Tutorial: BigDataBench 4.0

--- an Open Source Big Data and Artificial Intelligence (AI) Benchmark suite

This tutorial is aimed at presenting BigDataBench 4.0---an open source big data and artificial intelligence (AI) benchmark suite. We would like to introduce four benchmark categories in BigDataBench 4.0, including micro benchmarks, component benchmarks, end-to-end application benchmarks and simulation benchmarks for simulator-based research which largely reduce running time while keep high accuracy.

Location and Date

We will give a tutorial on BigDataBench at ASPLOS 2018 in Williamsburg, VA, USA.

March 24, 2018 (Saturday),09:00 - 12:00 (Half Day)

ROOM:TBD

Organizers and Presenters

Organizer: Jianfeng Zhan Chinese Academy of Sciences, and University of Chinese Academy of Sciences
Presenter: Jianfeng Zhan Chinese Academy of Sciences, and University of Chinese Academy of Sciences
Presenter: Chen Zheng Chinese Academy of Sciences, and University of Chinese Academy of Sciences
Presenter: Wanling Gao Chinese Academy of Sciences, and University of Chinese Academy of Sciences

Abstract

As a multi-discipline research and engineering effort, i.e., system, architecture, and data management, from both industry and academia, BigDataBench (IISWC’13, HPCA’14, PACT’16, TPDS’17) is an open-source big data and AI bartificial intelligence (AI) benchmark suite. The BigDataBench 4.0 version (released soon) models typical and important big data and AI application domains.
BigDataBench 4.0 provides 40 workloads with diverse implementations for each workload, including types of offline statistics, graph, streaming and data warehouse. For different benchmarking requirements, we provide multiple benchmark categories (micro benchmarks, component benchmarks, application benchmarks and simulation benchmarks) for system evluation, architecture evluation, and simulation-based micro-architectural evluation.

Schedule

09:00-09:30 What is a good benchmark?
09:30-10:00 A scalable dwarf-based big data benchmarking methodology
10:00-10:30 — Coffee break —
10:30-11:00 Introduction of BigDataBench 4.0 – including AI workloads
11:00-11:30 How to use BigDataBench
11:30-12:00 Big data proxy benchmarks for simulation

Biographies

Jianfeng Zhan
Jianfeng Zhan is a Professor of Computer Science and Engineering at Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences. His research interests include computer architecture, operating systems, data management, parallel and distributed systems. He has published over 100 papers in major journals and international conferences related to these research areas, and filed 40 patents. From 2004 to 2010, he leaded the R&D efforts of innovative cluster and cloud systems software for the dawning-series super computers (which ranked top 2 and top 10 on the top 500 list in 2010 and 2004, respectively). Among them, GridView was transferred to Sugon, which is a premier supercomputing company in China, and becomes its popular software product. Currently, he is leading the research efforts for datacenter and big data software stacks, including BigDataBench---an open source big data benchmarking project, and RainForest--- an operating system for warehouse-scale computing. He received the second-class Chinese National Technology Promotion Prize in 2006, the Distinguished Achievement Award of the Chinese Academy of Sciences in 2005, IISWC Best paper award in 2013, and Huawei Contribution Prize in 2013, respectively. More details about Prof. Zhan are available at http://prof.ict.ac.cn/jfzhan.

Chen Zheng
Chen Zheng is a post doc researcher at the Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences. His research focuses on Operating System, Virtualization, benchmarks, and data center workload characterization. He received his PHD degree in 2017 from Institute of Computing Technology in China.

Wanling Gao
Wanling Gao is a Ph.D candidate in computer science at the Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences. Her research interests focus on big data benchmark and big data analytics. She received her B.S. degree in 2012 from Huazhong University of Science and Technology.

Relate Links

BigDatabench http://prof.ict.ac.cn/BigDataBench/