In recent years the computer processors underpinning the large, distributed, workhorse computers used to solve the Boltzmann transport equation have become ever more parallel and diverse. Traditional CPU architectures have increased in core count, reduced in clock speed and gained a deep memory hierarchy. Multiple processor vendors offer a collectively diverse range of both CPUs and GPUs, with the architectures used in the fastest machines in the world ever growing in diversity of many-core architectures. Going forward, the landscape of processor technologies will require our codes to function well across multiple architectures. This ever increasing range of architectures represents a unique challenge for solving the Boltzmann equation using deterministic methods in particular, and so it is important to characterize the performance of those key algorithms across the processor spectrum. The solution of the transport equation is computationally expensive, and so we require well optimized and highly parallel solver implementations in order to solve interesting problems quickly. In this work we explore the performance profiles of deterministic S_N transport sweeps for both 3D structured (Cartesian) and unstructured (hexahedral) meshes. The study focuses on the characteristics of computational performance which are responsible for the actual performance of a transport solver.

近年来，用于解决玻尔兹曼输运方程的大型，分布式，主力计算机的计算机处理器变得越来越并行和多样化。传统的CPU体系结构增加了内核数，降低了时钟速度，并获得了更深的内存层次。多个处理器供应商提供了广泛的CPU和GPU范围，并且世界上最快的计算机中使用的体系结构在众多核体系结构中也不断增加。展望未来，处理器技术的发展将要求我们的代码在多种架构之间都能正常运行。不断增加的体系结构代表着使用确定性方法求解玻尔兹曼方程的独特挑战，因此，重要的是表征整个处理器范围内那些关键算法的性能。输运方程的解决方案计算量很大，因此，为了快速解决有趣的问题，我们需要精心优化和高度并行的求解器实现。在这项工作中，我们探索确定性的性能概况3 _N结构化（笛卡尔）网格和非结构化（六面体）网格的S _N传输扫描。该研究着重于计算性能的特征，这些特征决定了运输求解器的实际性能。