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These results identify base-model capability, target knowledge, execution feedback, and same-target measurement as central to cross-architecture kernel generation as central to cross-architecture kernel generation.
PETSCAgent-Bench is introduced, a multidimensional benchmark and agent-based framework for assessing whether AI-generated scientific code uses a production HPC library as an expert would and combines deterministic checks with LLM-based assessments.
HLSmith, an expert-guided framework for translating C/C++ programs into optimized HLS accelerators, is presented and evaluated on PolyBench against ChatHLS, a leading prior agent-orchestration framework for HLS accelerator development.
This work position PETSc as a domain-aware component in multi-step AI workflows that span question answering, code development, execution, and verification, and describes the infrastructure and prototype services that support these capabilities and outline their potential to enable more effective, reliable, and scalabl...
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