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| Robert Kennedy is a PhD Candidate at Florida Atlantic University. He is completing a PhD in Computer Science and joined the HPCC Systems Intern program for the third year running in 2020. During his internships, Robert has focused on machine learning projects relating to neural networks and has been using HPCC Systems with TensorFlow.
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Poster Abstract
HPCC Systems Platform leverages many commodity computers to perform high performance computing tasks. The underlying hardware traditionally only provide a CPU for the actual computations and communicate with other member computers via networking protocols. This approach has proven to be very effective for many demanding applications. However, training large neural networks–or Deep Learning–is best benefited by utilizing hardware acceleration for the bulk of the computationally expensive tasks.
This poster presents the results of my Summer 2020 internship project that expands HPCC Systems and its General Neural Network (GNN) bundle by leveraging multiple GPUs that span across a cluster. Using hardware acceleration with the GNN bundle allows the ECL machine learning developer to drastically reduce training time. Further, this work is not limited to one GPU nor one physical computer. This work demonstrates that it is now possible to spread GNN computations over multiple GPUs either multiple GPUs in one machine or across multiple GPUs across multiple machines.
Presentation
In this Video Recording, Robert provides a tour and explanation of his poster content.
Poster Title: Distributed GPU Accelerated Neural Networks with GNN
Click on the poster for a larger image.
