Symbolic Polyhedral-Based Energy Analysis for Nested Loop Programs

2026-04-08

AI Summary

This work addresses the challenge of energy estimation for nested-loop programs on parallel processor arrays, where traditional simulation-based approaches suffer from poor scalability. To overcome this limitation, the paper proposes a symbolic polyhedral energy modeling method that, for the first time, applies symbolic polyhedral analysis to energy estimation of nested loops. By integrating loop transformation theory with array architecture modeling, the approach explicitly captures the impact of mapping and scheduling decisions on energy consumption. Experimental results demonstrate that the method achieves high-accuracy energy predictions across multiple benchmarks, with computational overhead independent of problem size, thereby significantly enhancing the scalability of design space exploration.

Abstract

This work presents a symbolic approach for estimating the energy consumption for nested loop programs when mapped and scheduled on parallel processor array accelerator architectures. Instead of simulation-based evaluation, we derive a methodology for symbolic energy analysis that captures the impact of mapping and scheduling decisions of loop nests on processor arrays. We compare our approach against simulation-based results for selected benchmarks and varying sizes of the iteration spaces. Whereas the latter are not scalable, our symbolic analysis is shown to be independent of the problem size. The presented evaluation methodology can be beneficially used during the design space exploration of mapping and scheduling decisions, for studying the influence of array size variations, and for comparisons with other loop nest accelerator architectures.

Problem

• energy analysis
• nested loop programs
• processor array
• mapping and scheduling
• symbolic analysis

Innovation

• symbolic energy analysis
• polyhedral model
• nested loop programs
• processor array architectures
• design space exploration