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Publications of SPCL
| P. Schaad, T. Ben-Nun, P. Iff, T. Hoefler: | ||
| Inductive Loop Analysis for Practical HPC Application Optimization (arXiv:2511.06052. Nov. 2025) AbstractScientific computing applications heavily rely on multi-level loop nests operating on multidimensional arrays. This presents multiple optimization opportunities from exploiting parallelism to reducing data movement through prefetching and improved register usage. HPC frameworks often delegate fine-grained data movement optimization to compilers, but their low-level representations hamper analysis of common patterns, such as strided data accesses and loop-carried dependencies. In this paper, we introduce symbolic, inductive loop optimization (SILO), a novel technique that models data accesses and dependencies as functions of loop nest strides. This abstraction enables the automatic parallelization of sequentially-dependent loops, as well as data movement optimizations including software prefetching and pointer incrementation to reduce register spills. We demonstrate SILO on fundamental kernels from scientific applications with a focus on atmospheric models and numerical solvers, achieving up to 12× speedup over the state of the artDocumentsdownload article: access preprint on arxiv:  | ||
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