Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schrödinger Bridge
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915292888694784 |
|---|---|
| author | Bondar, Georgiy A. Gifford, Robert Phan, Linh Thi Xuan Halder, Abhishek |
| author_facet | Bondar, Georgiy A. Gifford, Robert Phan, Linh Thi Xuan Halder, Abhishek |
| contents | We propose to learn the time-varying stochastic computational resource usage of software as a graph structured Schrödinger bridge problem. In general, learning the computational resource usage from data is challenging because resources such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single and multi-core cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller, and a synthetic multi-core software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schrödinger Bridge Bondar, Georgiy A. Gifford, Robert Phan, Linh Thi Xuan Halder, Abhishek Optimization and Control Artificial Intelligence Machine Learning Systems and Control We propose to learn the time-varying stochastic computational resource usage of software as a graph structured Schrödinger bridge problem. In general, learning the computational resource usage from data is challenging because resources such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single and multi-core cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller, and a synthetic multi-core software. |
| title | Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schrödinger Bridge |
| topic | Optimization and Control Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2405.12463 |