The methods and performance of GPU-accelerated Direct Whole-Core Calculation (DWCC) are presented. GPU computing techniques were introduced to significantly reduce the execution time of the production-grade DWCC code nTRACER that employs the planar Method of Characteristics (MOC) and augmented axial MOC within the framework of the Coarse Mesh Finite Difference (CMFD) acceleration. The data structures and algorithms are largely modified from the legacy CPU solver in a way to be suitable for the computational characteristics of GPUs. Mixed precision technique is extensively utilized in order to exploit the superior single precision computing power of consumer-grade GPUs and to make use of limited GPU memories efficiently. Concurrent execution of CPU and GPU is then utilized to take advantage of heterogeneous computing architectures. This includes the source update by CPUs for lower energy groups during the ray tracing by GPUs for upper energy groups. The performance examination indicates that the planar MOC calculation time can be remarkably reduced by GPU acceleration. As the result of efficient GPU acceleration, a three-dimensional (3D) DWCC calculation for a typical light water reactor could be carried out in a few minutes on an industrially affordable cluster mounted with consumer-grade GPUs.

介绍了GPU加速直接全核计算（DWCC）的方法和性能。引入GPU计算技术可显着减少生产级DWCC代码nTRACER的执行时间，该代码在粗糙网格有限差分（CMFD）加速的框架内采用了平面特征方法（MOC）和增强的轴向MOC。数据结构和算法从传统的CPU求解器中进行了很大的修改，以适合GPU的计算特性。为了充分利用消费级GPU的卓越单精度计算能力并有效利用有限的GPU内存，混合精度技术得到了广泛的利用。然后利用CPU和GPU的并发执行来利用异构计算架构。这包括CPU对较低能量组的光源更新，而GPU对较高能量组的光线进行跟踪。性能检查表明，通过GPU加速可以显着减少平面MOC计算时间。由于高效的GPU加速，可以在几分钟内在装有消费级GPU的工业价格可承受的集群上对典型的轻水反应堆进行三维（3D）DWCC计算。