|
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
|
Paper submission: |
January 18, 2021 (Monday) 11:59:00 PM PT
January 25, 2021 (Monday) 11:59:00 PM PT
|
Notification of
acceptance: |
February 8, 2021 (Monday)
|
Camera-ready
copies: |
March 1, 2021 (Monday)
|
Workshop: |
April 19, 2021
(Monday) |
[
Go to Top ]
|
Organizers
|
[
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 ]
|
|