24,700 research outputs found
Achieving Efficient Strong Scaling with PETSc using Hybrid MPI/OpenMP Optimisation
The increasing number of processing elements and decreas- ing memory to core
ratio in modern high-performance platforms makes efficient strong scaling a key
requirement for numerical algorithms. In order to achieve efficient scalability
on massively parallel systems scientific software must evolve across the entire
stack to exploit the multiple levels of parallelism exposed in modern
architectures. In this paper we demonstrate the use of hybrid MPI/OpenMP
parallelisation to optimise parallel sparse matrix-vector multiplication in
PETSc, a widely used scientific library for the scalable solution of partial
differential equations. Using large matrices generated by Fluidity, an open
source CFD application code which uses PETSc as its linear solver engine, we
evaluate the effect of explicit communication overlap using task-based
parallelism and show how to further improve performance by explicitly load
balancing threads within MPI processes. We demonstrate a significant speedup
over the pure-MPI mode and efficient strong scaling of sparse matrix-vector
multiplication on Fujitsu PRIMEHPC FX10 and Cray XE6 systems
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow
Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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