HPCO 2013 - International Workshop on High-Performance Computing and Optimization (HPCO-2013)

We therefore cordially invite contributions that investigate these issues, introduce new execution environments, apply performance evaluations and show the applicability to science and enterprise applications. We welcome various different kinds of papers that could formalize, simplify and optimize all the aspects of existing data intensive applications in science, engineering and business. We particularly encourage the submission of position papers that describe novel research directions and work that is in its formative stages, and papers about practical experiences and lessons learned from production systems.
Papers of applied research, industrial experience reports, work-in-progress and vision papers with different criteria for each category that describe recent advances and efforts in the design and development of data intensive computing, functionalities and capabilities that will benefit many applications are also solicited.
List of topics
Topics of interests include, but are not limited to:
High performance distributed cache and optimization
High performance data transfer and ingestion
NoSQL data store
Machine Learning Algorithms for Big Data
Data intensive computing in science, commerce, entertainment and medicine
Data-intensive applications and their challenges
Data Clouds, Data Grids, and Data Centers
Data-aware toolkits and middleware
Network support for data-intensive computing
Remote and distributed visualization of large scale data
Data archives, digital libraries, and preservation
Service oriented architectures for data-intensive computing
High performance data access toolkits
Power and energy efficiency
Accountability, QoS, and SLAs
Data privacy and protection in a public cloud environment
Programming models, abstractions for data intensive computing
Data capturing, management, and scheduling techniques
Future research challenges of data intensive computing
Security and protection of sensitive data in collaborative environments
MapReduce implementation issues and improvements
MapReduce,Hadoop,Spark and their applications in data intensive computing
Large-scale MapReduce (Grid and Desktop Grid)
Scientific data-sets analysis
Monitoring, troubleshooting, and failure recovery
Distributed I/O (wide-area, grid, peer-to-peer)
Search and data retrieval
Storage and file systems
Performance measurement, analytic modeling, simulation
Distributed Ensemble Classifier