Program

Room: Sunburst 2

8:50 - 9:00

Opening Remarks   

9:00 - 10:00

Keynote Talk

Title: Converging HPC and Big Data / AI Infrastructures at Scale with BYTES-Oriented Architectures  

Speaker: Satoshi Matsuoka, Professor, Tokyo Institute of Technology; Fellow, Advanced Institute for Science and Technology (AIST); Director, Joint AIST-Tokyo Tech. Open Innovation Lab on Real World Big Data Computing, Japan

Abstract: With rapid rise and increase of Big Data and AI as a new breed of high-performance workloads on supercomputers, we need to accommodate them at scale, and thus the need for R&D for HW and SW Infrastructures where traditional simulation-based HPC and BD/AI would converge, in a BYTES-oriented fashion. The TSUBAME3 supercomputer at Tokyo Institute of Technology will become online in Aug. 2017, will embody various BYTES-oriented features to allow for such convergence to happen at scale, including significant scalable horizontal bandwidth as well as support for deep memory hierarchy and capacity, along with high flops in low precision arithmetic for deep learning.. TSUBAM3's technologies will be commoditized to construct one of the world’s largest BD/AI focused open and public computing infrastructure called ABCI (AI-Based Bridging Infrastructure), hosted by AIST-AIRC (AI Research Center), the largest public funded AI research center in Japan. The performance of the machine is slated to be well above 130 Petaflops for machine learning, but again the true nature of the machine is being BYTES-oriented, with acceleration in I/O and other data-centric properties desirable for accelerating BD/AI.

Bio: Satoshi Matsuoka has been a Full Professor at the Global Scientifi Information and Computing Center (GSIC), a Japanese national supercomputing center hosted by the Tokyo Institute of Technology, and since 2016 a Fellow at the AI Research Center (AIRC), AIST, the largest national lab in Japan. He received his Ph. D. from the University of Tokyo in 1993. He is the leader of the TSUBAME series of supercomputers, including TSUBAME2.0 which was the first supercomputer in Japan to exceed Petaflop performance and became the 4th fastest in the world on the Top500 in Nov. 2010, as well as the recent TSUBAME-KFC becoming #1 in the world for power efficiency for both the Green 500 and Green Graph 500 lists in Nov. 2013. He is also currently leading several major supercomputing research projects, such as the MEXT Green Supercomputing, JSPS Billion-Scale Supercomputer Resilience, as well as the JST-CREST Extreme Big Data. He has written over 500 articles according to Google Scholar, and chaired numerous ACM/IEEE conferences, most recently the overall Technical Program Chair at the ACM/IEEE Supercomputing Conference (SC13) in 2013. He is a fellow of the ACM and European ISC, and has won many awards, including the JSPS Prize from the Japan Society for Promotion of Science in 2006, awarded by his Highness Prince Akishino, the ACM Gordon Bell Prize in 2011, the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology in 2012, and recently the 2014 IEEE-CS Sidney Fernbach Memorial Award, the highest prestige in the field of HPC.

10:00 - 10:30

Coffee Break

10:30 - 12:00

Session I: High-Performance Graph Processing

Session Chair: Li Zha, Institute of Computing Technology, Chinese Academy of Sciences

Performance Evaluation of Scale-Free Graph Algorithms in Low Latency Non-Volatile Memory   

Manu Shantharam (UCSD), Kieta Iwabuchi (Tokyo Institute of Technology, Japan), Pietro Cicotti (UCSD), Laura Carrington (UCSD), Maya Gokhale (Lawrence Livermore National Laboratory), Roger Pearce (Lawrence Livermore National Laboratory)

High-Performance Data Analytics Beyond the Relational and Graph Data Models with GEMS   

Vito Giovanni Castellana (Pacific Northwest National Laboratory), Marco Minutoli (Pacific Northwest National Laboratory), Shreyansh Bhatt (Pacific Northwest National Laboratory), Khushbu Agarwal (Pacific Northwest National Laboratory), Arthur Bleeker (Pacific Northwest National Laboratory), Daniel Chavarria (Trovares Inc.), David Haglin (Trovares Inc.)

Graph Analytics: Complexity, Scalability, and Architectures   

Peter Kogge (Univ. of Notre Dame)

12:00 - 13:15

Lunch Break

13:15 - 15:00

Session II: Benchmarking and Performance Analysis

Session Chair: Xiaoyi Lu, The Ohio State University

Spark and HPC for High Energy Physics Data Analyses   

Saba Sehrish (Fermi National Accelerator Laboratory), Jim Kowalkowski (Fermi National Accelerator Laboratory)

The Consistency Analysis of Secondary Index on Distributed Ordered Tables   

Houliang Qi (Institute of Computing Technology, Chinese Academy of Sciences), Xu Chang (Institute of Computing Technology, Chinese Academy of Sciences), Xingwu Liu (Institute of Computing Technology, Chinese Academy of Sciences), Li Zha (Institute of Computing Technology, Chinese Academy of Sciences)

BigDataBench-S: An Open-source Scientific Big Data Benchmark Suite   

Xinhui Tian (ICT, CAS, China), Shaopeng Dai (ICT, CAS, China), Wanling Gao (ICT, CAS, China), Zhihui Du (Tsinghua University, China), Rui Ren (ICT, CAS, China), Yaodong Chen (IHEP, CAS, China), Zhifei Zhang (Captial Medical University, China), Zhen Jia (Princeton University), Peijian Wang (Xi'an Jiaotong University, China), Jianfeng Zhan (ICT, CAS, China)

Scalable Architecture for Anomaly Detection and Visualization in Power Generating Assets (Short Paper, 15 mins)   

Paras Jain (Georgia Tech), Chirag Tailor (Georgia Tech), Sam Ford (Georgia Tech), Liexiao Ding (Georgia Tech), Michael Phillips (Georgia Tech), Fang Liu (Georgia Tech), Nagi Gebraeel (Georgia Tech), Duen Horng Chau (Georgia Tech)

15:00 – 15:30

Coffee Break

15:30 – 17:00

Panel: Sunrise or Sunset: Exploring the Design Space of Big Data Software Stack   

Panel Moderator: Jianfeng Zhan, Institute of Computing Technology, Chinese Academy of Sciences, China

Panel Members:

The panel will discuss on the following three important questions the Big Data and HPC communities are facing today:

  • Are big data software stacks mature or not?
  • If yes, what is the new technology challenge? If not, what are the main driving forces for new-generation big data software stacks?
  • What chances are provided for the academia communities in exploring the design spaces of big data software stacks?

17:00 - 17:15

Closing Remarks