With the advent of the big data era, the amounts of sampling data and the dimensions of
data features are rapidly growing. It is highly desired to enable fast and efficient clustering of …

Dimensionality reduction refers to a set of mathematical techniques used to reduce
complexity of the original high‐dimensional data, while preserving its selected properties …

A distance matrix is simply an n× n two-dimensional array that contains pairwise distances of
a set of n points in a metric space. It has a wide range of usage in several fields of scientific …

Develo** high-performance applications on emerging multi-and many-core architectures
requires efficient map** techniques and architecture-specific tuning methodologies to …

Data from sequencing instruments are produced at such rates that their analysis is
becoming increasingly computationally challenging. Although DNA sequence clustering of …

High-throughput mass spectrometers can produce thousands of peptide spectra from a
single complex protein sample in a short amount of time. These data sets contain a …

Modern mass spectrometers can produce large numbers of peptide spectra from complex
biological samples in a short time. A substantial amount of redundancy is observed in these …

Sequence clustering is an important computational step in numerous bioinformatics
applications such as high-throughput immune system characterization, marker-based …

Multicore processors can provide sufficient computing power and flexibility for complex
streaming applications, such as high-definition video processing. For less hardware …

Today's emerging architectures have higher levels of parallelism incorporated within a
processor. They require efficient strategies to extract the performance they have to offer. In …