DOPA: GPU-based protein alignment using database and memory access optimizations.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• The Smith-Waterman (S-W) algorithm provides optimal biological sequence alignment but is computationally intensive.
• Existing heuristic methods like FASTA and BLAST offer speed at the expense of accuracy.
• Growing sequence data necessitates efficient database restructuring for faster alignment.

Purpose of the Study:

• To develop a high-performance protein sequence alignment implementation that overcomes the limitations of existing methods.
• To accelerate the Smith-Waterman algorithm for practical applications in large-scale biological databases.

Main Methods:

• Implementation of a novel protein sequence alignment algorithm on Graphics Processing Units (GPUs).
• Optimization of database organization to minimize memory access and reduce bandwidth bottlenecks.
• Development of the Database Optimized Protein Alignment (DOPA) system.

Main Results:

• The DOPA implementation achieves a performance of 21.4 Giga Cell Updates Per Second (GCUPS).
• This represents a 1.13-fold improvement over the previous fastest GPU-based implementation.
• The optimized database organization effectively distributes workload across GPU multiprocessors.

Conclusions:

• The DOPA system offers a significant performance enhancement for protein sequence alignment.
• Organizing databases into equal-length sequence sets is key to efficient GPU utilization.
• This GPU-based approach provides a faster and more accurate alternative for biological sequence analysis.