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We design parallelized computational frameworks to accelerate computer modeling using High Throughput Computing (HTC). We create a cluster of computational nodes, using idle CPUs in offices or research labs in order to eliminate the substantial costs required for hiring High Performance Computers (HPC). In addition, the platform-independent (cloud-based) structure of his framework removes the necessity of installing the  computer model on each individual unit in the cluster.

We use this framework for cases that need massive iterative simulations, such as Monte Carlo simulation, evolutionary optimization algorithms and scenario analyses. Then, we interpret the results using data partitioning methods. Below are the results for Monte Carlo simulation of Rainwater Harvesting systems accelerated by HTC and interpreted by Morse-Smale regression models. Different colors show different rainfall depths and dash line connects optimal design in each rainfall depth (knee of curve).

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