Intel Many Integrated Core (MIC) architecture is a computing platform for energy-efficient and accelerated execution of highly parallel applications. Unlike General Purpose Graphics Processing Units (GPGPUs), MIC-based Intel Xeon Phi coprocessors can execute applications compiled from the same source code in C/C++/Fortran as the CPU platform, which may be of crucial importance for some legacy scientific applications. Furthermore, optimization for the MIC architecture usually requires the same strategies as optimization for general-purpose multicore CPUs. As a consequence, the effort of porting a code to Xeon Phi may yield a better performing application for the CPU. This trend of "double rewards" was apparent in a case study that I will demonstrate in this talk, where a calculation of cosmic dust heating in the interstellar medium was adapted for the MIC architecture. Through code optimization and the use of Intel Xeon Phi coprocessors, we were able to shorten the duration of a data-driven Bayesian analysis of Galactic structure parameters from years to weeks. I will outline the methods used in the optimization process, which are representative of a general class of physics problems with voxelized simulation spaces, dense linear algebra, discretized and analytically fitted functional dependencies of physical quantities.