Parallel Algorithms/Architectures, Computational Structural Biology

In recent years, advances in parallel computing have enabled the creation of multi-processor architectures that enable efficiency and speed in today's computer environment. Parallel Algorithms and Architectures is a comprehensive introduction to this technology, teaching parallelism's basics in a logical and understandable manner. The article develops the algorithms of vector processors, shared-memory parallel machines, and distributed-memory machines, moving from theory to implementation and emphasizing the link between architectures, models, and algorithms. Parallel architectures and parallel algorithms are inextricably linked.

Computational biology has made significant progress. Trends in human health have been discovered through the integration of heterogeneous 'big data,' and disease-associated genes have been identified and categorised. On a different front, researchers are attempting to understand the dynamic arrangement of chromatin in order to obtain insight into the fundamental topic of genome control. Computational biology has been successful in identifying disease-linked genes and in modelling the brain using artificial intelligence neuron connections and electrical flow. Individual sequencing has enabled comparisons of comparable genomes in diseased and healthy tissues, while technical developments have enabled imaging and tracking of molecules in activity in single cells using computational biology.