Calculation of Stochastic Heating and Emissivity of Cosmic Dust Grains with Optimization for the Intel Many Integrated Core Architecture

Cosmic dust particles effectively attenuate starlight. Their absorption of starlight produces emission spectra from the nearto far-infrared, which depends on the sizes and properties of the dust grains, and spectrum of the heating radiation field. The nearto mid-infrared is dominated by the emissions by very small grains. Modeling the absorption of starlight by these particles is, however, computationally expensive and a significant bottleneck for self-consistent radiation transport codes treating the heating of dust by stars. In this paper, we summarize the formalism for computing the stochastic emissivity of cosmic dust, which was developed in earlier works, and present a new library HEATCODE implementing this formalism for the calculation for arbitrary grain properties and heating radiation fields. Our library is highly optimized for general-purpose processors with multiple cores and vector instructions, with hierarchical memory cache structure. The HEATCODE library also efficiently runs on co-processor cards implementing the Intel Many Integrated Core (Intel MIC) architecture. We discuss in detail the optimization steps that we took in order to optimize for the Intel MIC architecture, which also significantly benefited the performance of the code on general-purpose processors, and provide code samples and performance benchmarks for each step. The HEATCODE library performance on a single Intel Xeon Phi coprocessor (Intel MIC architecture) is ∼ 2 times a general-purpose two-socket multicore processor system with approximately the same nominal power consumption. The library supports heterogeneous calculations employing host processors simultaneously with multiple coprocessors, and can be easily incorporated into existing radiation transport codes.