The objective of this one-day workshop is to investigate opportunities in
accelerating analytics workloads and data management systems. Over the years, the scope of database
analytics has changed substantially, beginning with traditional OLAP, data warehousing, ETL, to HTAP,
Streaming/Real-time Processing, Edge/IoT, and finally to machine learning and deep learning workloads
such as Generative AI or Vector/semantic databases. Increasing use of Large Language Models(LLMs) for
as a source for knowledge extraction for various end uses (e.g., in an AI assistant or Agentic system),
creates new opportunities for database systens. At the same time, hardware and software capabilities have
seen tremendous improvements. The workshop aims to explore how database analytics can be accelerated
using modern processors (e.g., commodity and specialized Multi-core, Many-core, chiplets, 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, NVRAM, and Phase-change Memory),
multi-core and distributed programming paradigms like CUDA/OpenCL, MPI/OpenMP, and
MapReduce/Spark, and integration with data-science/deep-learning 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 intent of the ADMS workshop is to bring together people
from diverse fields such as computer architecture, high-performance computing, systems, and
programming languages to address key functionality and scalability problems in data management.
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., integration of various AI technologies, 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
- Accelerating training, inference, and storage of Large Language Models for Generative AI
- Autonomic Tuning for Data Management Workloads on Hybrid Architectures
- Algorithms for Accelerating Multi-modal Multi-tiered Systems
- Applications of GPUs and other data-parallel accelerators
- 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 Generative AI, DNA-storage, Quantum Technologies
Workshop Co-Chairs
For questions regarding the
workshop please send email to contact@adms-conf.org.
Program Committee
- Francesco Fusco, IBM Research, Zurich
- Wentao Huang, National University of Singapore
- Julia Spindler, TUM
- Selim Tekin, Georgia Tech
- Hubert Mohr-Daurat, Imperial College, London
- Rathijit Sen, Microsoft
- Hong Min, IBM T. J. Watson Research Center
- Viktor Sanca, Oracle
- Subhadeep Sarkar, Boston University
- Paper Submission: Monday, 2 June, 2025, 9 am EST (EXTENDED)
- Notification of Acceptance: Friday, 27 June, 2025
- Camera-ready Submission: Friday, 18 July, 2025
- Workshop Date: Monday, 1 September, 2025
Submission Site
All submissions will be handled electronically via EasyChair.
Publication and Formatting Guidelines
The ADMS'25 proceedings will be published as a part of the official VLDB Workshop Proceedings and indexed via DBLP.
We will use the same document templates as the VLDB 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.
|