HardBD & Active'18

HardBD & Active'19

Joint Workshop of HardBD (International Workshop on Big Data Management on Emerging Hardware)
          and Active (Workshop on Data Management on Virtualized Active Systems)

To be Sponsored by and Held in Conjunction with ICDE 2019

April 8, 2019 in Macau SAR, China

bullet Description
bullet Topics
bullet Submission
bullet Important Dates
bullet Program
bullet Keynote
bullet Industry Talk
bullet Organizers
bullet PC Members
bullet Sponsor


HardBD : Data properties and hardware characteristics are two key aspects for efficient data management. A clear trend in the first aspect, data properties, is the increasing demand to manage and process Big Data in both enterprise and consumer applications, characterized by the fast evolution of Big Data Systems, such as Key-Value stores, Document stores, Graph stores, Spark, MapReduce/Hadoop, Graph Computation Systems, Tree-Structured Databases, as well as novel extensions to relational database systems. At the same time, the second aspect, hardware characteristics, is undergoing rapid changes, imposing new challenges for the efficient utilization of hardware resources. Recent trends include massive multi-core processing systems, high performance co-processors, very large main memory systems, persistent main memory, fast networking components, big computing clusters, and large data centers that consume massive amounts of energy. Utilizing new hardware technologies for efficient Big Data management is of urgent importance.

Active : Existing approaches to solve data-intensive problems often require data to be moved near the computing resources for processing. These data movement costs can be prohibitive for large data sets. One promising solution is to bring virtualized computing resources closer to data, whether it is at rest or in motion. The premise of active systems is a new holistic view of the system in which every data medium and every communication channel become compute-enabled. The Active workshop aims to study different aspects of the active systems' stack, understand the impact of active technologies (including but not limited to hardware accelerators such as SSDs, GPUs, FPGAs, and ASICs) on different applications workloads over the lifecycle of data, and revisit the interplay between algorithmic modeling, compiler and programming languages, virtualized runtime systems and environments, and hardware implementations, for effective exploitation of active technologies.

HardBD & Active'19 : Both HardBD and Active are interested in exploiting hardware technologies for data-intensive systems. The aim of this half-day joint workshop is to bring together researchers, practitioners, system administrators, and others interested in this area to share their perspectives on exploiting new hardware technologies for data-intensive workloads and big data systems, and to discuss and identify future directions and challenges in this area. The workshop aims at providing a forum for academia and industry to exchange ideas through research and position papers.

[ Go to Top ]


 Topics of interest include but are not limited to:

  • Systems Architecture on New Hardware
  • Data Management Issues in Software-Hardware-System Co-design
  • Main Memory Data Management (e.g. CPU Cache Behavior, SIMD, Lock-Free Designs, Transactional Memory)
  • Data Management on New Memory Technologies (e.g., SSDs, NVMs)
  • Active Technologies (e.g., GPUs, FPGAs, and ASICs) in Co-design Architectures
  • Distributed Data Management Utilizing New Network Technologies (e.g., RDMA)
  • Novel Applications of New Hardware Technologies in Query Processing, Transaction Processing, or Big Data Systems (e.g., Hadoop, Spark, NoSQL, NewSQL, Document Stores, Graph Platforms etc.)
  • Novel Applications of Low-Power Modern Processors in Data-Intensive Workloads
  • Virtualizing Active Technologies on Cloud (e.g., Scalability and Security)
  • Benchmarking, Performance Models, and/or Tuning of Data Management Workloads on New Hardware Technologies

[ Go to Top ]

  Submission Guidelines

     We welcome submissions of original, unpublished research papers that are not being considered for publication in any other forum. Papers should be prepared in the IEEE format and submitted as a single PDF file. The paper length should not exceed 6 pages. The submission site is https://cmt3.research.microsoft.com/HardBDActive2019.

     Authors of a selection of accepted papers will be invited to submit an extended version to the Distributed and Parallel Databases (DAPD) journal.

[ Go to Top ]

  Important Dates

Paper submission: January 18, 2019 (Friday) 11:59:00 PM PT
January 25, 2019 (Friday) 11:59:00 PM PT
Notification of acceptance: February 8, 2019 (Friday)
Camera-ready copies: February 22, 2019 (Friday)
Workshop: April 8, 2019 (Monday)

[ Go to Top ]


9:00-9:05am Welcome Messages

9:05-10:00am Session I: Keynote

10:00-10:30am Coffee Break

10:30-11:20am Session II: Research Presentation

11:20-12:00pm Session III: Invited Industry Talk

[ Go to Top ]

  Keynote Talk

From In-Memory Database To In-Memory Computing:
A Long Journey of Architectural Paradigm Shift

Sang K. Cha (Seoul National University)

Sang K. Cha

Bio: Sang Kyun Cha has been a professor of Seoul National University since February, 1992. He is an entrepreneur who founded Transact In Memory, Inc. in Silicon Valley based on his group's research on in-memory database systems. The company was acquired by SAP in November 2005 and his various experiments inside SAP eventually produced a prototype that served as a foundation of SAP HANA. Until January 2014, he served as a founding chief architect of SAP HANA and saw his long-time in-memory database research was finally changing the industry paradigm. He also saw the emerging wave of data-driven digitalization, and returned fully to Seoul National University to start a grand challenge of transforming university education and research with data science. In April of 2014, he founded SNU Big Data Institute which involves around 200 professors across all disciplines. Since then he has been creating multiple experimental vehicles such as SNU Urban Data Science Laboratory for smart cities with the funding from Seoul City government and SNU Digital Transformation Academy teaching data science and engineering to job-seeking youths of non-computer science majors. He has been the longest-serving board member of Korea Telecom and the digitalization chair of Korea Electric Power Co. He also serves on numerous advisory boards of government and industry. He received his Ph.D. from Stanford with his work on natural language question answering over database in 1991. His BS and MS degrees are from SNU in electrical engineering and control and instrumentation engineering, respectively.

[ Go to Top ]

  Industry Talk

Industrial-Strength OLTP using Main Memory and Many Cores

Aharon Avitzur (Huawei)

Aharon Avitzur

Using modern main memory storage and many cores for high performance On-Line Transaction Processing (OLTP) has been discussed extensively in the databases community. In this talk we describe our results in bringing these advances to an industrial-strength OLTP product. We started with an extensive research and experimentation with several alternative concurrency control mechanisms using modern HW. Then we realized our findings in an industrial-grade SW component and integrated it in the PostgreSQL based GaussDB of Huawei. We ended up with 3X-5X performance improvements of a full-blown TPCC benchmark on a range of machines with 32 to 192 cores, as well as on a variety of cluster configurations. In this session we will review our main research findings and some of the challenges in their implementation in the PostgreSQL code base, yielding a leading industrial-strength database product.

Bio: Aharon Avitzur is an expert in database core internals, system architecture and performance engineering. Aharon received his B.Sc. and M.Sc. from Ben-Gurion University, Israel. Before joining Huawei, Aharon was a CTO at SAP Labs and Chief Architect of Better Place Innovation Labs. Today Aharon leads the Database research team of Gauss Labs in Huawei's Tel-Aviv Research Center.

[ Go to Top ]


[ Go to Top ]

  PC Members

  • Manos Athanassoulis, Boston University
  • Sebastian BreƟ, DFKI GmbH
  • Peiquan Jin, Univerisity of Science and Technology of China
  • Wolfgang Lehner, TU Dresden
  • Yinan Li, Microsoft Research
  • Qiong Luo, Hong Kong University of Science and Technology
  • Stefan Manegold, CWI
  • Ilia Petrov, Reutlingen University
  • Eva Sitaridi, Amazon
  • Tianzheng Wang, Simon Fraser University
  • Xiaodong Zhang, Ohio State University

[ Go to Top ]


[ Go to Top ]