ADMS 2012 - Third International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures

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, and FPGAs), storage systems (e.g., Storage-class Memories like SSDs and Phase-change Memory), and hybrid programming models like CUDA and OpenCL.
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 4 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 Analytical (e.g., Data Mining) 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
Data Layout Issues for Modern Memory and Storage Hierarchies
Novel Applications of Low-Power Processors (e.g., ARM Processor based systems)
New Benchmarking Methodologies for Storage-class Memories