ADMS 2022
Thirteenth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures
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In conjunction with VLDB 2022


Virtual Workshop, Monday, September 5, 2022
11pm-4am Sydney Time
3pm-8pm Europe(Berlin) Time
9am-2pm EST (New York)
6am-11am PST (San Francisco)

 
 
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Workshop Overview

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.

Topics of Interest

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

Keynote Presentation
9-10 am ET, 3-4pm Europe


  • 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)


Accepted Paper Presentation
10.30am-2pm ET, 4.30-8pm Europe


  • (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 Istv├ín, Technical University of Darmstadt, and Florin Dinu, Huawei Research Center Munich (Paper, Presentation)


Organization

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

Important Dates

  • 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 Instructions

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.