While polyhedral compilers have shown success in implementing advanced code transformations, they still have challenges in selecting the most profitable transformations that lead to the best speedups. This has motivated the use of machine learning to build cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a viable proof-of-concept of this approach. While such a proof-of-concept has shown promise, it still has significant limitations. State-of-the-art polyhedral compilers that use a deep-learning cost model only support a small subset of affine transformations, limiting their ability to apply complex code transformations. They also only support simple programs that have a single loop nest and a rectangular iteration domain, limiting their applicability to many programs. These limitations significantly impact the generality of such compilers and autoschedulers and put into question the whole approach. In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs. It supports the exploration of a large set of affine transformations, allowing the application of complex sequences of polyhedral transformations. It also supports the optimization of programs with multiple loop nests and with rectangular and non-rectangular iteration domains, allowing the optimization of an extensive set of programs. We implement and evaluate LOOPer and show that it achieves speedups over the state-of-the-art. On the Polybench benchmark, LOOPer achieves a geometric mean speedup of 1.59x over Tiramisu. LOOPer also achieves competitive speedups with a geometric mean speedup of 1.34x over Pluto, a state-of-the-art polyhedral compiler that does not use a machine-learning based cost model.

LOOPer是第一个使用基于深度学习的代价模型的多面体自动调度器，覆盖了大量的仿射变换和程序。它支持探索大量的仿射变换序列，并且支持具有多个循环嵌套和矩形/非矩形迭代域的程序的优化。我们实现和评估了LOOPer，并展示其比现有技术获得了加速。在Polybench基准测试中，LOOPer相对于Tiramisu获得了1.59倍的几何平均加速度，以及相对于Pluto获得了1.34倍的几何平均加速度，Pluto是一个不使用基于机器学习的代价模型的多面体编译器。

LOOPer: 基于学习的自动代码优化器 for 多面体编译器