Researchers in the Performance and Algorithms Group are currently engaged in a wide variety of projects including collaborations with computer architects and programming models researchers. Below is a subset of our active research projects and topics.
Parallel Primitives for Randomized Algorithms on Sparse Data project targets scalable randomized methods broadly within the context of scientific data analysis. Read More »
This project aims to harness exascale computing and non-conventional machine-learning approaches in order to design tailored optical excitations for controlling electron-driven dynamics in chemical/material systems using real-time time-dependent density functional theory (RT-TDDFT). Read More »
Understanding the interplay between science, AI method, framework, and architecture is essential to quantifying the computational potential for current and future architectures running AI models and for identifying the bottlenecks and the ultimate limits of today's models. Read More »
CSPACER is a lightweight communication runtime for application-specific optimized communication patterns. It is composed of two layers. The lowest layer is a sub-microsecond communication layer that implements the consistent space abstraction primitives. On top of this layer is a communication pattern layer that is used as integration skeletons… Read More »