← 研踪 PaperTrailarXiv:2611.13762 · v1
Exploiting What NVIDIA Profilers Hide: Fast and Accurate Analytical GPU Performance Modeling
首次提交 2026-11-10 17:02
版本更新 2026-11-10 17:02
雷达观测 2026-11-11 11:04
摘要
GPUs have become essential to many fields, including AI and scientific computing. As GPUs grow more complex, the design space of their hardware configurations becomes increasingly large, yet cycle-level simulators remain too slow to explore it. Analytical models make this exploration much faster. However, existing GPU analytical models fall short of accurately predicting performance across hardware configurations, because they re-estimate or scale every stall of a warp on the target configuration. We observe that fixed-latency stalls stay the same across configurations, whereas queueing stalls vary with the pressure on each hardware unit. Based on this observation, we propose ForeSight, a queueing analytical model that reuses the fixed-latency stalls measured on GPU hardware and recomputes the queueing stalls of each hardware unit from its pressure. To provide ForeSight with the counter values of each hardware unit, we build UnitSight, a profiler that recovers these values, which NVIDIA's official profilers hide by aggregating across units. Our evaluation on hardware configurations of nine NVIDIA GPUs shows that ForeSight predicts kernel execution time with a mean absolute percentage error (MAPE) of 8.3%, compared with 14.1% for the best baseline. ForeSight also predicts the end-to-end execution time of LLM inference on vLLM with a MAPE of 2.0%. It runs 49x faster than the cycle-level simulator Accel-Sim and faster than interval-based models such as GCoM. With the aid of ForeSight, we explore configurations derived from the H20 GPU for LLM inference and find one with 1.78x its performance/cost.
作者备注：13 pages, 7 figures, 2 tables
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v1 · 2026-11-10 17:02 · 最新观测版本
Exploiting What NVIDIA Profilers Hide: Fast and Accurate Analytical GPU Performance Modeling
GPUs have become essential to many fields, including AI and scientific computing. As GPUs grow more complex, the design space of their hardware configurations becomes increasingly large, yet cycle-level simulators remain too slow to explore it. Analytical models make this exploration much faster. However, existing GPU analytical models fall short of accurately predicting performance across hardware configurations, because they re-estimate or scale every stall of a warp on the target configuration. We observe that fixed-latency stalls stay the same across configurations, whereas queueing stalls vary with the pressure on each hardware unit. Based on this observation, we propose ForeSight, a queueing analytical model that reuses the fixed-latency stalls measured on GPU hardware and recomputes the queueing stalls of each hardware unit from its pressure. To provide ForeSight with the counter values of each hardware unit, we build UnitSight, a profiler that recovers these values, which NVIDIA's official profilers hide by aggregating across units. Our evaluation on hardware configurations of nine NVIDIA GPUs shows that ForeSight predicts kernel execution time with a mean absolute percentage error (MAPE) of 8.3%, compared with 14.1% for the best baseline. ForeSight also predicts the end-to-end execution time of LLM inference on vLLM with a MAPE of 2.0%. It runs 49x faster than the cycle-level simulator Accel-Sim and faster than interval-based models such as GCoM. With the aid of ForeSight, we explore configurations derived from the H20 GPU for LLM inference and find one with 1.78x its performance/cost.
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