This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and evaluating the performance and energy efficiency of different hardware platforms for big data workloads.

1. BigDataBench: Benchmarking
Big Data Systems
http://prof.ict.ac.cn/jfzhan
INSTITUTE OF COMPUTING TECHNOLOGY
1
Jianfeng Zhan
Computer Systems Research Center, ICT, CAS
CCF Big Data Technology Conference
2013-12-06

2. Why Big Data Benchmarking?
2
Measuring big data architecture and
systems quantitatively
2/

3. What is BigDataBench?

An open source project on big data
benchmarking:
•
3/
http://prof.ict.ac.cn/BigDataBench/
•
6 real-world data sets and 19 workloads
–
•
4V characteristics
–
3/
Extended in near future
Volume, Variety, Velocity, and Veracity

4. 4/
Comparison of Big Data Benchmarking Efforts
4/

5. 5/
Possible Users
Systems
OS for big data
File systems for big data
…………………………..
Architecture
Data
management
Processor
Memory
Networks
…………..
BigDataBench
Performance
optimization
Co-design
5/
…….....
Distributed systems
Scheduling
Programming systems

6. Research Publications

Characterizing data analysis workloads in data
centers. Zhen Jia, Lei Wang, Jianfeng Zhan,
Lixing Zhang, and Chunjie Luo. IISWC 2013
Best paper award
6/

6/
BigDataBench: a Big Data Benchmark Suite
from Internet Services. Lei Wang, Jianfeng
Zhan, et al. HPCA 2014, Industry Session.

7. Outline
7/
1
2
3
Benchmarking Methodology and Decision
Case Study
3
How to Use
5
4
Future Work

8. 8/
BigDataBench Methodology
4V of Big Data
8/
BigDataBench

9. Methodology (Cont’)
9/
Represent
ative Data
Sets
Investigate
Typical
Application
Domains
Data Types
Structured
Semi-structured
Unstructured
Data
Sources
Text data
Graph data
Table data
Extended …
Big Data
Sets
Preserving
4V
data generation tool
preserving data
characteristics
Diverse
Worklo
ads
Application
Types
Basic & Important
Operations and
Algorithms
Extended…
Offline analytics
Realtime analytics
Online services
Represent
Software Stack
Extended…
BigDataBench
Big Data
Workloads

10. 10/
Methodology (Cont’)
4V of Big Data
System and architecture
characteristics
10/
BigDataBench
Similarity
analysis

11. Top Sites on the Web
More details in http://www.alexa.com/topsites/global;0
Search Engine, Social Network and Electronic
Commerce hold 80% page views of all the
11/
Internet service.

12. 12/
12/
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13. 13/
19 Chosen Workloads
Micro Benchmarks
Basic Datastore
Operations
Relational Queries
Application
Scenarios
Search engines
Social networks
E-commerce system
13/

14. Data Generation Tools

Data Sources

Text, Graph and Table
• Six real raw data

14/
Synthetics Data

Scale
• From GB to PB

Features
• Preserve characteristics of real-world data
14/

15. 15/
Naïve Text generator
machine
evaluate
big
system
data
mining
architecture
select word randomly
CPU
cpu
memory
benchmarking
learning
words
documents
following multinomial distribution
Only modeling on the word level;
15/

16. Improved Text generator
16/
topic2
topic1
select topic randomly
machine
evaluate
big
CPU
data
mining
architecture
CPU
select word randomly
benchmarking
topic3
memory system
learning
topics
following multinomial distribution
words
following multinomial distribution under topic2
Modeling on the both topic and word
level
16/
document

17. Outline
17/
1
2
3
Benchmarking Methodology and Decision
Case Study
3
5
4
17/
How to Use
Future Work

18. BigDataBench Case Study
18/
Performance evaluation and Diagnosis
SJTU, and XJTU
Workload
Characterization
Evaluating Big
Data Hardware
Systems
ICT, CAS
SIAT, CAS
USTC, and Florida
International
University
BigDataBench
Networks for
big data
OSU
Energy Efficiency of
Big Data Systems
CNCERT
http://prof.ict.ac.cn/BigDataBench/#users
18/

19. 19/
19/
Testbed

20. 20/
Workloads Analyzed
http://prof.ict.ac.cn/BigDataBench

21. Floating point operation intensity
Data Analytics
Services
21
The total number of (floating point or integer) instructions divided by the
total number of memory access bytes in a run of workload.
Very low floating point operation intensities ( 0.009), two orders of
magnitude lower than the theory number of state-of-practice CPU (1.8)
21/

22. Instruction Breakdown
Data Analytics
Services
 Less floating point operations
22/

More Integer operations

23. 23/
Ratio of Integer to Floating Point
Operations
Data Analytics


Services
The average of big data workloads is 100
Parsec, HPCC and SPECFP (1.4, 1.0, 0.67)

24. Integer operation intensity
Data Analytics
Services
The average integer operation intensity of big data
24/ workloads is 0.49
 That of PARSEC, HPCC, SPECFP is 1.5, 0.38, 0.23


25. Cache Behaviors
Data Analytics
Services
Big data workloads have high L1I misses than HPC workloads
 Data analysis workloads have better L2 cache behaviors than service workloads
25/
except BFS


Big data workloads have good L3 behaviors

26. TLB Behaviors
14
data analysis
5
service
ITLB misses of big data workloads are higher than HPC workloads.
 DTLB misses of big data workloads are higher than HPC workloads.
26/

26/

27. BigDataBench Case Study
27/
Performance evaluation and Diagnosis
SJTU, and XJTU
Big Data workload
Characterization
Evaluating Big
Data Hardware
Systems
ICT, CAS
SIAT, CAS
USTC, and Florida
International
University
BigDataBench
Networks for
big data
OSU
Energy Efficiency of
Big Data Systems
CNCERT
http://prof.ict.ac.cn/BigDataBench/#users

28. Evaluating Big Data Hardware Systems
28/

29. Experimental Platforms
Xeon (Common processor)
Atom ( Low power processor)
Tilera (Many
Brief Comparison
Basic Information
core processor)
CPU Type
Intel Atom D510
Tilera TilePro36
CPU Core
4 cores @
1.6GHz
2 cores @
1.66GHz
36 cores @
500MHz
L1 I/D
Cache
32KB
24KB
16KB/8KB
L2 Cache
29/
Intel Xeon
E5310
4096KB
512KB
64KB

30. Experimental Platforms
Hadoop Cluster
Information
Xeon VS Atom
Xeon VS Tilera
[ 1 Xeon master+7
Comprison
[1 Xeon master+7 Xeon
Xeon slaves ] VS [ 1
(the same logical
slaves] VS [ 1 Xeon
Atom master +7 Atom
core number)
master +1 Tilera slave]
slaves]
Hadoop setting
30/
Following the guidance on Hadoop official
website

31. Benchmark Selection
BigDataBench 1.0
Application
Characteristics
Sort
O(n*log2n)
Integer comparison
WordCount
O(n)
Integer comparison and
calculation
Grep
O(n)
String comparison
Naïve Bayes
O(m*n)
Floating-point computation
SVM
31/
Time
Complexity
O(n3)
Floating-point computation

32. Metrics
Performance: Data processed per second
(DPS)
Energy Efficiency: Application Performance
Power Usage Effectiveness(DPJ)
32/

33. Xeon VS Atom – DPJ
33/

34. Xeon VS Tilera – DPJ
34/

35. Reference
Jing Quan, University of Science and Technology of China, Yingjie
Shi, Chinese Academy of Sciences, Ming Zhao, Florida
International University, Wei Yang, University of Science and
Technology of China.
”The Implications from Benchmarking Three Different Data
Center Platforms”
The First Workshop on Big Data Benchmarks, Performance
Optimization, and Emerging hardware (BPOE 2013) in
conjunction with 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013)
35/

36. Outline
36/
1
2
3
Benchmarking Methodology and Decision
Case Study
3
How to Use
5
4
Future Work

37. BigDataBench Class

For Architecture


For OS


37/
19 among 19
19 among 19
For Runtime environment (Hadoop)

9 of 19 workloads
•Sort, Grep, WordCount, PageRank, Index, Kmeans, Connected Components,
Collaborative Filtering and Naive Bayes.

For Data management

6 of 19 workloads
•Read, Write, Scan, Select Query, Aggregate Query, Join Query
37/

38. BigDataBench Class: data sources

Text related

6 of 19 workloads
•Sort, Grep, WordCount, Index, Collaborative Filtering and Naive Bayes

Graph related

•BFS, PageRank, Kmeans, and Connected Components
38/

4 of 19 workloads
Table related

9 of 19 workloads
•Read, Write, Scan, Select Query, Aggregate Query, Join Query, Nutch Server, Olio
Server and Rubis Server

39. BigDataBench Class: Application Types

Online Services

6 of 19 workloads
• Read, Write, Scan, Nutch server, Olio Server and Rubis server

Offline Analytics
39/ 
10 of 19 workloads
• Sort, Grep, WordCount, BFS, PageRank, Index, Kmeans, Connected Components,
Collaborative Filtering and Naive Bayes.

Realtime Analytics

3 of 19 workloads
• Select Query, Aggregate Query and Join Query

40. BigDataBench Class: Application Domains

Search engine related:

Basic Operations + Search Engine
7 of 19 workloads
•Sort, Grep, WordCount, BFS, PageRank, Index and Nutch Server

Social network related:
Basic Cloud OLTP+ Basic Relational Query+ Social
Network
40/


9 of 19 workloads
•Read, Write, Scan, Select Query, Aggregate Query, Join Query, Olio Server, Kmeans and
Connected Components
E-commerce related:
Basic Cloud OLTP+ Basic Relational Query+ Social
Network

9 of 19 workloads
• Read, Write, Scan, Select Query, Aggregate Query, Join Query, Rubis server, Collaborative
Filtering and Naive Bayes

41. Outline
41/
1
2
3
Benchmarking Methodology and Decision
Case Study
3
How to Use
5
4
Future Work

42. Near Future Work

Multi-media data

Deep learning workloads
42/


42/
HPC
Refine BigDataBench

43. Related Resources

BigDataBench project


BPOE workshop

43/
http://prof.ict.ac.cn/BigDataBench


http://prof.ict.ac.cn/bpoe
A series of workshops on Big Data Benchmarks,
Performance Optimization, and Emerging Hardware
BPOE-4: interaction among OS, architecture, and data
management
• Co-located with ASPLOS 2014

44. BPOE-4 SC
Christos Kozyrakis, Stanford
 Xiaofang Zhou, University of Queensland
 Dhabaleswar K Panda, Ohio State University
 Raghunath Nambiar, Cisco
 Lizy K John, University of Texas at Austin
 Xiaoyong Du, Renmin University of China
44/
 H. Peter Hofstee, IBM Austin Research Laboratory
 Ippokratis Pandis, IBM Almaden Research Center
 Alexandros Labrinidis, University of Pittsburgh
 Bill Jia, Facebook
 Jianfeng Zhan, ICT, Chinese Academy of Sciences


45. 45/
THANKS