{"ID":6267129,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08307","arxiv_id":"2607.08307","title":"Empirical Analysis of GPU Frequency Behavior Under ML Workloads","abstract":"This work presents ongoing research on the frequency scaling behavior of NVIDIA GPUs when executing ML/AI workloads. Our preliminary findings show that, on lower-performance GPUs, the operating frequency is strongly affected by the recent workload history, typically within an 80ms window. This behavior challenges a common assumption underlying several state-of-the-art ML latency-prediction techniques, which treat individual GPU kernel latencies as independent and therefore estimate total execution time by summing isolated per-kernel measurements. Our results indicate that such an assumption does not always hold, as the GPU's dynamic frequency scaling introduces inter-kernel dependencies. We also outline several promising directions for leveraging this observation in future work, including improved latency-prediction models, GPU kernel-reordering strategies, and NAS-driven guidelines for frequency/latency/energy-aware model design.","short_abstract":"This work presents ongoing research on the frequency scaling behavior of NVIDIA GPUs when executing ML/AI workloads. Our preliminary findings show that, on lower-performance GPUs, the operating frequency is strongly affected by the recent workload history, typically within an 80ms window. This behavior challenges a com...","url_abs":"https://arxiv.org/abs/2607.08307","url_pdf":"https://arxiv.org/pdf/2607.08307v1","authors":"[\"Truong-Thanh Le\",\"Hoang-Loc La\",\"Amir Taherkordi\",\"Frank Eliassen\",\"Phuong Hoai Ha\",\"Peiyuan Guan\"]","published":"2026-07-09T09:48:37Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}