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The integration of structure from motion (SFM) and unmanned aerial vehicle (UAV) technologies has allowed for the generation of very high-resolution three-dimensional (3D) point cloud data (up to millimeters) to detect and monitor surface changes. However, a bottleneck still exists in accurately and rapidly registering the point clouds at different times. The existing point cloud registration algorithms, such as the Iterative Closest Point (ICP) and the Fast Global Registration (FGR) method, were mainly developed for the registration of small and static point cloud data, and do not perform well when dealing with large point cloud data with potential changes over time. In particular, registering large data is computationally expensive, and the inclusion of changing objects reduces the accuracy of the registration. In this paper, we develop an AI-based workflow to ensure high-quality registration of the point clouds generated using UAV-collected photos. We first detect stable objects from the ortho-photo produced by the same set of UAV-collected photos to segment the point clouds of these objects. Registration is then performed only on the partial data with these stable objects. The application of this workflow using the UAV data collected from three erosion plots at the East Tennessee Research and Education Center indicates that our workflow outperforms the existing algorithms in both computational speed and accuracy. This AI-based workflow significantly improves computational efficiency and avoids the impact of changing objects for the registration of large point cloud data. 

In this paper, we present work towards the development of a new data analytics and machine learning (ML) framework, called MagmaDNN. Our main goal is to provide scalable, high-performance data analytics and ML solutions for scientific applications running on current and upcoming heterogeneous many-core GPU-accelerated architectures. To this end, since many of the functionalities needed are based on standard linear algebra (LA) routines, we designed MagmaDNN to derive its performance power from the MAGMA library. The close integration provides the fundamental (scalable high-performance) LA routines available in MAGMA as a backend to MagmaDNN. We present some design issues for performance and scalability that are specific to ML using Deep Neural Networks (DNN), as well as the MagmaDNN designs towards overcoming them. In particular, MagmaDNN uses well established HPC techniques from the area of dense LA, including task-based parallelization, DAG representations, scheduling, mixed-precision algorithms, asynchronous solvers, and autotuned hyperparameter optimization. We illustrate these techniques and their incorporation and use to outperform other frameworks, currently available. 

MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the framework providing fast memory operations, direct cuda access, and compile time errors. Common neural network layers such as Fully Connected, Convolutional, Pooling, Flatten, and Dropout are included. Hyperparameter tuning is performed with a parallel grid search engine. MagmaDNN uses several techniques to accelerate network training. For instance, convolutions are performed using the Winograd algorithm and FFTs. Other techniques include MagmaDNNs custom memory manager, which is used to reduce expensive memory transfers, and accelerated training by distributing batches across GPU nodes. This paper provides an overview of the MagmaDNN framework and how it leverages the MAGMA library to attain speed increases. This paper also addresses how deep networks are accelerated by training in parallel and further challenges with parallelization. 

openDIEL is a workflow engine that aims to give researchers and users of HPC an efficient way to coordinate, organize, and interconnect many disparate modules of computation in order to effectively utilize and allocate HPC resources [13]. A GUI has been developed to aid in creating workflows, and allows for the specification of data science jobs, including specification neural network architectures, data processing, and hyperparameter tuning. Existing machine learning tools can be readily used in the openDIEL, allowing for easy experimentation with various models and approaches.