SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929403722727424 |
|---|---|
| author | Lersch, Daniel Schram, Malachi Dai, Zhenyu Rajput, Kishansingh Wu, Xingfu Sato, N. Childers, J. Taylor |
| author_facet | Lersch, Daniel Schram, Malachi Dai, Zhenyu Rajput, Kishansingh Wu, Xingfu Sato, N. Childers, J. Taylor |
| contents | Large scale, inverse problem solving deep learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem often poses challenges to the algorithm and requires the proper utilization of high-performance computing systems. Most deep learning algorithms require, due to their design, custom parallelization techniques in order to be resource efficient while showing a reasonable convergence. In this paper we introduces a \underline{S}calable \underline{A}synchronous \underline{G}enerative workflow for solving \underline{I}nverse \underline{P}roblems \underline{S}olver (SAGIPS) on high-performance computing systems. We present a workflow that utilizes a parallelization approach where the gradients of the generator network are updated in an asynchronous ring-all-reduce fashion. Experiments with a scientific proxy application demonstrate that SAGIPS shows near linear weak scaling, together with a convergence quality that is comparable to traditional methods. The approach presented here allows leveraging GANs across multiple GPUs, promising advancements in solving complex inverse problems at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver Lersch, Daniel Schram, Malachi Dai, Zhenyu Rajput, Kishansingh Wu, Xingfu Sato, N. Childers, J. Taylor Distributed, Parallel, and Cluster Computing Large scale, inverse problem solving deep learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem often poses challenges to the algorithm and requires the proper utilization of high-performance computing systems. Most deep learning algorithms require, due to their design, custom parallelization techniques in order to be resource efficient while showing a reasonable convergence. In this paper we introduces a \underline{S}calable \underline{A}synchronous \underline{G}enerative workflow for solving \underline{I}nverse \underline{P}roblems \underline{S}olver (SAGIPS) on high-performance computing systems. We present a workflow that utilizes a parallelization approach where the gradients of the generator network are updated in an asynchronous ring-all-reduce fashion. Experiments with a scientific proxy application demonstrate that SAGIPS shows near linear weak scaling, together with a convergence quality that is comparable to traditional methods. The approach presented here allows leveraging GANs across multiple GPUs, promising advancements in solving complex inverse problems at scale. |
| title | SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.00051 |