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dc.contributor.authorHamam, Alwaleed A.
dc.contributor.authorKhan, Ayaz H.
dc.date.accessioned2017-05-04T12:33:23Z
dc.date.available2017-05-04T12:33:23Z
dc.date.issued2017-03-13
dc.identifier.urihttp://hdl.handle.net/10754/623352
dc.description.abstractDeep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
dc.titleExploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
dc.typePoster
dc.conference.dateMarch 13-15, 2017
dc.conference.nameHigh Performance Computing Saudi Arabia (HPC Saudi) 2017
dc.conference.locationKAUST
dc.contributor.institutionComputer Science Department, College of Computer, Qassim University, Saudi Arabia
refterms.dateFOA2018-06-13T14:24:24Z


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