8 research outputs found
Accuracy and Performance Comparison of Video Action Recognition Approaches
Over the past few years, there has been significant interest in video action
recognition systems and models. However, direct comparison of accuracy and
computational performance results remain clouded by differing training
environments, hardware specifications, hyperparameters, pipelines, and
inference methods. This article provides a direct comparison between fourteen
off-the-shelf and state-of-the-art models by ensuring consistency in these
training characteristics in order to provide readers with a meaningful
comparison across different types of video action recognition algorithms.
Accuracy of the models is evaluated using standard Top-1 and Top-5 accuracy
metrics in addition to a proposed new accuracy metric. Additionally, we compare
computational performance of distributed training from two to sixty-four GPUs
on a state-of-the-art HPC system.Comment: Accepted for publication at IEEE HPEC 202
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
Deployment of Real-Time Network Traffic Analysis using GraphBLAS Hypersparse Matrices and D4M Associative Arrays
Matrix/array analysis of networks can provide significant insight into their
behavior and aid in their operation and protection. Prior work has demonstrated
the analytic, performance, and compression capabilities of GraphBLAS
(graphblas.org) hypersparse matrices and D4M (d4m.mit.edu) associative arrays
(a mathematical superset of matrices). Obtaining the benefits of these
capabilities requires integrating them into operational systems, which comes
with its own unique challenges. This paper describes two examples of real-time
operational implementations. First, is an operational GraphBLAS implementation
that constructs anonymized hypersparse matrices on a high-bandwidth network
tap. Second, is an operational D4M implementation that analyzes daily cloud
gateway logs. The architectures of these implementations are presented.
Detailed measurements of the resources and the performance are collected and
analyzed. The implementations are capable of meeting their operational
requirements using modest computational resources (a couple of processing
cores). GraphBLAS is well-suited for low-level analysis of high-bandwidth
connections with relatively structured network data. D4M is well-suited for
higher-level analysis of more unstructured data. This work demonstrates that
these technologies can be implemented in operational settings.Comment: Accepted to IEEE HPEC, 8 pages, 8 figures, 1 table, 69 references.
arXiv admin note: text overlap with arXiv:2203.13934. text overlap with
arXiv:2309.0180
Focusing and Calibration of Large Scale Network Sensors using GraphBLAS Anonymized Hypersparse Matrices
Defending community-owned cyber space requires community-based efforts.
Large-scale network observations that uphold the highest regard for privacy are
key to protecting our shared cyberspace. Deployment of the necessary network
sensors requires careful sensor placement, focusing, and calibration with
significant volumes of network observations. This paper demonstrates novel
focusing and calibration procedures on a multi-billion packet dataset using
high-performance GraphBLAS anonymized hypersparse matrices. The run-time
performance on a real-world data set confirms previously observed real-time
processing rates for high-bandwidth links while achieving significant data
compression. The output of the analysis demonstrates the effectiveness of these
procedures at focusing the traffic matrix and revealing the underlying stable
heavy-tail statistical distributions that are necessary for anomaly detection.
A simple model of the corresponding probability of detection () and
probability of false alarm () for these distributions highlights
the criticality of network sensor focusing and calibration. Once a sensor is
properly focused and calibrated it is then in a position to carry out two of
the central tenets of good cybersecurity: (1) continuous observation of the
network and (2) minimizing unbrokered network connections.Comment: Accepted to IEEE HPEC, 9 pages, 12 figures, 1 table, 63 references, 2
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