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High Performance Computing on Large-Scale Platforms Research
- Autotuning the performance of the software I/O stack.
- Design, implement and evaluate a decentralized file cache hierarchy for large-scale architectures.
- Multiple-level data staging for large scale HPC architectures: write-back and prefetching.
- Study decentralized I/O coordination strategies for improving resource utilization in large-scale HPC architectures.
- Leverage data locality for optimal communication in collective I/O.
- View-based non-contiguous I/O optimizations.
- Inspector-executor collective I/O.
Parallel File Systems Research
- Generic data layouts for parallel files based on multi-dimensional data distribution algorithms from parallelizing compilers.
- File system-level generalized file views.
- File-system-level collective I/O (two-phase I/O and server-directed I/O).
- File-system-level integration and study of cooperative caching and collective I/O.
- Elastic distributed file system partitions.
- Fault-tolerant models for parallel file systems.
- Study decentralized parallel I/O scheduling in parallel file systems.
- Novel MPI-IO implementation for Clusterfile and GPFS.
Machine Learning Research
- Auto-tuning of parallel I/O access performance based on machine learning models for performance prediction
- Leverage machine learning prediction models in the design of large-scale distributed data management.
- Time-series analysis: apply autoregressive and exponentially smoothing models for on-line history-based workload prediction.
- Social network analysis: employ structural and dynamic network analysis for understanding data locality, data sharing and data popularity patterns.
- Dynamic clustering algorithms for improving content locality.
Cloud Computing Research
- Predictive data grouping algorithms for optimizing content locality and server load balance.
- Elastic data placement algorithms for optimizing server utilization.
- Multi-model algorithms for on-line workload scalability.
Peer- to-Peer Systems Research
- Leverage collaborative classifications and multiple clustering for improving content locality.
- Node-level and cluster-level self-organization for adapting to locality dynamics.
- Efficient parallel lookup algorithm for high data recall, high tolerance to node failure, and avoidance of redundant communication.