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Description
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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.
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Topics
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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
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Submission Guidelines
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Important Dates
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| Paper submission: |
January 18, 2019 (Friday) 11:59:00 PM PT
January 25, 2019 (Friday) 11:59:00 PM PT
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| Notification of
acceptance: |
February 8, 2019 (Friday)
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| Camera-ready
copies: |
February 22, 2019 (Friday)
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| Workshop: |
April 8, 2019
(Monday) |
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Program
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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
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Keynote Talk
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From In-Memory Database To In-Memory Computing:
A Long Journey of Architectural Paradigm Shift
Sang K. Cha (Seoul National University)
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.
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Industry Talk
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Industrial-Strength OLTP using Main Memory and Many Cores
Aharon Avitzur (Huawei)
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.
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Organizers
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PC
Members
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- Manos Athanassoulis, Boston University
- Sebastian Breß, 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
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Sponsor
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