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Accelerating Graph Processing On Large-Scale Multicores, Masab Ahmad
Accelerating Graph Processing On Large-Scale Multicores, Masab Ahmad
Doctoral Dissertations
With the ever-increasing amount of data and input variations, portable performance is becoming harder to exploit on today’s architectures. Computational setups utilize single-chip processors, such as GPUs or large-scale multicores for graph analytics. Some algorithm-input combinations perform more efficiently when utilizing a GPU’s higher concurrency and bandwidth, while others perform better with a multicore’s stronger data caching capabilities. Architectural choices also occur within selected accelerators, where variables such as threading and thread placement need to be decided for optimal performance. This paper proposes a performance predictor paradigm for a heterogeneous parallel architecture where multiple disparate accelerators are integrated in an …
Towards Provable And Scalable Machine Learning, Jin Lu
Towards Provable And Scalable Machine Learning, Jin Lu
Doctoral Dissertations
In the recent decade, machine learning has been substantially developed and has demonstrated great success in various domains such as web search, computer vision, and natural language processing. Despite of its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. In this dissertation, we show that if a certain structure occurs in sample data, it is possible to solve the related problem with provable guarantees. We propose to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, …
Mapping Relict Charcoal Hearths In The Northeast Us Using Deep Learning Convolutional Neural Networks And Lidar Data, Eli Anderson
Mapping Relict Charcoal Hearths In The Northeast Us Using Deep Learning Convolutional Neural Networks And Lidar Data, Eli Anderson
Master's Theses
Advanced machine learning combined with widespread, publicly available, airborne light detection and ranging (LiDAR) data has great potential to automate the extraction and classification of landforms and 17th-early 20th century land use features preserved under forest canopy throughout the northeast US landscape. Previous studies have shown that stone walls, house foundations and relict charcoal hearths (RCHs) stand out clearly in derivative LiDAR digital elevation model (DEM) products such as slope and hillshade maps, but to date, mapping has been mainly carried out by on screen digitization. In this study, a deep learning convolutional neural network (CNN) algorithm …