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 Generative AI, 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
• 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

• Rajesh Bordawekar, IBM T.J. Watson Research Center
• Tirthankar Lahiri, Oracle

       For questions regarding the workshop please send email to contact@adms-conf.org.

Program Committee

• Bulent Abali, IBM Research
• Adwait Jog, University of Virginia
• Rajaram Krishnamurthy, AMD
• Ju Hyoung Mun, Brandeis University
• Selim Tekin, Georgia Tech
• Chinmayi Krishnappa, Oracle
• Allison Holloway, Oracle
• Qiong Luo, HKUST
• Peter Hofstee, Delft University and IBM
• Rathijit Sen, Microsoft
• Lawrence Benson, HPI

Important Dates

• Paper Submission: Monday, 24 June, 2024, 9 am EST
• Notification of Acceptance: Friday, 19 July, 2024
• Camera-ready Submission: Friday, 2 August, 2024
• Workshop Date: Monday, 25 August, 2024

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

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.