In the prequel to this paper, we presented a systematic framework for processing spline spaces. In this paper, we take the results of that framework and provide a code generation pipeline that automatically generates efficient implementations of spline spaces. We decompose the final algorithm from Part I and translate the resulting components into LLVM-IR (a low level language that can be compiled to various targets/architectures). Our design provides a handful of parameters for a practitioner to tune - this is one of the avenues that provides us with the flexibility to target many different computational architectures and tune performance on those architectures. We also provide an evaluation of the effect of the different parameters on performance.
翻译:在本文的序言中,我们提出了一个处理样板空间的系统框架。在本文中,我们采纳了这个框架的结果,并提供了一个代码生成管道,自动生成样板空间的高效实施。我们从第一部分中分解了最终算法,并将由此产生的组件翻译成LLLVM-IR(一种低水平的语言,可以编译为不同的目标/结构)。我们的设计为从业者调和提供了几个参数——这是为我们提供灵活选择许多不同计算结构和调整这些结构性能的渠道之一。我们还评估了不同参数对性能的影响。