Scalable storage architectures, archival storage, storage virtualization, emerging storage devices and techniques
Performance benchmarking, resource management, and workload studies from production systems including both traditional HPC and data-intensive workloads.
Programmability, APIs, and fault tolerance of storage systems
Parallel file systems, metadata management, and complex data management, object and key-value storage, and other emerging data storage/retrieval techniques
Programming models and frameworks for data intensive computing including extensions to traditional and nontraditional programming models, asynchronous multi-task programming models, or to data intensive programming models
Techniques for data integrity, availability and reliability especially
Productivity tools for data intensive computing, data mining and knowledge discovery
Application or optimization of emerging “big data” frameworks towards scientific computing and analysis
Techniques and architectures to enable cloud and container-based models for scientific computing and analysis
Techniques for integrating compute into a complex memory and storage hierarchy facilitating in situ and in transit data processing
Data filtering/compressing/reduction techniques that maintain sufficient scientific validity for large scale compute-intensive workloads
Tools and techniques for managing data movement among compute and data intensive components both solely within the computational infrastructure as well as incorporating the memory/storage hierarchy