The DBSCAN method for spatial clustering has received significant attention due to its
applicability in a variety of data analysis tasks. There are fast sequential algorithms for …

Clustering is used to extract hidden patterns and similar groups from data. Therefore,
clustering as a method of unsupervised learning is a crucial technique for big data analysis …

I/O performance is a critical aspect of data-intensive scientific computing. We seek to
advance the state of the practice in understanding and diagnosing I/O performance issues …

Moving toward exascale computing, the size of data stored and accessed by applications is
ever increasing. However, traditional disk-based storage has not seen improvements that …

High performance computing (HPC) architectures have been adding new layers of storage,
such as burst buffers, to tolerate latency between memory and disk-based file systems …

We propose a dynamically load-balanced algorithm for parallel particle tracing, which
periodically attempts to evenly redistribute particles across processes based on kd tree …

Dbscan is a density-based clustering algorithm which is well known for its ability to discover
clusters of arbitrary shape as well as to distinguish noise. As it is computationally expensive …

DBSCAN is one of the most popular and effective clustering algorithms that is capable of
identifying arbitrary-shaped clusters and noise efficiently. However, its super-linear …

I/O efficiency is essential to productivity in scientific computing, especially as many scientific
domains become more data-intensive. Many characterization tools have been used to …

Emerging high performance computing (HPC) systems are expected to be deployed with an
unprecedented level of complexity due to a deep system memory and storage hierarchy …