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http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18502
Title: | An AI-driven Resource-Recommendation System for Cloud HPC |
Authors: | Λιακόπουλος, Δημήτριος Σούντρης Δημήτριος |
Keywords: | Resource-Recommendation System High-Performance Computing Benchmarks Machine Learning Deep Learning Neural Networks Multi-Layer Perceptron Cloud Computing Parallel Computing Hardware Acceleration Graphics Processing Unit Intelligence Processing Unit |
Issue Date: | 27-Oct-2022 |
Abstract: | Ιn recent years, the Cloud has evolved into a powerful environment with many instances available for the user to choose. Especially, for High-Performance Computing (HPC) applications the vast amount of available hardware leaves the user with a challenging choice to make. When making this choice, many aspects should be taken into account, in particular the available budget and the time-frame of the project. This information, although fundamental, is really hard to acquire before project completion. In this thesis, a Resource-Recommendation System, based on Neural-Networks (NNs), is proposed in order to help the user determine which instance fits their needs best and help them make their choice. For these reasons, different Multi-Layer Perceptrons (MPLs) where selected and evaluated on different High-Performance Computing (HPC) benchmarks, both synthetic and application. Initially, raw-data was collected from different Cloud instances for each benchmark and organised in a proper way to later be used. The next phase, was the data-prepossessing, in which each data-set was surveyed in order to determine the available trends and a synthetically-generated, more complete data-set was created for the majority of the benchmarks. Lastly, the new syntheticallygenerated data-set was encoded in a proper way and it was fed into the Neural Network (NN) in order to predict the execution times of each benchmark. In total, a combination of six different benchmarks and five Multi-Layer Perceptros (MLPs) were trained and evaluated. Last but not least, the training of the Neural-Networks (NNs) was realised both on Graphics Processing Units (GPUs) and Graphcore’s Intelligence Processing Units (IPUs). |
URI: | http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18502 |
Appears in Collections: | Διπλωματικές Εργασίες - Theses |
Files in This Item:
File | Description | Size | Format | |
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Dimitris_Liakopoulos_Ntua_Diploma_Thesis.pdf | 17.32 MB | Adobe PDF | View/Open |
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