Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913872105963520 |
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| author | Caculo, Sriyash Madhav, Mahesh Baxter, Jeff |
| author_facet | Caculo, Sriyash Madhav, Mahesh Baxter, Jeff |
| contents | Accurate performance projection of large-scale benchmarks is essential for CPU architects to evaluate and optimize future processor designs. SimPoint sampling, which uses Basic Block Vectors (BBVs), is a widely adopted technique to reduce simulation time by selecting representative program phases. However, BBVs often fail to capture the behavior of applications with extensive array-indirect memory accesses, leading to inaccurate projections. In particular, the 523.xalancbmk_r benchmark exhibits complex data movement patterns that challenge traditional SimPoint methods. To address this, we propose enhancing SimPoint's BBV methodology by incorporating Memory Access Vectors (MAV), a microarchitecture independent technique that tracks functional memory access patterns. This combined approach significantly improves the projection accuracy of 523.xalancbmk_r on a 192-core system-on-chip, increasing it from 80% to 98%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations Caculo, Sriyash Madhav, Mahesh Baxter, Jeff Hardware Architecture Applications I.6.4; B.8.2; C.4 Accurate performance projection of large-scale benchmarks is essential for CPU architects to evaluate and optimize future processor designs. SimPoint sampling, which uses Basic Block Vectors (BBVs), is a widely adopted technique to reduce simulation time by selecting representative program phases. However, BBVs often fail to capture the behavior of applications with extensive array-indirect memory accesses, leading to inaccurate projections. In particular, the 523.xalancbmk_r benchmark exhibits complex data movement patterns that challenge traditional SimPoint methods. To address this, we propose enhancing SimPoint's BBV methodology by incorporating Memory Access Vectors (MAV), a microarchitecture independent technique that tracks functional memory access patterns. This combined approach significantly improves the projection accuracy of 523.xalancbmk_r on a 192-core system-on-chip, increasing it from 80% to 98%. |
| title | Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations |
| topic | Hardware Architecture Applications I.6.4; B.8.2; C.4 |
| url | https://arxiv.org/abs/2506.02344 |