BS/MS and Undergradute Research Projects
- Data-intensive computing, Scalable Graph Analytics, In-situ Data Analytics
- Including novel execution models, algorithm and system analysis and performance characterization, large-scale experiments (1000's of nodes), efficient block-based and orchestrated computing, efficient exploitation of non-volatile memory
- Data center Workflow, Data-center Architecture, and OpenStack
- Study of real production data centers and workflows, performance analysis of complex applications (genomics) in a complex networked, storage OpenStack environment, analysis of computational structure, proposal and evaluation of new data center architectures and OpenStack features
- Heterogeneous, yet General-purpose Computer architecture
- Study of exploiting new computer architecture structures (heterogeneous) that can achieve 10-fold to 100-fold energy and performance improvements. Balancing these opportunities with flexible programming from high level software and programming languages.
See our group homepage
- Graph Analytics Programming at Scale
- Big data and graph analytics are transforming science, commerce, and government. We have several projects involving the programming of graph analytics algorithms at scale (1000's of nodes, terabytes to petabytes), their performance evaluation, and application.
- Extreme-scale Computing on Unreliable Processors
- Energy limits and extreme scaling ensure that individual computers of the future will be unreliable. The challenge is how to program them? Studies in programming scientific applications, pde solvers, linear algebra, or other algorithmic structures using the Global View Resilience (GVR) system to achieve reliability in billion-core machines of the future.
for more background, and if you are interested, email Professor Andrew Chien.