ADMS 2021

Time 2021-05-16 10:12:59

Web Name: ADMS 2021

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

ID:176951

Keywords:

ADMS,

Description:

2021 Twelth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures Monday, August 16, 2021 In conjunction with VLDB 2021 Hybrid (In-person+Online)The objective of this one-day workshop is to investigateopportunities in accelerating analytics workloads and data management systems which includetraditional 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 specializedMulti-core, Many-core, GPUs, and FPGAs), processing systems (e.g., hybrid,massively-distributed clusters, and cloud based distributed computinginfrastructure), networking infrastructures (e.g., RDMA overInfiniBand), memory and storage systems (e.g., storage-class Memorieslike SSDs, active memories, NVRams, andPhase-change Memory), multi-core anddistributed programming paradigms like CUDA/OpenCL, MPI/OpenMP, and MapReduce/Spark, and integration with data-science frameworks such asSklearn, TensorFlow, or PyTorch. Exploratory topics such as DNA-based storage or quantumalgorithms are also within the preview of the ADMS workshop. 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 CPUand memory technologies. Newer processors are more capable in thecompute and memory capabilities, are power-efficient, and are optimized for multiple applicationdomains. Commodity systems are increasingly usingmulti-core processors with more than 6 cores per chip andenterprise-class systems are using processors with at least 32 cores perchip. Specialized multi-core processors such as the GPUs havebrought the computational capabilities of supercomputers to cheapercommodity machines. On the storage front, FLASH-based solid statedevices (SSDs) are becoming smaller in size, cheaper in price, and larger incapacity. Exotic technologies like Phase-change memory are on thenear-term horizon and can be game-changers in the way data is storedand processed. In spite of the trends, currently there is limited usage ofthese technologies in data management domain. Naive exploitation ofmulti-core processors or SSDs often leads to unbalanced systems. Itis, therefore, important to evaluate applications in a holistic mannerto ensure effective utilization of CPU and memoryresources. This workshop aims to understand impact of modernhardware technologies on accelerating core components of datamanagement workloads. Specifically, the workshop hopes to explorethe interplay between overall system design, core algorithms, query optimizationstrategies, programming approaches, performance modelling andevaluation, 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 Accelerating Data Science/Machine Learning Workloads Exploratory topics such as DNA-storage, Quantum Technologies For questions regarding theworkshop please send email to contact@adms-conf.org.Program CommitteePaper Submission: Monday, 28 June, 2021, 9 am ESTNotification of Acceptance: Friday, 16 July, 2021Camera-ready Submission: Friday, 24 July, 2021Workshop Date: Monday, 16 August, 2021All submissions will be handled electronically via EasyChair. We will use the same document templates as the VLDB21 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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