A MPI-based Distributed Computation for Supporting Optimization of Urban Designs with QUIC EnvSim

Abstract

In the present day of urbanization, rise in urban infrastructure is causing an increase in air temperatures and pollution concentrations. This leads to an increase in the energy required to cool buildings and more focused efforts to mitigate pollution. An effective way to mitigate these problems is by carefully designing cityscapes i.e., by placing the buildings, vegetation optimally and choosing energy efficient building materials. Researchers have been building computational models to understand the effects of urban infrastructure on microclimate. Simulating these models is a computationally expensive task. QUIC EnvSim (QES) is a dynamic, scalable and high performance framework that has provided a platform for building and simulating these models. QUIC EnvSim uses Graphics Processing Units (GPUs) to run each individual simulation faster than previous simulation codes. Though each individual simulation takes a short time, it is often required to perform large numbers of simulations and it can take a long time to complete them. This thesis introduces MPI QUIC, a scalable and extendable framework for running these simulations across a cluster of machines, effectively reducing the time required to run all simulations. Various tests on the framework have shown that the framework is capable of running large numbers of simulations in a relatively less amount of time. A test running 65536 simulation was performed. The estimated time for running the test on a single computer is approximately 11.37 days, with each simulation taking approximately 15 seconds to complete. The framework was able to finish running all the simulations in 19 hours, 0 minutes and 25 seconds showing a tremendous speed up of 92.5%. Thus urban planners can use this framework along with QUIC EnvSim to understand the effects of urban forms on microclimate and take informed design decision relatively quickly for building environment friendly urban landscapes. Besides providing a distributed computational environment, the other goal of the MPI QUIC project is to provide a user friendly interface for specifying optimization problems. The current work provides the ground work for the successors of the current work to provide a programmable interface for end users for specifying optimization problems. The framework is also designed so that future implementers can incorporate optimization algorithms that can optimize on multiple fitness functions.