Ripple：在异构架构上使用多态数据布局简化大规模计算
Ripple : Simplified Large-Scale Computation on Heterogeneous Architectures with Polymorphic Data Layout
摘要：
GPU现在用于HPC中的各种问题。然而，有效利用多个GPU的计算能力是一项挑战。实现良好性能的主要挑战是内存布局，影响内存带宽，有效使用GPU的内存空间，GPU间通信和同步。我们使用Ripple库来解决这些问题，该库提供了跨多个维度和多个GPU的计算空间的统一视图，允许多态数据布局，并提供了一个简单的图形接口来描述一种算法，可以优化GPU间的数据传输。我们描述了Ripple提供的抽象，以允许简单地描述复杂的计算，并以最小的开销在许多GPU上高效地执行。我们展示了一些例子的性能结果，从粒子运动到有限体积方法和程函方程，以及在多个GPU上显示出良好的强弱缩放结果。
英文摘要：
GPUs are now used for a wide range of problems within HPC. However, making efficient use of the computational power available with multiple GPUs is challenging. The main challenges in achieving good performance are memory layout, affecting memory bandwidth, effective use of the memory spaces with a GPU, inter-GPU communication, and synchronization. We address these problems with the Ripple library, which provides a unified view of the computational space across multiple dimensions and multiple GPUs, allows polymorphic data layout, and provides a simple graph interface to describe an algorithm from which inter-GPU data transfers can be optimally scheduled. We describe the abstractions provided by Ripple to allow complex computations to be described simply, and to execute efficiently across many GPUs with minimal overhead. We show performance results for a number of examples, from particle motion to finite-volume methods and the eikonal equation, as well as showing good strong and weak scaling results across multiple GPUs.
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