HardBD & Active'22

HardBD & Active'24

Joint International Workshop on Big Data Management on Emerging Hardware
and Data Management on Virtualized Active Systems

To be Sponsored by and Held in Conjunction with ICDE 2024

May 13, 2024 in Utrecht, Netherlands

bullet Description
bullet Topics
bullet Submission
bullet Important Dates
bullet Program
bullet Keynote
bullet Organizers
bullet PC Members
bullet Previous Workshops


HardBD & Active'24 will be a one-day workshop co-located with ICDE'24. The aim of this one-day workshop is to bring together researchers, practitioners, system administrators, and others interested to share their perspectives on exploiting new hardware technologies for data-intensive 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.

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 Topics of interest include but are not limited to:

  • Data Management with Software-Hardware-System Co-design
  • Main Memory Data Management (e.g. Multi-core, Cache, SIMD)
  • Active Technologies (e.g., GPU, FPGA, and ASIC) in Co-design Architectures
  • Data Management on New Memory Technologies (e.g., SSD, NVM, HBM, PIM)
  • Distributed Data Management Utilizing New Network Technologies (e.g., RDMA, CXL)
  • Data Management on the Cloud (e.g., Scalability and Security, Disaggregation)
  • Secure Data Management Exploiting Trusted Execution Environment
  • Novel Applications of New Hardware Technologies in Query Processing, Transaction Processing, or Big Data Systems (e.g., SQL/NoSQL/NewSQL Databases, Hadoop/Spark, Blockchains, etc.)
  • Benchmarking, Performance Models, and/or Tuning of Data Management Workloads on Modern Hardware

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  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We welcome submissions of original, unpublished research papers that are not being considered for publication in any other forum.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 excluding the bibliography. The submission site is https://cmt3.research.microsoft.com/HardBDActive2024.

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  Important Dates

Paper submission: January 26 February 16, 2024 (Friday) 11:59:00 PM PT
Notification of acceptance: February 23 March 8, 2024 (Friday)
Camera-ready copies: March 8 March 22, 2024 (Friday)
Workshop: May 13, 2024 (Monday)

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9:00-9:10 Welcome Message

9:10-10:10 Keynote Talk 1

10:10-10:30 Coffee Break

10:30-11:30 Keynote Talk 2

12:00-13:30 Lunch

13:30-15:00 Research Session

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  Keynote Talks

Gustavo Alonso      What is More Difficult, to Build or to Program a Hard DB?

Gustavo Alonso
ETH Zurich

Abstract: Computing platforms are evolving rapidly along many dimensions: processors, specialization, disaggregation, acceleration, smart memory and storage, etc. Many of these developments are being driven by data science but very few existing engines and data processing platforms make us of modern hardware. One reason is the deluge of possible configurations and deployment options, most of them too new to have a precise idea of their performance implications and lacking proper support in the form of tools and platforms that can manage the underlying diversity. This growing heterogeneity opens up many opportunities but also raises significant challenges. In the talk I will describe our efforts to explore the possibilities that modern hardware opens for data management and discuss a system we are building for data processing on heterogeneous computing platforms that has as its main goal to effectively cope with the great variety of emerging hardware.

Bio: Gustavo Alonso is a professor in the Department of Computer Science of ETH Zurich where he is a member of the Systems Group. His research interests include data management, distributed systems, cloud computing architecture, and hardware acceleration through reconfigurable computing. Gustavo has received 4 Test-of-Time Awards for his research in databases, software runtimes, middleware, and mobile computing. He is an ACM Fellow, an IEEE Fellow, a Distinguished Alumnus of the Department of Computer Science of UC Santa Barbara, and has received the Lifetime Achievements Award from the European Chapter of ACM SIGOPS (EuroSys).

Jianjun Chen      Hybrid Data Analytical Processing and Hardware Acceleration Research at ByteDance

Jianjun Chen
Director of Infrastructure System Lab, ByteDance US

Abstract: Three distinctive types of data processing workloads, including OLTP, OLAP and Serving, are commonly seen in real production systems at ByteDance. In the past, dedicated systems have been built and used to process them separately, forcing the same data to be ETLed into multiple systems, causing data consistency problems, low data freshness, wasting resources, and increasing learning and maintenance costs. In this talk, we describe our recent journey of building: 1) ByteHTAP [VLDB 2022], an HTAP (hybrid transactional and analytical processing) system with high data freshness and strong data consistency. 2) Krypton [VLDB 2023], a competitive cloud-native HSAP (hybrid serving and analytical processing) system that provides excellent elasticity, query performance and high data freshness. In addition, we briefly talk about some sampled recent work in hardware acceleration research at ByteDance, including: 1) Accelerating Cloud-Native Databases with Distributed PMem Stores [ICDE 2023]; 2) Exploring Performance and Cost Optimization with ASIC-Based CXL Memory [EuroSys 2024].

Bio: Dr. Jianjun Chen is Director of Infrastructure System Lab at ByteDance US, where he leads a team of top-notch researchers and software engineers to work on cutting edge technologies in the infrastructure system related areas, including but not limited to database, storage, computing, networking, ML, hardware/software codesign etc. Before that, he was a technical VP of Huawei US Silicon Valley R&D Center, leading advanced database research and development in the Huawei database group. Dr. Chen received his Ph.D in 2001 from the Computer Sciences department of University of Wisconsin, Madison and is a recipient of the SIGMOD 10 year Test-Time award in 2010 for his visionary work in scalable continuous query processing, as part of his Ph.D dissertation research.

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  PC Members

  • Bingsheng He, National University of Singapore
  • Wolfgang Lehner, TU Dresden
  • Yinan Li, Microsoft Research
  • Ilia Petrov, Reutlingen University
  • Thamir Qadah, Umm Al-Qura University
  • Kai-Uwe Sattler, TU Ilmenau
  • Evangelia Sitaridi, NVIDIA
  • Rebecca Taft, Cockroach Labs
  • Tianzheng Wang, Simon Fraser University
  • Xuan Zhou, East China Normal University

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