This paper is devoted to the development of highly efficient kernels performing vector operations relevant in linear system solvers. In particular, we focus on the low arithmetic intensity operations (i.e., streaming operations) performed within the conjugate gradient iterative method, using the parameters specified in the CEED benchmark problems for high-order hexahedral finite elements. We propose a suite of new Benchmark Streaming tests to focus on the distinct streaming operations which must be performed. We implemented these new tests using the OCCA abstraction framework to demonstrate portability of these streaming operations on different GPU architectures, and propose a simple performance model for such kernels which can accurately capture data movement rates as well as kernel launch costs.

中文翻译：

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便携式高阶有限元内核 I：流操作

本文致力于开发执行与线性系统求解器相关的向量运算的高效内核。特别是，我们重点关注在共轭梯度迭代方法中执行的低算术强度运算（即流运算），使用高阶六面体有限元的 CEED 基准问题中指定的参数。我们提出了一套新的 Benchmark Streaming 测试，专注于必须执行的不同流操作。我们使用 OCCA 抽象框架实现了这些新测试，以展示这些流操作在不同 GPU 架构上的可移植性，并为此类内核提出了一个简单的性能模型，该模型可以准确捕获数据移动速率以及内核启动成本。