Local adaptive mesh refinement for shock hydrodynamics
Journal of Computational Physics
CVODE, a stiff/nonstiff ODE solver in C
Computers in Physics
A semi-implicit numerical scheme for reacting flow: II. stiff, operator-split formulation
Journal of Computational Physics
A common data management infrastructure for adaptive algorithms for PDE solutions
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Adaptive Runtime Managementof SAMR Applications
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
Using the Common Component Architecture to Design High Performance Scientific Simulation Codes
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Faster PDE-based simulations using robust composite linear solvers
Future Generation Computer Systems - Special issue: Selected numerical algorithms
A Component Architecture for High-Performance Scientific Computing
International Journal of High Performance Computing Applications
The role of multi-method linear solvers in PDE-based simulations
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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Scientific simulations modeling complex physical phenomena exhibit varying degrees of spatiotemporal and computational heterogeneity, which can pose significant challenges in algorithmic efficiency and runtime performance. Addressing these challenges often requires an understanding of application behavior and the impact of heterogeneity on the simulation, especially when analytical approaches are not feasible. Runtime calibration is used to analyze the impact of computational heterogeneity on the performance of two different load balancing strategies applied to two different problems of 2-D methane-air combustion using different chemistry models. Experimental evaluation demonstrates that such empirical approaches are inevitable in component-based scientific computations, where performance is determined by the problem characteristics and the particular connectivity of components in the simulation code, none of which are known before runtime.