Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909803937267712 |
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| author | Poptani, Akash Khadem, Alireza Mahlke, Scott Miller, Jonah Dolence, Joshua Das, Reetuparna |
| author_facet | Poptani, Akash Khadem, Alireza Mahlke, Scott Miller, Jonah Dolence, Joshua Das, Reetuparna |
| contents | Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon, a block-structured AMR benchmark, on CPU-GPU systems. We show that smaller mesh blocks and deeper AMR levels degrade GPU performance due to increased communication, serial overheads, and inefficient GPU utilization. Through detailed profiling, we identify inefficiencies, low occupancy, and memory access bottlenecks. We further analyze rank scalability and memory constraints, and propose optimizations to improve GPU throughput and reduce memory footprint. Our insights can inform future AMR deployments on Department of Energy's upcoming heterogeneous supercomputers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE Poptani, Akash Khadem, Alireza Mahlke, Scott Miller, Jonah Dolence, Joshua Das, Reetuparna Distributed, Parallel, and Cluster Computing Performance Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon, a block-structured AMR benchmark, on CPU-GPU systems. We show that smaller mesh blocks and deeper AMR levels degrade GPU performance due to increased communication, serial overheads, and inefficient GPU utilization. Through detailed profiling, we identify inefficiencies, low occupancy, and memory access bottlenecks. We further analyze rank scalability and memory constraints, and propose optimizations to improve GPU throughput and reduce memory footprint. Our insights can inform future AMR deployments on Department of Energy's upcoming heterogeneous supercomputers. |
| title | Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2509.19701 |