Computer Science and Applied Mathematics
The Computer Science and Mathematics department focuses on making sense of large-scale data, including those output by major scientific facilities. To manage such diverse high-volume data, researchers are developing extreme-scale and streaming machine learning capabilities, as well as on-the-wire processing algorithms—all augmented by research on programmingmodels, compilers, performance analysis, modeling/portability, and network protocols.
- Analysis on the Wire (AoW)
- Automatic Parallelization and Optimization for Lattice QCD Software using a Source-to-Source Compiler
- Exascale Application Development for Lattice Quantum Chromodynamics
- Extreme-Scale Computing for Exascale and Strong-Scaled Problems (XPRESS)
- GPU Research Center
- Integrated End-to-End Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD)
- Mesoscale Models for Brittle Fracture of Solids
- Multicore-Aware Data Transfer Middleware: Harnessing Multicore Parallelism to Scale Data Movement
- SOLLVE: Scaling OpenMP with LLVm for Exascale performance and portability
- Software Defined Network Science Flows
- Visualizing and Interacting with Incremental Machine Learning Processes
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