Paper Info
Title
Amdahl's Law in Big Data Analytics: Alive and Kicking in TPCx-BB (BigBench)
Abstract
Big data, specifically data analytics, is responsible for driving many of consumers' most common online activities, including shopping, web searches, and interactions on social media. In this paper, we present the first (micro)architectural investigation of a new industry-standard, open source benchmark suite directed at big data analytics applications—TPCx-BB (BigBench). Where previous work has usually studied benchmarks which oversimplify big data analytics, our study of BigBench reveals that there is immense diversity among applications, owing to their varied data types, computational paradigms, and analyses. In our analysis, we also make an important discovery generally restricting processor performance in big data. Contrary to conventional wisdom that big data applications lend themselves naturally to parallelism, we discover that they lack sufficient thread-level parallelism (TLP) to fully utilize all cores. In other words, they are constrained by Amdahl's law. While TLP may be limited by various factors, ultimately we find that single-thread performance is as relevant in scale-out workloads as it is in more classical applications. To this end we present core packing: a software and hardware solution that could provide as much as 20% execution speedup for some big data analytics applications.
Year
DOI
Venue
2018
10.1109/HPCA.2018.00060
2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)
Keywords
Field
DocType
big data analytics,Hadoop,BigBench,TPCx BB,Amdahl's law,core packing
Data science,Data analysis,Suite,Computer science,Amdahl's law,Usability,Parallel computing,Data type,Big data,Benchmark (computing),Speedup
Conference
ISSN
ISBN
Citations 
1530-0897
978-1-5386-3660-2
1
PageRank 
References 
Authors
0.35
15
4
Name
Order
Citations
PageRank
Daniel Richins1162.40
Tahrina Ahmed210.35
Russell M. Clapp310.35
Vijay Janapa Reddi42931140.26