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AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters

Machine learning (ML) in the past decade has been one of the most popular topics of research within the computing community. Interest within the computing field ranges across all levels of the computation stack. We show this stack in Figure 1. This work introduces an open framework, called AIgean, t...

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Detalles Bibliográficos
Autores principales: Tarafdar, Naif, Guglielmo, Giuseppe Di, Harris, Philip C, Krupa, Jeffrey D, Loncar, Vladimir, Rankin, Dylan S, Tran, Nhan, Wu, Zhenbin, Shen, Qianfeng, Chow, Paul
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1109/fccm48280.2020.00072
http://cds.cern.ch/record/2801430
Descripción
Sumario:Machine learning (ML) in the past decade has been one of the most popular topics of research within the computing community. Interest within the computing field ranges across all levels of the computation stack. We show this stack in Figure 1. This work introduces an open framework, called AIgean, to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). Users can flexibly modify any layer of the machine learning stack in Figure 1 to suit their need. This allows both machine learning domain experts to focus on higher algorithmic layers, and distributed systems experts to create the communication layers below.