Exploiting pre-optimized kernels with polyhedral transformations for CGRA compilation

TL;DR

This paper presents a novel compilation approach that uses polyhedral transformations to optimize matrix multiplication kernels on CGRAs, achieving significant speedups by exposing hidden parallelism.

Contribution

It introduces a specialized mmul kernel schedule and a compilation methodology that leverages polyhedral analysis to improve CGRA performance.

Findings

Achieved up to 9.1x speedup on benchmarks with hidden mmuls.

Effectively exposes parallelism in complex computational patterns.

Maximizes resource utilization and runtime performance.

Abstract

Modern computing workloads commonly involve matrix-matrix multiplication (mmul) as a core computing pattern. Coarse-Grained Reconfigurable Arrays (CGRAs) can flexibly and efficiently support it, since they combine operation-level reconfigurability and high energy efficiency. However, mapping computational kernels that include mmul with state-of-the-art compilation strategies often leads to suboptimal results, since its multi-dimensional structure hampers the uncovering of its inherent parallelism and, ultimately, runtime performance. Here, we take a different position: we introduce a specialized mmul CGRA kernel schedule, parametrizable across different CGRA sizes. Then, we describe a novel compilation methodology that adapts program representations to effectively leverage it, employing polyhedral transformations to analyze complex computational patterns and expose hidden mmul operations through loop reordering and splitting. The identified patterns are then substituted with optimized assembly, while the remaining program sections are compiled independently. CGRA configurations are then generated, encompassing pre-compiled and compiled parts. Our strategy maximizes resource utilization and ultimately run-time performance, even when mmul is not directly apparent in the source code. The experimental results show speedups up to 9.1x across different benchmarks that contain hidden mmuls and CGRA instances of various sizes.