Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species - Sathi Lakshmi Teja Sri; Manas Kumar Yogi (International Journal of Algorithms Design and Analysis Review, 2026)
The rebuilding of phylogenetic trees for marine species shows major computing problems because of the massive genomic data and the huge biodiversity inherent in ocean ecosystems. Traditional phylogenetic methods are accurate but become more expensive when they are processing with thousands of marine taxa parallelly. This article shows a critical analysis of parallel greedy algorithms as an adaptable solution for large-scale marine phylogenetics. It examines the main principles of greedy heuristics applied in the construction of tree based on distance, specifically on neighbor joining and its variations. The paper demonstrates that the implementation of the parallelization techniques, data parallelism, task parallelism, and the hybrid models may be utilized to overcome the time complexity factors that restrict the use of the traditional implementation of the usual methods in a quadratic manner. The analysis integrates current advances in shared computing architectures, graphical processing units (GPU) acceleration, and algorithmic optimizations that show the working of marine metagenomic datasets that have tens of thousands of operational taxonomic units. Performance improvements are evaluated in various fields of marine research, such as microbial community studies and vertebrate evolutionary studies, and examples of nearly linear speedup of parallel greedy approaches on high-performance computing clusters have been documented. In addition to this, we discuss the synthesis of these methods with advanced technologies such as cloud computing and software containerization to enable accessible marine biodiversity studies. The results show that parallel greedy methods decrease phylogenetic rebuilding time complexity from days to hours for datasets containing 10,000+ marine species, showcasing live analysis of natural samples and providing large-scale evolutionary studies crucial for conservation and climate impact assessment.
Keywords: High-performance computing (HPC); Scalable phylogenetic pipelines; marine genomics; metagenomic datasets; parallel greedy algorithms; phylogenetic tree reconstruction