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 near- to far-infrared, which depends on the sizes and properties of the dust grains, and spectrum of the heating radiation field. The near- to 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 approximately 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.

Turbulence Dissipation and Particle Injection in Non-Linear Diffusive Shock Acceleration with Magnetic Field Amplification

The highly amplified magnetic fields suggested by observations of some supernova remnant (SNR) shells are most likely an intrinsic part of efficient particle acceleration by shocks. This strong turbulence, which may result from cosmic ray driven instabilities, both resonant and non-resonant, in the shock precursor, is certain to play a critical role in self-consistent, nonlinear models of strong, cosmic ray modified shocks. Here we present a Monte Carlo model of nonlinear diffusive shock acceleration (DSA) accounting for magnetic field amplification through resonant instabilities induced by accelerated particles, and including the effects of dissipation of turbulence upstream of a shock and the subsequent precursor plasma heating. Feedback effects between the plasma heating due to turbulence dissipation and particle injection are strong, adding to the nonlinear nature of efficient DSA. Describing the turbulence damping in a parameterized way, we reach two important results: first, for conditions typical of supernova remnant shocks, even a small amount of dissipated turbulence energy (~10%) is sufficient to significantly heat the precursor plasma, and second, the heating upstream of the shock leads to an increase in the injection of thermal particles at the subshock by a factor of several. In our results, the response of the non-linear shock structure to the boost in particle injection prevented the efficiency of particle acceleration and magnetic field amplification from increasing. We argue, however, that more advanced (possibly, non-resonant) models of turbulence generation and dissipation may lead to a scenario in which particle injection boost due to turbulence dissipation results in more efficient acceleration and even stronger amplified magnetic fields than without the dissipation.