ADMS 2018
Ninth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures

Monday, August 27, 2018
In conjunction with VLDB 2018, Rio De Janeiro, Brazil
Workshop Overview

The objective of this one-day workshop is to investigate opportunities in accelerating data management systems and analytics workloads (which include traditional OLTP, data warehousing/OLAP, ETL, Streaming/Real-time, Analytics (including Machine Learning), and HPC/Deep Learning) using processors (e.g., commodity and specialized Multi-core, GPUs, FPGAs, and ASICs), storage systems (e.g., Storage-class Memories like SSDs and Phase-change Memory), and programming models like MapReduce, Spark, CUDA, OpenCL, and OpenACC.

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 CPU and memory capabilities 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 8 cores per chip, where each core can execute upto 4 simultaneous threads. 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 usage of multi-core processors or SSDs often leads to unbalanced system. 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

Accepted Papers
  • Optimizing Group-By And Aggregation using GPU-CPU Co-Processing, Diego Gomes Tomé, Tim Gubner, Mark Raasveldt, Eyal Rozenberg and Peter Boncz, CWI, Vrije Universiteit, Amsterdam

  • Hammer Slide: Work- and CPU-efficient Streaming Window Aggregation, Georgios Theodorakis, Alexandros Koliousis, Peter Pietzuch and Holger Pirk, Imperial College, London

  • Near-Data Filters: Taking Another Brick from the Memory Wall Diego Gomes Tomé (CWI, Amsterdam), Tiago Rodrigo Kepe, Marco Antonio Zanata Alves and Eduardo Cunha De Almeida (UFPR, Brazil)

  • Column Scan Acceleration in Hybrid CPU-FPGA Systems, Nusrat Lisa, Annett Ungethüm, Dirk Habich, Wolfgang Lehner, Nguyen Duy Anh Tuan and Akash Kumar (TU Dresden)

  • Hassium: Hardware Assisted Database Synchronization, Hillel Avni and Aharon Avitzur (Huawei)

  • Full Speed Ahead: 3D Spatial Database Acceleration with GPUs, Lucas Villa Real and Bruno Silva, IBM Research, Brazil

  • Adaptive Cache Mode Selection for Queries over Raw Data, Tahir Azim (EPFL), Azqa Nadeem (Delft University of Technology), and Anastasia Ailamaki (EPFL)

  • Low-Latency Transaction Execution on Graphics Processors: Dream or Reality?, Iya Arefyeva, Gabriel Campero Durand, Marcus Pinnecke, David Broneske and Gunter Saake (University of Magdeburg)


Workshop Co-Chairs

       For questions regarding the workshop please send email to

Program Committee

  • Raja Appuswamy, EPFL
  • Shashank Chavan, Oracle
  • Christoph Dubach, University of Edinburgh
  • Markus Dreseler, HPI
  • Stefan Manegold, CWI
  • Bingsheng He, NUS
  • Diego Arroyuelo, Universidad Técnica Federico Santa María
  • Nikolay Sakharnykh, Nvidia
  • Carsten Binnig, TU Darmstadt
  • Kajan Kanagaratnam, IBM Analytics
  • Bill Howe, University of Washington
  • Wellington Martins, INF/UFG
  • Arun Raghavan, Oracle Labs
  • Ken Salem, University of Waterloo
  • Rajkumar Sen, Striim Inc.
  • Man Lung Yiu, Hongkong Polytechnic University

Important Dates

  • Paper Submission: Monday, 11 June, 2018 (EXTENDED)
  • Notification of Acceptance: Friday, 29 June, 2018
  • Camera-ready Submission: Friday, 20 July, 2018
  • Workshop Date: Monday, 27 August, 2018

Submission Instructions

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the VLDB18 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 4 pages of content) are encouraged as well.