The objective of this one-day workshop is to investigate
opportunities in accelerating analytics workloads and data
management systems which include
traditional OLTP, data warehousing/OLAP, HTAP, ETL, Streaming/Real-time Processing,
Business Analytics (including machine learning and deep learning workloads), and Data Visualization, using modern processors (e.g., commodity and specialized
Multi-core, Many-core, GPUs, and FPGAs), processing systems (e.g., hybrid,
massively-distributed clusters, and cloud based distributed computing
infrastructure), networking infrastructures (e.g., RDMA over
InfiniBand), memory and storage systems (e.g., storage-class Memories
like SSDs, active memories, NVRams, and
Phase-change Memory), multi-core and
distributed programming paradigms like CUDA/OpenCL, MPI/OpenMP, and MapReduce/Spark, and integration with data-science frameworks such as
Sklearn, TensorFlow, or PyTorch. Exploratory topics such as DNA-based storage or quantum
algorithms are also within the preview of the ADMS workshop.
The current data management scenario is characterized by the following trends: traditional OLTP and
OLAP/data warehousing systems are being used for increasing complex workloads (e.g., Petabyte of data,
complex queries under real-time constraints, etc.); applications are becoming far more distributed, often
consisting of different data processing components; non-traditional domains such as bio-informatics, social
networking, mobile computing, sensor applications, gaming are generating growing quantities of data of
different types; economical and energy constraints are leading to greater consolidation and virtualization
of resources; and analyzing vast quantities of complex data is becoming more important than traditional
transactional processing.
At the same time, there have been tremendous improvements in the CPU
and memory technologies. Newer processors are more capable in the
compute and memory capabilities, are power-efficient, and are optimized for multiple application
domains. Commodity systems are increasingly using
multi-core processors with more than 6 cores per chip and
enterprise-class systems are using processors with at least 32 cores per
chip. Specialized multi-core processors such as the GPUs have
brought the computational capabilities of supercomputers to cheaper
commodity machines. On the storage front, FLASH-based solid state
devices (SSDs) are becoming smaller in size, cheaper in price, and larger in
capacity. Exotic technologies like Phase-change memory are on the
near-term horizon and can be game-changers in the way data is stored
and processed.
In spite of the trends, currently there is limited usage of
these technologies in data management domain. Naive exploitation of
multi-core processors or SSDs often leads to unbalanced systems. It
is, therefore, important to evaluate applications in a holistic manner
to ensure effective utilization of CPU and memory
resources. This workshop aims to understand impact of modern
hardware technologies on accelerating core components of data
management workloads. Specifically, the workshop hopes to explore
the interplay between overall system design, core algorithms, query optimization
strategies, programming approaches, performance modelling and
evaluation, etc., from the perspective of data management applications.
The suggested topics of
interest include, but are not restricted to:
- Hardware and System Issues in Domain-specific Accelerators
- New Programming Methodologies for Data Management Problems on Modern Hardware
- Query Processing for Hybrid Architectures
- Large-scale I/O-intensive (Big Data) Applications
- Parallelizing/Accelerating Machine Learning/Deep Learning Workloads
- Autonomic Tuning for Data Management Workloads on Hybrid Architectures
- Algorithms for Accelerating Multi-modal Multi-tiered Systems
- Energy Efficient Software-Hardware Co-design for Data Management Workloads
- Parallelizing non-traditional (e.g., graph mining) workloads
- Algorithms and Performance Models for modern Storage Sub-systems
- Exploitation of specialized ASICs
- Novel Applications of Low-Power Processors and FPGAs
- Exploitation of Transactional Memory for Database Workloads
- Exploitation of Active Technologies (e.g., Active Memory, Active
Storage, and Networking)
- New Benchmarking Methodologies for Accelerated Workloads
- Applications of HPC Techniques for Data Management Workloads
- Acceleration in the Cloud Environments
- Accelerating Data Science/Machine Learning Workloads
- Exploratory topics such as DNA-storage, Quantum Technologies
- Performant-efficient AI Pipelines,
Meena
Arunachalam, Intel
Dr. Meena
Arunachalam is
a Director, AI
and Analytics
Group,
End-to-End AI
and
Performance
Architecture,
at Intel. She
heads an
organization
responsible
for delivering
End-to-End AI
pipeline SW
and HW
optimizations,
creating AI
solutions and
performance
projections/
architectural
analyses for
deep learning
workloads on
current and
future CPUs. (Presentation)
- (10.30-11 am)
A DBMS-centric Evaluation of BlueField DPUs on Fast Networks,
Lasse Thostrup, Daniel
Failing, Tobias Ziegler and
Carsten Binnig, Technical University of Darmstadt.
(Paper, Presentation)
- (11-11.30 am)
Efficiently Compiling Dynamic Code for Adaptive Query Processing,
Tobias Schmidt, Philipp
Fent and Thomas Neumann, TU Munich.
(Paper, Presentation)
- (11.30-12 pm)
LSM-Trees Under (Memory) Pressure,
Ju Hyoung Mun, Zichen Zhu,
Aneesh Raman and Manos
Athanassoulis, Boston University.
(Paper, Presentation)
- (12.30-1 pm)
What Are You Waiting For? Use Coroutines for Asynchronous I/O to Hide I/O Latencies and Maximize the Read Bandwidth!,
Leonard von Merzljak,
Philipp Fent, Thomas
Neumann and Jana Giceva,
TU Munich.
(Paper, Presentation)
- (1-1.30 pm)
An Adaptive Column Compression Family for Self-Driving Databases,
Marcell Feher, Daniel
Lucani, Aarhus University, and
Ioannis Chatzigeorgiou,
Lancaster University.
(Paper, Presentation)
- (1.30-2 pm)
A Short Study of Recent Smart Storage Solutions for OLAP: Lessons and Opportunities,
Faeze Faghih, Zsolt
István, Technical University
of Darmstadt, and Florin
Dinu, Huawei Research Center Munich
(Paper, Presentation)
Workshop Co-Chairs
For questions regarding the
workshop please send email to contact@adms-conf.org.
Program Committee
- Bulent Abali, IBM Research
- Manos Athanassoulis, Boston University
- Raja Appuswamy, EURECOM
- Martin Boissier, HPI
- Nigel Gulstone, Amazon
- Hao Gao, Nvidia
- Anuva Kulkarni, Google
- Areg Melik-Adamyan, Intel
- Aunn Raza, EPFL
- Viktor Rosenfeld, DFKI
- Jia Shi, Oracle
- Sayantan Sur, Mellanox/Nvidia
- Paper Submission: Monday, 27 June, 2022, 9 am EST
- Notification of Acceptance: Friday, 22 July, 2022
- Camera-ready Submission: Friday, 5 August, 2022
- Workshop Date: Monday, 5 September, 2022
Submission Site
All submissions will be handled electronically via EasyChair.
Formatting Guidelines
We will use the same document templates as the VLDB22 conference. You can find them here.
It is the authors' responsibility to ensure that
their submissions adhere
strictly to the VLDB format detailed here. In particular, it is not allowed to modify the format with the objective of squeezing in more material. Submissions that do not comply with the formatting detailed here will be rejected without review.
As per the VLDB submission guidelines, the paper length for a full paper is
limited to 12 pages, excluding
bibliography. However, shorter
papers (at least 6 pages of content) are encouraged as
well.
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