application-driven energy-efficient architecture explorations for big data authors: xiaoyan gu rui...
TRANSCRIPT
![Page 1: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/1.jpg)
Application-driven Energy-efficient Architecture
Explorations for Big DataAuthors:
Xiaoyan GuRui HouKe ZhangLixin ZhangWeiping Wang(Institute of Computing Technology,Chinese Academy of Sciences)
Reviewed by-
Siddharth Bhave(University of Washington, Tacoma)
![Page 2: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/2.jpg)
Big Data What is Big Data?
Problems with Big data Energy Consumption Velocity (Operation latency and throughput) Volume (storing capacity) Variety
Managing Big Data Problems Storage Technologies Partitioning Multithreading Parallel Processing Efficient Architecture Hadoop, Map Reduce, MAHOUT Find bottle neck
![Page 3: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/3.jpg)
Introduction
Big data management at architecture level
Two architecture systems Xeon-based cluster Atom Based (micro-server) Cluster
Comparison Based on: - Energy consumption Execution time
![Page 4: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/4.jpg)
Motivation
Ever increasing data.
Energy and Time tradeoff in Xeon and Atom based clusters.
Bottleneck by the processes of compression/decompression
Stateless data processing
![Page 5: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/5.jpg)
Mastiff
Mastiff - Targeted application for performance analysis
Big data processing engine
Columnar store policy
Compression Ratio on 3 GB
data
Compression Ratio
on 100 GB data
Compression Ratio
on 500 GB data
Mastiff 0.54 0.53 0.518
Hadoop HDFS
0.72 0.71 0.7
![Page 6: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/6.jpg)
Working flow of the Mastiff
![Page 7: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/7.jpg)
Methodology
TPC-H test benchmark of queries and concurrent data
1 TB of verification data
2 cases - data load and data query
Fluke NORMA 4000
Average cases and median results are reported
![Page 8: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/8.jpg)
Power and Performance Evaluation
Time on Atom
Cluster (30
nodes)
Time on Xeon
Cluster (30
nodes)
Time on Xeon
Cluster (15
nodes)
Data Load 3.435 hours
1.543 hours
3.242 hours
Data Query 5.877 hours
2.724 hours
5.564 hours
Take 3 cases for time and energy consumption
31 nodes – Atom Cluster (1 master node)
31 nodes – Xeon Cluster (1 master node)
16 nodes – Xeon Cluster (1 master node)
![Page 9: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/9.jpg)
Energy consumption between 30-node Atom Cluster and 30-node Xeon Cluster
Power and Performance Evaluation (cont’d)
![Page 10: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/10.jpg)
Energy consumption between 30-node Atom Cluster and 15-node Xeon Cluster
Power and Performance Evaluation (cont’d)
![Page 11: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/11.jpg)
Time Breakdown in Map Phase
Power and Performance Evaluation (cont’d)
![Page 12: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/12.jpg)
Time Breakdown in Reduce phase
Power and Performance Evaluation (cont’d)
![Page 13: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/13.jpg)
Findings
Atom platform more power efficient
Data compression and decompression occupies significant percentage.
Compression and decompression can be done in software pipeline fashion i.e. with multiple interleave
![Page 14: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/14.jpg)
Propositions
Heterogeneous architecture
Accelerators to perform data compression/decompression
Multiple interleaved compression/decompression
![Page 15: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/15.jpg)
Off-chip and On-chip Accelerators
![Page 16: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/16.jpg)
Multiple Interleaved Tasks
![Page 17: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/17.jpg)
Strengths
A much needed innovative concept
Organized well
Detailed description of energy and time investigation
Already implemented propositions
![Page 18: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/18.jpg)
Weaknesses
Not enough power meters to monitor all nodes
2 assumptions Power of every network router is evenly counted
towards nodes Energy consumption of each node is similar
Results are generalized by Hadoop even if they might not be true for every application.
Vague propsitions implementation
![Page 19: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/19.jpg)
FAWN: A Fast Array of Wimpy Nodes
Authors:
David G. AndersenJason FranklinMichael KaminskyAmar PhanishayeeLawrence TanVijay Vasudevan(Carnegie Mellon University)
![Page 20: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/20.jpg)
High performance, energy efficient system for storage
Large number of small low-performance (hence wimpy) nodes with moderate amounts of local storage
2 parts: FAWN-DS (data store) and FAWN-KV (key value)
Motivation Traditional architecture consumes too much
power I/O bottleneck due to current storage inabilities
Introduction
![Page 21: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/21.jpg)
Features
Pairs of low powered embedded nodes with flash storage
FAWN-DS is the backend that consists of the large number of nodes
Each node has some RAM and flash
FAWN-KV is a consistent, replicated, highly available and high performance key value storage system
![Page 22: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/22.jpg)
FAWN Architecture
![Page 23: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/23.jpg)
Efficient Data Streaming with On-chip Accelerators: Opportunities and Chanllenges
Authors:
Rui HouLixin ZhangMichael C. HuangKun WangHubertus FrankeYi GeXiaotao Chang(University of Rochester)
![Page 24: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/24.jpg)
Motivation
Transistor density increasing day by day
Many cores are integrated in a single die
Advantage of on-chip accelerator instead of using it as PCI
![Page 25: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/25.jpg)
On-Chip Accelerator Architecture
![Page 26: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/26.jpg)
3 types of accelerators Crypto accelerators Decompression accelerators Network offload accelerator
Some common characteristics of data stream in the 3 accelerators
Optimize the power and performance of the accelerators.
Features
![Page 27: Application-driven Energy-efficient Architecture Explorations for Big Data Authors: Xiaoyan Gu Rui Hou Ke Zhang Lixin Zhang Weiping Wang (Institute of](https://reader035.vdocuments.mx/reader035/viewer/2022062717/56649e115503460f94afd1f2/html5/thumbnails/27.jpg)
Thank You