Analyzing the impact of computational heterogeneity on runtime performance of parallel scientific components

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.