Symbolic Polyhedral-Based Energy Analysis for Nested Loop Programs
This work provides a tool for design space exploration in mapping and scheduling decisions for loop nest accelerators, though it is incremental as it builds on existing polyhedral analysis methods.
The authors tackled the problem of estimating energy consumption for nested loop programs on parallel processor arrays by developing a symbolic analysis method, which is independent of problem size and avoids the scalability issues of simulation-based approaches.
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.