ADMS 2020

Time 2020-10-01 23:06:19

Web Name: ADMS 2020

WebSite: http://www.adms-conf.org

ID:66896

Keywords:

ADMS,

Description:

2020 Eleventh International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures Monday, August 31, 2020 In conjunction with VLDB 2020 Online Presentations OnlyThe objective of this one-day workshop is to investigate opportunities in accelerating data managementsystems 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/SOCs), high-speed networking, storage systems (e.g., Storage-class Memories like SSDs and Phase-change Memory), programming models like Spark, CUDA/OpenCL, and environments such as various Python-based data science infrastructures (e.g., Nvidia RAPIDS), on on-prem or cloud-based systems.The current data management scenario is characterized by the following trends: traditional OLTP andOLAP/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, oftenconsisting of different data processing components; non-traditional domains such as bio-informatics, socialnetworking, mobile computing, sensor applications, gaming are generating growing quantities of data ofdifferent types; economical and energy constraints are leading to greater consolidation and virtualizationof resources; and analyzing vast quantities of complex data is becoming more important than traditionaltransactional 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 multipleapplication domains. Commodity systems are increasingly using multi-core processors with more than 20cores per chip and enterprise-class systems are using processors with even more cores per chip, where each core can execute upto 4 simultaneous threads. Specializedmulti-core processors such as the GPUs have brought the computational capabilities of supercomputersto cheaper commodity machines. On the storage front, non-volatilememories (NVMe) and solid state devices (SSDs) arebecoming smaller in size, cheaper in price, and larger in capacity. Exotic technologies like DNA storage 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 toevaluate applications in a holistic manner to ensure effective utilization of CPU and memory resources. Thisworkshop aims to understand impact of modern hardware technologies on accelerating core componentsof data management workloads. Specifically, the workshop hopes to explore the interplay between overallsystem design, core algorithms, query optimization strategies, programming approaches, performance modellingand evaluation, etc., from the perspective of data management applications.Hardware and System Issues in Domain-specific AcceleratorsNew Programming Methodologies for Data Management Problems on Modern HardwareQuery Processing for Hybrid ArchitecturesLarge-scale I/O-intensive (Big Data) ApplicationsParallelizing/Accelerating Machine Learning/Deep Learning WorkloadsAutonomic Tuning for Data Management Workloads on Hybrid ArchitecturesAlgorithms for Accelerating Multi-modal Multi-tiered SystemsEnergy Efficient Software-Hardware Co-design for Data Management WorkloadsParallelizing non-traditional (e.g., graph mining) workloadsAlgorithms and Performance Models for modern Storage Sub-systemsExploitation of specialized ASICsNovel Applications of Low-Power Processors and FPGAsExploitation of Transactional Memory for Database WorkloadsExploitation of Active Technologies (e.g., Active Memory, ActiveStorage, and Networking) New Benchmarking Methodologies for Accelerated Workloads Applications of HPC Techniques for Data Management Workloads Acceleration in the Cloud Environments DNA-inspired storage and processing Exploitation of Quantum Technologies VLDB’20 workshop: Prof. Sang Kyun Cha, Seoul National University, will present his perspectives on present and future of in-memory databases Prof. Masanori Hashimoto, Osaka University, will talk about FPGA acceleration opportunities for machine learning workloads. Prof. Sang Cha, Seoul National University (Links: Keynote Presentation (PDF))For questions regarding theworkshop please send email to contact@adms-conf.org.Program CommitteePaper Submission: Monday, 29 June, 2020, 9 am ESTNotification of Acceptance: Friday, 17 July, 2020Camera-ready Submission: Friday, 24 July, 2020Workshop Date: Monday, 31 August, 2020All submissions will be handled electronically via EasyChair. We will use the same document templates as the VLDB20 conference. You can find them here.It is the authors' responsibility to ensure that their submissions adhere strictly to the VLDB formatdetailed 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.

TAGS:ADMS 

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