This thesis summarizes our project in building and setting up a Beowulf cluster. The idea of the project was brought forward by the company CGI in Karlstad, Sweden. CGI’s wish is that the project will serve as a starting point for future research and development of a larger Beowulf cluster. The future work can be made by both employees at CGI and student exam projects from universities. The projects main purpose was to construct a cluster by using several credit card sized single board computers, in our case the Raspberry Pi 3. The process of installing, compiling and con- figuring software for the cluster is explained. The MPICH and TensorFlow software platforms are reviewed. A performance evaluation of the cluster with TensorFlow is given. A single Raspberry Pi 3 can perform neural network training at a rate of seven times slower than an Intel system (i5-5250U at 2.7 GHz and 8 GB RAM at 1600 MHz). The performance degraded significantly when the entire cluster was training. The precise cause of the performance degradation was not found, but is ruled out to be in software, either a programming error or a bug in TensorFlow.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kau-55074 |
| Date | January 2017 |
| Creators | Bleeker, Ellen-Louise, Reinholdsson, Magnus |
| Publisher | Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf, application/pdf |
| Rights | info:eu-repo/semantics/openAccess, info:eu-repo/semantics/openAccess |
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