Paper Info
Title
Understanding Processors Design Decisions for Data Analytics in Homogeneous Data Centers
Abstract
Our global economy increasingly depends on our ability to gather, analyze, link, and compare very large data sets. Keeping up with such big data poses challenges in terms of both computational performance and energy efficiency, and motivates different approaches to explore data center systems and architectures. To better understand the processor design decisions in context of data analytics in data centers, we conduct comprehensive evaluations using representative data analaytics workloads on representative conventional multi-core and many-core processors. After a comprehensive analysis of performance, power, energy efficiency and performance-cost efficiency, we have the following observations: contrasted with the conventional wisdom that uses wimpy many-core processors to improve energy-efficiency, the brawny multi-core processors with SMT (simultaneous multithreading) and dynamic overclocking technologies outperform the counterparts in terms of not only execution time, but also energy-efficiency for most of data analytics workloads in our experiments.
Year
DOI
Venue
2019
10.1109/TBDATA.2017.2758792
IEEE Transactions on Big Data
Keywords
Field
DocType
Program processors,Data analysis,Pipelines,Multicore processing,Clocks
Data science,Data mining,Overclocking,Software analytics,Data analysis,Computer science,Simultaneous multithreading,Analytics,Big data,Multi-core processor,Data center
Journal
Volume
Issue
ISSN
5
1
2332-7790
Citations 
PageRank 
References 
0
0.34
23
Authors
8
Name
Order
Citations
PageRank
Zhen Jia133817.82
Wanling Gao229919.12
Yingjie Shi300.34
Sally A. Mckee41928152.59
Zhenyan Ji523.07
Jianfeng Zhan676762.86
Lei Wang757746.85
Lixin Zhang8212.69