Paper Info
Title
Data Motifs: A Lens Towards Fully Understanding Big Data and AI Workloads.
Abstract
The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approachto modelling and characterizing big data and AI workloads. We consider each big data and AI workload as a pipeline of one or more classes of units of computation performed on different initial or intermediate data inputs. Each class of unit of computation captures the common requirements while being reasonably divorced from individual implementations, and hence we call it a data motif. For the first time, among a wide variety of big data and AI workloads, we identify eight data motifs that take up most of the run time of those workloads, including Matrix, Sampling, Logic, Transform, Set, Graph, Sort and Statistic. We implement the eight data motifs on different software stacks as the micro benchmarks of an open-source big data and AI benchmark suite --- BigDataBench 4.0 (publicly available from http://prof.ict.ac.cn/BigDataBench), and perform comprehensive characterization of those data motifs from perspective of data sizes, types, sources, and patterns as a lens towards fully understanding big data and AI workloads. We believe the eight data motifs are promising abstractions and tools for not only big data and AI benchmarking, but also domain-specific hardware and software co-design.
Year
DOI
Venue
2018
10.1145/3243176.3243190
PACT '18: International conference on Parallel Architectures and Compilation Techniques Limassol Cyprus November, 2018
Keywords
DocType
Volume
Data Motif, Big Data, AI, Workload Characterization
Conference
abs/1808.08512
ISBN
Citations 
PageRank 
978-1-4503-5986-3
1
0.35
References 
Authors
24
12
Name
Order
Citations
PageRank
Wanling Gao129919.12
Jianfeng Zhan276762.86
Lei Wang357746.85
Chunjie Luo443421.86
Daoyi Zheng552.81
Fei Tang6177.36
Biwei Xie7111.81
Chen Zheng82137.64
W. Xu930947.55
Xiwen He10111.15
hainan ye1173.93
Rui Ren12396.66