ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation
Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenCL, and OpenMP target offload. Large language models and autonomous coding agents are increasingly proposed for such migration, but the field lacks reliable ways to measure whether they preserve the low-level parallel semantics that make translations behaviorally valid, including thread indexing, synchronization, memory management, host-device coordination, and API-specific execution structure. We present ParBench, a kernel-centric benchmark framework for evaluating LLM-based parallel API translation under execut...
| Authors | Samyak Jhaveri, Erel Kaplan, Tom Yotam et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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