CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911075812769792 |
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| author | Zhao, Yanbo Cui, Jinku Li, Zecheng Jiao, Shuyin Liu, Xu Li, Jiajia |
| author_facet | Zhao, Yanbo Cui, Jinku Li, Zecheng Jiao, Shuyin Liu, Xu Li, Jiajia |
| contents | GPUs have become indispensable in high-performance computing, machine learning, and many other domains. Efficiently utilizing the memory subsystem on GPUs is critical for maximizing computing power through massive parallelism. Analyzing memory access patterns has proven to be an effective method for understanding memory bottlenecks in applications. However, comprehensive runtime and fine-grained memory profiling support is lacking on GPU architectures. In this work, we introduce cuThermo, a lightweight and practical profiling tool for GPU memory analysis. It operates on GPU binaries without requiring any modifications to hardware, operating system, or application source code. Given a CUDA application, cuThermo identifies memory inefficiencies at runtime via a heat map based on distinct visited warp counts to represent word-sector-level data sharing and provides optimization guidance in performance tuning iterations. Through our experiments on six applications, we identified five memory access patterns that are portable across different GPU architectures. By evaluating optimization on two GPUs, cuThermo achieves up to $721.79\%$ performance improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling Zhao, Yanbo Cui, Jinku Li, Zecheng Jiao, Shuyin Liu, Xu Li, Jiajia Distributed, Parallel, and Cluster Computing Performance GPUs have become indispensable in high-performance computing, machine learning, and many other domains. Efficiently utilizing the memory subsystem on GPUs is critical for maximizing computing power through massive parallelism. Analyzing memory access patterns has proven to be an effective method for understanding memory bottlenecks in applications. However, comprehensive runtime and fine-grained memory profiling support is lacking on GPU architectures. In this work, we introduce cuThermo, a lightweight and practical profiling tool for GPU memory analysis. It operates on GPU binaries without requiring any modifications to hardware, operating system, or application source code. Given a CUDA application, cuThermo identifies memory inefficiencies at runtime via a heat map based on distinct visited warp counts to represent word-sector-level data sharing and provides optimization guidance in performance tuning iterations. Through our experiments on six applications, we identified five memory access patterns that are portable across different GPU architectures. By evaluating optimization on two GPUs, cuThermo achieves up to $721.79\%$ performance improvement. |
| title | CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2507.18729 |