Maximal Atomic irRedundant Sets: a Usage-based Dataflow Partitioning Algorithm

TL;DR

This paper introduces a novel dataflow partitioning algorithm that identifies maximal iteration sets with no redundant data transfer, improving memory efficiency in polyhedral program transformations.

Contribution

It proposes a new partitioning method for flow-out sets in polyhedral programs, optimizing data communication and storage without redundancy.

Findings

Algorithm successfully computes maximal non-redundant iteration sets.

Application potential in data compression and memory management.

Demonstrated on selected polyhedral programs.

Abstract

Programs admitting a polyhedral representation can be transformed in many ways for locality and parallelism, notably loop tiling. Data flow analysis can then compute dependence relations between iterations and between tiles. When tiling is applied, certain iteration-wise dependences cross tile boundaries, creating the need for inter-tile data communication. Previous work computes it as the flow-in and flow-out sets of iteration tiles. In this paper, we propose a partitioning of the flow-out of a tile into the maximal sets of iterations that are entirely consumed and incur no redundant storage or transfer. The computation is described as an algorithm and performed on a selection of polyhedral programs. We then suggest possible applications of this decomposition in compression and memory allocation.