Introduction
The objective of this one-day workshop is to investigate opportunities in accelerating data management systems and workloads (which include traditional OLTP, data warehousing/OLAP, ETL, Streaming/Real-time, Business Analytics, and XML/RDF Processing) 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.
Call for paper
Important date
Submission Topics
The suggested topics of interest include, but are not restricted to:
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Hardware and System Issues in Domain-specific Accelerators
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New Programming Methodologies for Data Management Problems on Modern Hardware
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Query Processing for Hybrid Architectures
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Large-scale I/O-intensive (Big Data) Applications
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Parallelizing/Accelerating Analytical (e.g., Data Mining) Workloads
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Autonomic Tuning for Data Management Workloads on Hybrid Architectures
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Algorithms for Accelerating Multi-modal Multi-tiered Systems
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Energy Efficient Software-Hardware Co-design for Data Management Workloads
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Parallelizing non-traditional (e.g., graph mining) workloads
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Algorithms and Performance Models for modern Storage Sub-systems
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Exploitation of specialized ASICs
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Novel Applications of Low-Power Processors and FPGAs
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Exploitation of Transactional Memory for Database Workloads
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Exploitation of Active Technologies (e.g., Active Memory, Active Storage, and Networking)
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New Benchmarking Methodologies for Storage-class Memories
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Applications of HPC Techniques for Data Management Workloads
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Acceleration in the Cloud Environments
All Comments
Important Date
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Sep 01
2017
Conference Date
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Jun 12 2017
Draft paper submission deadline
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Jun 26 2017
Draft Paper Acceptance Notification
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Sep 01 2017
Registration deadline
Contact Information
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