The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Mohammad Ali and Apan Qasem (Texas State University)
Abstract: There have been significant advances in machine learning-driven performance modeling in recent years. One key limitation of such approaches is that their success depends, to a large degree, on the formulation of the outcome or objective, which is typically done by human experts. In this paper, we propose a novel approach of automatically generating new optimization heuristics using inductive program synthesis. To explore the feasibility of this approach, we investigated the graph-coloring register allocation heuristic used in the state-of-the-art compilers today. In particular, we focused on the task of live range splitting. The results show that when using a Genetic Algorithm, we can obtain splitting heuristics that are within 10% of the optimal split after 202 generations.
Best Poster Finalist (BP): no
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