Skip to main content
Computer Science
CS
Computer Science
Study
Prospective Students
Current Students
Research
Research Areas
Research Groups
People
All People
Faculty
Affiliate Faculty
Instructional Faculty
Research Scientists
Research Staff
Postdoctoral Fellows
Administrative Staff
Alumni
Students
News
Events
About
CEMSE Division
Apply
Task based Runtime Systems
High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems
Rabab Alomairy, Postdoctoral Research Fellow, King Abdullah University of Science and Technology
Jun 20, 11:00
-
13:00
B9 L4 R4223
Tile Low Rank
Algorithmic redesign
Task based Runtime Systems
Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.
