From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation
arXiv 2602.22240•6ba4188f5da543a2ccc94784c48156d5c726e35570724ccbaf5842ffe48ba338
ACSLC++-parallelismFrama-CHPXIEEE-754LLVMOpenMPRustTorchLeanarrayscode-generationconcurrencydata-racefloating-point-semantics','certificate-checking','IBP','LiRPA','formal-verificationfunction-contractslarge-language-modelsmemory-safetyneural-network-verificationparallelismscalabilitystatic-analysissymbolic-executionundefined-behaviorunsafe-code
Paper metadata
- arXiv ID
- 2602.22240
- Version
- Not specified by this published record
- Category
- Computer Science — Programming Languages (cs.PL)
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Evidence and limitations
- Source ID
- arxiv_cs_pl
- Record identifier
- 6ba4188f5da543a2ccc94784c48156d5c726e35570724ccbaf5842ffe48ba338
- Enrichment time
- 2026-03-04T19:54:26Z
- AI-assisted enrichment
- Yes
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