Novel methods are presented in this initial study for the fusion of GPU kernels in the artificial compressibility method (ACM), using tensor product elements with constant Jacobians and flux reconstruction. This is made possible through the hyperbolisation of the diffusion terms, which eliminates the expensive algorithmic steps needed to form the viscous stresses. Two fusion approaches are presented, which offer differing levels of parallelism. This is found to be necessary for the change in workload as the order of accuracy of the elements is increased. Several further optimisations of these approaches are demonstrated, including a generation time memory manager which maximises resource usage. The fused kernels are able to achieve 3-4 times speedup, which compares favourably with a theoretical maximum speedup of 4. In three dimensional test cases, the generated fused kernels are found to reduce total runtime by ${\sim}25\%$, and, when compared to the standard ACM formulation, simulations demonstrate that a speedup of $2.3$ times can be achieved.

翻译：在本初步研究中提出了将GPU内核结合到人工压缩法(ACM)中的GPU内核的新方法,使用恒定的雅各布和通量重建的强压产品元素,通过超陈化扩散条件,消除了形成粘结压力所需的昂贵的算法步骤;提出了两种混合方法,提供不同水平的平行效应;认为这对工作量的变化是必要的,因为元素的精确度提高了;进一步优化了这些方法,包括使资源使用最大化的一代时间内存管理器;引信内核能够实现3-4倍的加速,而理论上的最大速度为4倍;在三维试验中,产生的引信内核被认为可以将总运行时间减少25,000美元;与标准ACM配制相比,模拟表明可以实现2.3美元的速度。