A quantitative study of deep learning training on heterogeneous supercomputers
Deep learning (DL) has become a key technique for solving complex problems in scientific
research and discovery. DL training for science is substantially challenging because it has to …
research and discovery. DL training for science is substantially challenging because it has to …
Bespokv: Application tailored scale-out key-value stores
Enterprise KV stores are not well suited for HPC applications, and entail customization and
cumbersome end-to-end KV design to extract the HPC application needs. To this end, in this …
cumbersome end-to-end KV design to extract the HPC application needs. To this end, in this …
LIRS: Enabling efficient machine learning on NVM-based storage via a lightweight implementation of random shuffling
Machine learning algorithms, such as Support Vector Machine (SVM) and Deep Neural
Network (DNN), have gained a lot of interests recently. When training a machine learning …
Network (DNN), have gained a lot of interests recently. When training a machine learning …
Customizable scale-out key-value stores
Enterprise KV stores are often not well suited for HPC applications, and thus cumbersome
end-to-end KV design customization is required to meet the needs of modern HPC …
end-to-end KV design customization is required to meet the needs of modern HPC …
A scalable pipeline for gigapixel whole slide imaging analysis on leadership class hpc systems
Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological
features at multiple res-olutions organized across different levels. Images produced by WSI …
features at multiple res-olutions organized across different levels. Images produced by WSI …
An experimental study on deep learning based on different hardware configurations
J Li, C Zhang, Q Cao, C Qi, J Huang… - … , and Storage (NAS), 2017 - ieeexplore.ieee.org
Deep learning has exhibited high accuracy and applicability in machine learning field
recently, by consuming tremendous computational resources processing massive data. To …
recently, by consuming tremendous computational resources processing massive data. To …
[HTML][HTML] Data Readiness and Data Exploration for Successful Power Line Inspection
E Antwi-Bekoe, GT Maale, EM Martey, W Asiedu… - 2023 - intechopen.com
Sufficiently large, curated, and representative training data remains key to successful
implementation of deep learning applications for wide-scale power line inspection …
implementation of deep learning applications for wide-scale power line inspection …
Analyzing the Interplay Between Random Shuffling and Storage Devices for Efficient Machine Learning
Machine learning algorithms, such as Support Vector Machine (SVM) and Deep Neural
Network (DNN), have gained a lot of interest recently. When training a machine learning …
Network (DNN), have gained a lot of interest recently. When training a machine learning …
A co-occurrence background model with hypothesis on degradation modification for object detection in strong background changes
Object detection has become an indispensable part of video processing and current
background models are sensitive to background changes. In this paper, we propose a novel …
background models are sensitive to background changes. In this paper, we propose a novel …
Performance implications of big data in scalable deep learning: on the importance of bandwidth and caching
M Hodak, D Ellison, P Seidel… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
Deep learning techniques have revolutionized many areas including computer vision and
speech recognition. While such networks require tremendous amounts of data, the …
speech recognition. While such networks require tremendous amounts of data, the …