Abstract
In radiation transport calculations, the effects of material temperature on neutron/nucleus interactions must be taken into account through Doppler broadening adjustments to the microscopic cross section data. Historically, Monte Carlo transport simulations have accounted for this temperature dependence by interpolating among precalculated Doppler broadened cross sections at a variety of temperatures. More recently, there has been much interest in on-the-fly Doppler broadening methods, where reference data is broadened on-demand during particle transport to any temperature. Unfortunately, Doppler broadening operations are expensive on traditional central processing unit (CPU) architectures, making on-the-fly Doppler broadening unaffordable without approximations or complex data preprocessing. This work considers the use of graphics processing unit (GPU)s, which excel at parallel data processing, for on-the-fly Doppler broadening in continuous-energy Monte Carlo simulations. Two methods are considered for the broadening operations – a GPU implementation of the standard SIGMA1 algorithm and a novel vectorized algorithm that leverages the convolution properties of the broadening operation in an attempt to expose additional parallelism. Numerical results demonstrate that similar cross section lookup throughput is obtained for on-the-fly broadening on a GPU as cross section lookup throughput with precomputed data on a CPU, implying that offloading Doppler broadening operations to a GPU may enable on-the-fly temperature treatment of cross sections without a noticeable reduction in cross section processing performance in Monte Carlo transport codes.
Highlights
Neutron transport algorithms require accurate representation of the fundamental underlying cross section data
To gauge the performance of utilizing graphics processing unit (GPU) for Doppler broadening, an exploratory mini-app was created with both central processing unit (CPU) and GPU implementations of the traditional SIGMA1 and the new vectorized Doppler broadening algorithms
Comparisons of the two GPU implementations with respect to cross section lookups without Doppler broadening on a CPU, which is the standard practice in most Monte Carlo codes, as well as CPU SIGMA1 on-the-fly Doppler broadening, which represents the “worst possible case” of a performance hit when adding on-the-fly broadening to a transport simulation were of particular interest
Summary
Neutron transport algorithms require accurate representation of the fundamental underlying cross section data. An important aspect of this is accurate modeling of the impact of material temperature on cross sections due to the thermal motion of the target nuclei during particle transport Effects of material temperature have been handled in neutron transport calculations by pre-calculating broadened cross sections at various temperatures representative of the problem being solved and interpolating the cross section on this temperature mesh. These broadened cross sections are most frequently calculated using the SIGMA1 method [1,2]. The second is a novel method that relies on the fact that the Doppler broadening kernel used in SIGMA1 computation is fundamentally a convolution and leverages an existing fast convolution method in an attempt to expose additional parallelism in the operation, making it more suitable for computation on a GPU
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