Simulation plays a major role in nearly every area of science and engineering—from data analysis to physical models. Our faculty design, build, and analyze the behavior of numerical algorithms to ensure that numerical methods are accurate and that implementations are efficient.
We design and analyze the accuracy of methods, developing numerical approximations to partial differential equations with advanced finite element methods and integral equations. We also develop solvers for these problems, instrumenting techniques based on numerical linear algebra, iterative subspace methods, and multigrid methods. Our research explores the efficiency of these methods on a range of architectures and environments, from high-concurrency nodes, such as GPUs, to large-scale supercomputing systems. We explore parallel scalability and analyze performance in computing kernels from graph algorithms to sparse linear algebra.
CS Faculty and Their Research Interests
|Paul Fischer||numerical PDEs, spectral element methods, computational fluid dynamics, parallel and high-performance algorithms, iterative methods|
|William Gropp||high performance scientific computing, scalable numerical algorithms for PDEs, large-scale parallel software|
|Michael T. Heath||numerical analysis and scientific computing, numerical linear algebra and optimization|
|Laxmikant Kale||simulation software, numerical libraries and algorithms|
|Andreas Kloeckner||integral equation methods for PDEs, high-order finite element methods for hyperbolic PDEs, tools and languages for high-performance computing, time integration|
|William Kramer||extreme-scale computing and analytics, performance evaluation, data and storage techniques|
|Luke Olson||numerical analysis, scientific computing, large-scale simulation|
|Marc Snir||large-scale parallel systems, algorithms, and libraries|
|Edgar Solomonik||communication complexity|
|Robert Brunner, Astronomy||computational astrophysics|
|Daniel S. Katz, NCSA||resilience and fault-tolerance, many-task computing, parallel and distributed computing, sustainable and open science software|
Argonne National Lab
|determinism in high-performance and distributed computing, check-pointing, fault prediction|
Related Scientific Computing Research Efforts and Groups
- Blue Waters in the National Center for Supercomputing Applications
- Center for Exascale Simulation of Plasma-Coupled Combustion
- Computational Science and Engineering
- National Center for Supercomputing Applications (NCSA)
- Parallel Computing Institute (PCI)
- Scientific Computing at Illinois
- Theoretical and Computational Biophysics Group
Scientific Computing Research News
Fischer Plans to Use Grant to Improve Accuracy, Reduce Cost of Fluid-Flow Models for Nuclear ReactorsJuly 17, 2018 An $800,000 Department of Energy grant will support work to create fluid-flow models for nuclear reactors that can run on desktop computers.
Phys.org -- Research from Illinois that includes work by CS Assistant Professor Jian Peng has provided a new technique for distinguishing between corn and soybeans in satellite data, crops that previously have been impossible to distinguish from space. Also covered by Farmers Advance.