Description

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'21 : Both HardBD and Active are interested in exploiting hardware technologies for data-intensive systems. The aim of this one-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

 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

  Important Dates

[ Go to Top ]

  Organizers

• Shimin Chen, Chinese Academy of Sciences, chensm@ict.ac.cn

• Stefan Manegold , CWI, Stefan.Manegold@cwi.nl

• Mohammad Sadoghi, UC Davis, msadoghi@ucdavis.edu

[ Go to Top ]

  PC Members

• Manos Athanassoulis, Boston University
• Bingsheng He, National University of Singapore
• 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
• Ilia Petrov, Reutlingen University
• Eva Sitaridi, Amazon
• Tianzheng Wang, Simon Fraser University
• Xiaodong Zhang, Ohio State University

[ Go to Top ]