Introduction into parallel computing through deep learning

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dc.contributor.advisorKovács, László
dc.contributor.authorSáfrány, Artúr
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2020-05-12T10:58:19Z
dc.date.available2020-05-12T10:58:19Z
dc.date.created2020
dc.description.abstractThis thesis provides an overview of different faces of parallel computing and highlights the importance of parallelization through deep learning. It covers the core aspects of neural networks. Furthermore, it gives an insight into a self-made implementation called Borjomi. It gives a brief historical overview of parallelism, and without going into deep technical details, it covers various types of it, including task-level, instruction-level, and data-level parallelism. About neural networks, it presents some core aspects, the main components and the basic workflow, while concentrating more on technical and implementation details rather than the scientific background. After an overall overview, the focus is more on convolutional networks. At the end of the paper, the last part provides insight into my own deep learning implementation called Borjomi, which is a lightweight deep learning framework, specialized for convolutional networks. It presents the main structure of the project, the tensor implementation, and the management of layer connections. Also, it highlights the capabilities and the supported architectures which can be used to boost Borjomi with different kinds of parallelization techniques.hu_HU
dc.description.correctorÁthelyezve a megfelelő gyűjteménybe. PF
dc.description.courseProgramtervező Informatikushu_HU
dc.description.degreeBSc/BAhu_HU
dc.format.extent37hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/286925
dc.language.isoenhu_HU
dc.subjectparallelisationhu_HU
dc.subjectneural networkshu_HU
dc.subjectdeep learninghu_HU
dc.subjectmultithreadinghu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleIntroduction into parallel computing through deep learninghu_HU
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