Denoising Application Performance Models with Noise-Resilient Priors
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916911643623424 |
|---|---|
| author | de Morais, Gustavo Geiß, Alexander Calotoiu, Alexandru Corbin, Gregor Tarraf, Ahmad Hoefler, Torsten Mohr, Bernd Wolf, Felix |
| author_facet | de Morais, Gustavo Geiß, Alexander Calotoiu, Alexandru Corbin, Gregor Tarraf, Ahmad Hoefler, Torsten Mohr, Bernd Wolf, Felix |
| contents | As parallel codes are scaled to larger computing systems, performance models play a crucial role in identifying potential bottlenecks. However, constructing these models analytically is often challenging. Empirical models based on performance measurements provide a practical alternative, but measurements on high-performance computing (HPC) systems are frequently affected by noise, which can lead to misleading predictions. To mitigate the impact of noise, we introduce application-specific dynamic priors into the modeling process. These priors are derived from noise-resilient measurements of computational effort, combined with domain knowledge about common algorithms used in communication routines. By incorporating these priors, we effectively constrain the model's search space, eliminating complexity classes that capture noise rather than true performance characteristics. This approach keeps the models closely aligned with theoretical expectations and substantially enhances their predictive accuracy. Moreover, it reduces experimental overhead by cutting the number of repeated measurements by half. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Denoising Application Performance Models with Noise-Resilient Priors de Morais, Gustavo Geiß, Alexander Calotoiu, Alexandru Corbin, Gregor Tarraf, Ahmad Hoefler, Torsten Mohr, Bernd Wolf, Felix Performance Distributed, Parallel, and Cluster Computing As parallel codes are scaled to larger computing systems, performance models play a crucial role in identifying potential bottlenecks. However, constructing these models analytically is often challenging. Empirical models based on performance measurements provide a practical alternative, but measurements on high-performance computing (HPC) systems are frequently affected by noise, which can lead to misleading predictions. To mitigate the impact of noise, we introduce application-specific dynamic priors into the modeling process. These priors are derived from noise-resilient measurements of computational effort, combined with domain knowledge about common algorithms used in communication routines. By incorporating these priors, we effectively constrain the model's search space, eliminating complexity classes that capture noise rather than true performance characteristics. This approach keeps the models closely aligned with theoretical expectations and substantially enhances their predictive accuracy. Moreover, it reduces experimental overhead by cutting the number of repeated measurements by half. |
| title | Denoising Application Performance Models with Noise-Resilient Priors |
| topic | Performance Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.10996 |