most citedA Dwarf-based Scalable Big Data Benchmarking Methodology

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

cs.DC2018

Data Motif-based Proxy Benchmarks for Big Data and AI Workloads

Wanling Gao, Jianfeng Zhan, Lei Wang +8

For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too hug…

cs.DC2018

Data Motifs: A Lens Towards Fully Understanding Big Data and AI Workloads

Wanling Gao, Jianfeng Zhan, Lei Wang +9

The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approach to modelling and characterizing big…

cs.DC2018

ScaleSimulator: A Fast and Cycle-Accurate Parallel Simulator for Architectural Exploration

Ori Chalak, Cai Weiguang, Li Wei +7

Design of next generation computer systems should be supported by simulation infrastructure that must achieve a few contradictory goals such as fast execution time, high accuracy,…

cs.DC2018

Data Dwarfs: A Lens Towards Fully Understanding Big Data and AI Workloads

Wanling Gao, Jianfeng Zhan, Lei Wang +6

The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approach to characterizing big data and AI wo…

cs.AR20172 cited

A Dwarf-based Scalable Big Data Benchmarking Methodology

Wanling Gao, Lei Wang, Jianfeng Zhan +7

Different from the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload, this paper presents a scalable big data benchmarking meth…