Abstract
This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.
Highlights
At the beginning of the 21st century, an explosion of information was discovered from the living organisms, especially in areas of molecular biology and genetics
The results show that integrating the parallel efficiency of a cluster with one or more hardware accelerators can significantly increase performance for even the compute/data intensive hidden Markov models (HMM) searches
A better speedup can be obtained with respect to the Amdahl’s law. This observation implies that the performance of parallel version of HMMER including both shared memory and distributed memory parallelism is related to the size of input data set
Summary
At the beginning of the 21st century, an explosion of information was discovered from the living organisms, especially in areas of molecular biology and genetics. For a genome as large as the human genome, it may take many days of CPU time on large-memory, multiprocessor computers to analyze To handle this much data, computational strategies are important to tackle this vital bottleneck, which can aid scientists in the extraction of useful and important biological data. The methods we obtained from the entries of the individual implementations may be useful to many other bioinformatics applications We believe that such an overview is useful for those who want to obtain a general idea about the various means by which these implementations achieved at high performance and high throughput with the most recent computing techniques. The bioinformatics computing research is a very dynamic field and is especially true for the hardware-accelerated cluster world that has emerged at a tremendous rate in the last few years.
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