将基于网格的3D+3V混合Vlasov动理学等离子体模拟Vlasiator移植到异构GPU架构
Porting the grid-based 3D+3V hybrid-Vlasov kinetic plasma simulation Vlasiator to heterogeneous GPU architectures
AI总结：
本文介绍了将空间等离子体模拟代码Vlasiator移植到异构CPU/GPU架构的设计方案，通过统一代码库支持CUDA和HIP，提出高效稀疏速度网格调整方法，为未来百亿亿次模拟奠定基础。
摘要：
Vlasiator是一个空间等离子体模拟代码，它在三个空间维度和三个速度维度上对近地离子动理学动力学进行建模。它具有高度并行性，通过分布函数直接对Vlasov方程进行建模，在笛卡尔网格上进行离散化，而不是采用更常见的粒子模拟（PIC）方法。在对近地空间进行建模时，等离子体特性在温度、密度和磁场强度上跨越几个数量级。为了将所需的六维网格装入内存，Vlasiator利用了基于稀疏块的速度网格，根据Vlasov求解器的平流要求增加或删除速度空间的数据块。此外，空间网格通过基于单元的八叉树细化进行自适应细化。在本文中，我们描述了将Vlasiator移植到异构CPU/GPU架构的设计选择。我们详细介绍了内存管理、算法更改和内核构造，以及我们的统一代码库方法，从而实现了向NVIDIA和AMD硬件（分别为CUDA和HIP语言）的可移植性。特别地，我们展示了一种高度并行的块调整方法，允许对稀疏速度网格进行高效重排序。我们详细说明了已克服的陷阱，并制定了优化计划，以促进未来使用多节点GPU超级计算进行百亿亿次模拟。
英文摘要：
Vlasiator is a space plasma simulation code which models near-Earth ion-kinetic dynamics in three spatial and three velocity dimensions. It is highly parallelized, modeling the Vlasov equation directly through the distribution function, discretized on a Cartesian grid, instead of the more common particle-in-cell approach. Modeling near-Earth space, plasma properties span several orders of magnitude in temperature, density, and magnetic field strength. In order to fit the required six-dimensional grids in memory, Vlasiator utilizes a sparse block-based velocity mesh, where chunks of velocity space are added or deleted based on the advection requirements of the Vlasov solver. In addition, the spatial mesh is adaptively refined through cell-based octree refinement. In this paper, we describe the design choices of porting Vlasiator to heterogeneous CPU/GPU architectures. We detail the memory management, algorithmic changes, and kernel construction as well as our unified codebase approach, resulting in portability to both NVIDIA and AMD hardware (CUDA and HIP languages, respectively). In particular, we showcase a highly parallel block adjustment approach allowing efficient re-ordering of a sparse velocity mesh. We detail pitfalls we have overcome and lay out a plan for optimization to facilitate future exascale simulations using multi-node GPU supercomputing.
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