GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval Analysis
Hanna Cha, Sungchul Lee, Jounghoo Lee, Yeonan Ha, Joonsung Kim, Youngsok Kim
2025年份
1被引次数
摘要
To design next-generation Graphics Processing Units (GPUs), GPU architects rely on GPU performance analyses to identify key GPU performance bottlenecks and explore GPU design spaces.Unfortunately, the existing GPU performance analysis mechanisms make it difficult for GPU architects to conduct fast and accurate GPU performance analyses.The existing mechanisms can provide misleading
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- GCoM: a detailed GPU core model for accurate analytical modeling of modern GPUsJounghoo Lee, Yeonan Ha, Suhyun Lee, Jinyoung Woo 等ISCA 2022 · 被引用 25 次
- AccelWattch: A Power Modeling Framework for Modern GPUsVijay Kandiah, Scott Peverelle, Mahmoud Khairy, Junrui Pan 等MICRO 2021 · 被引用 134 次
- ValueExpert: exploring value patterns in GPU-accelerated applicationsKeren Zhou, Yueming Hao, John M. Mellor-Crummey, Xiaozhu Meng 等ASPLOS 2022 · 被引用 19 次
- GPU Scale-Model SimulationHossein SeyyedAghaei, Mahmood Naderan-Tahan, Lieven EeckhoutHPCA 2024 · 被引用 13 次
- PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance PredictionKaixuan Zhang, Yunfan Cui, Shuhao Zhang, Chutong Ding 等ISCA 2026 · 被引用 2 次
