2 research outputs found
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment
This paper describes a scalable active learning pipeline prototype for
large-scale brain mapping that leverages high performance computing power. It
enables high-throughput evaluation of algorithm results, which, after human
review, are used for iterative machine learning model training. Image
processing and machine learning are performed in a batch layer. Benchmark
testing of image processing using pMATLAB shows that a 100 increase in
throughput (10,000%) can be achieved while total processing time only increases
by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust
scalability. The images and algorithm results are provided through a serving
layer to a browser-based user interface for interactive review. This pipeline
has the potential to greatly reduce the manual annotation burden and improve
the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets
Our society has never been more dependent on computer networks. Effective
utilization of networks requires a detailed understanding of the normal
background behaviors of network traffic. Large-scale measurements of networks
are computationally challenging. Building on prior work in interactive
supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have
developed an efficient method for computing a wide variety of streaming network
quantities on diverse time scales. Applying these methods to 100,000,000,000
anonymized source-destination pairs collected at a network gateway reveals many
previously unobserved scaling relationships. These observations provide new
insights into normal network background traffic that could be used for anomaly
detection, AI feature engineering, and testing theoretical models of streaming
networks.Comment: 6 pages, 6 figures,3 tables, 49 references, accepted to IEEE HPEC
202