop
Mesos
Resource Management
SCADS Storage HDFS
3rd party AMPLab
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13. Data Center Programming: Spark
• In-memory cluster computing framework for
applications that reuse working sets of data
– Iterative algorithms: machine learning, graph
processing, optimization
– Interactive data mining: order of magnitude faster
than disk-based tools
• Key idea: RDDs “resilient distributed datasets”
that can automatically be rebuilt on failure
– Keep large working sets in memory
– Fault tolerance mechanism based on “lineage”
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14. Spark: Motivation
Complex jobs, interactive queries and online
processing all need one thing that Hadoop* MR
lacks:
• Efficient primitives for data sharing
Query 1
Stage 1
Stage 2
Stage 3
Job 1
Job 2
Query 2 …
Query 3
Iterative job Interactive mining Stream processing
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15. Xfer and Sharing in Hadoop*
HDFS HDFS HDFS HDFS
read write read write
Iter. 1 Iter. 2 . . .
Input
HDFS Query 1 Result 1
read
Query 2 Result 2
Query 3 Result 3
Input
. . .
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16. Spark: In-Memory Data Sharing
Iter. 1 Iter. 2 . . .
Input
Query 1
One-time
processing
Query 2
Query 3
Input Distributed
memory . . .
16

17. Introducing Shark
• Spark + Hive* (the SQL in NoSQL)
• Utilizes Spark’s in-memory RDD caching
and flexible language capabilities: result
reuse, and low latency
• Scalable, fault-tolerant, fast
• Query Compatible with Hive
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18. Benchmarks: Query 1
30GB input table
SELECT * FROM grep WHERE field LIKE ‘%XYZ%’;
18

19. Benchmark: Query 2
5 GB input table
SELECT pagerank, pageURL FROM rankings WHERE
pagerank > 10;
*
19

20. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on big data research
• Efficient in-memory implementations of map reduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
20

21. Data Parallelism (MapReduce)
2
1 8
6
4 1 2 8
3 2
7
4
2 4
8 1 4
CPU 1 CPU 2 CPU 3 CPU 4
7
4
2 5
. . . . . .
1
5
9 5
3 4
9 3 4
3 8
Solve a huge number of independent subproblems
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22. MapReduce for Data-Parallel ML
• Excellent for large data-parallel tasks!
Data-Parallel Graph-Parallel
MapReduce Is there more to
Feature Cross Machine
Extraction Validation
Computing Sufficient
Learning
?
Statistics
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23. Machine Learning Pipeline
Extract Graph
Features Formation Structured
Data Value
Machine
from
Learning Data
Algorithm
similar face
faces belief
images faces labels
propagation
docs important doc
shared LDA
words topics
movie words
side collaborative
ratings rated movie
info filtering
movies recommend
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24. Parallelizing Machine Learning
Extract Graph
Features Formation Structured
Data Value
Machine
from
Learning Data
Algorithm
Graph Ingress Graph-Structured
mostly data-parallel Computation
graph-parallel
24

25. Addressing Graph-Parallel ML
Data-Parallel Graph-Parallel
Map Reduce Graph-Parallel Abstraction
Map Reduce?
Feature Cross Graphical Semi-Supervised
Extraction Validation Models Learning
Gibbs Sampling Label Propagation
Computing Sufficient Belief Propagation CoEM
Statistics Variational Opt.
Collaborative Data-Mining
Filtering PageRank
Triangle Counting
Tensor Factorization
25

26. Example:
Never Ending Learner Project (CoEM)
16
Better
14
Optimal
Hadoop*
12
95 Cores 7.5 hrs
10
GraphLab 16 Cores 30 min
Speedup
8
GraphLab CoEM
Distributed 6x fewer CPUs!
32 EC2 80 secs
6 15x Faster!
GraphLab
4
machines
2
0
0.3% of Hadoop time
0 2 4 6 8 10 12 14 16
Number of CPUs
26

27. Example: PageRank
5.5 hrs.
Hadoop*
1 hr.
Twister*
8 min.
GraphLab
40M Webpages, 1.4 Billion Links
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28. Agenda
• Big data and the Hadoop* ecosystem
• Intel university collaborations on big data research
• Efficient in-memory implementations of map reduce
• Efficient graph algorithms for analytics
• Intel’s efforts moving research to production
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29. Intel’s Efforts on Hadoop*
• Intel® Distribution for Apache Hadoop*
– Performance, security and management
– Downloadable from http://hadoop.intel.com/
• Intel’s open source initiatives for Hadoop
– HiBench: comprehensive Hadoop benchmark suite
 https://github.com/intel-hadoop/hibench
– Project Panthera: efficient support of standard SQL features
on Hadoop
 https://github.com/intel-hadoop/project-panthera
– Project Rhino: enhanced data protection for the Apache
Hadoop ecosystem
 https://github.com/intel-hadoop/project-rhino
– Graph Builder: scalable graph construction using Hadoop
 http://graphlab.org/intel-graphbuilder/
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30. Using Spark/Shark for In-memory, Real-time
Data Analysis
• Use case 1: ad-hoc & interactive queries
– Interactive queries (exploratory ad-hoc queries, BI charting &
mining)
– Similar projects: Google* Dremel, Facebook* Peregrine,
Cloudera* Impala, Apache* Drill, etc. (several seconds latency)
– Use Shark/Spark to achieve close to sub-second latency for
interactive queries
• Use case 2: in-memory, real-time analysis
– Iterative data mining, online analysis (e.g., loading table into
memory for online analysis, caching intermediate results for
iterative machine learning)
– Similar projects: Google PowerDrill
– Use Shark/Spark to reliably load data in distributed memory for
online analysis
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31. Using Spark/Shark for In-memory, Real-time
Data Analysis
• Use case 3: stream processing
– Streaming analysis, CEP (e.g., intrusion detection, real-time
statistics, etc.)
– Similar projects: Twitter* Storm, Apache* S4, Facebook* Puma
– Use Spark streaming for stream processing
 Better reliability
 Unified framework and application for both offline, online &
streaming analysis
• Use case 4: graph-parallel analysis & machine learning
– Use case: graph algorithms, machine learning (e.g., social
network analysis, recommendation engine)
– Similar projects: Google* Pregel, CMU GraphLab*
– Use Bagel (Pregel on Spark) for graph parallel analysis &
machine learning on Spark
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32. Summary
• MapReduce as implemented in Hadoop* is extremely
useful, but:
– In-memory implementations show serious advantages
– Graph algorithms may be more suitable for problem at hand
• Intel continues to work with university researchers
• Intel works to implement research results into
production environments
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33. Call to Action
• Use Intel Research results in your own big
data efforts!
• Work with us on the next-gen, in-memory,
real-time analysis using Spark/Shark
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