ADEPT-T adds traceback functionality to ADEPT, a GPU accelerated implementation of the Smith-Waterman alignment algorithm. Implementation details of ADEPT can be found in the publication here: https://rdcu.be/b7fhY. ADEPT uses GPU's two level parallelism to perform multiple sequence alignments in batches while using fine grained parallelism to accelerate each individual alignment. Overall it provides several time faster performance in comparison to existing SIMD implementations for CPU, a comparative study with existing CPU and GPU methods has been provided in the publication mentioned above. ADEPT performs a complete smith-waterman alignment with affine gap penalities and can align both protein and DNA sequences.
ADEPT-T introduces a novel diagonal major indexing method combined with a compressed binary representation of the traceback matrices to accelerate it on GPUs. Our analysis demonstrates that ADEPT-T is 3.5x and 35x faster than traceback stages in the current state-of-the-art SW libraries on GPU and CPU respectively. ADEPT-T's integration in MetaHipMer, a widely used large-scale genome assembler, demonstrated an overall speedup of 9% and accelerated the alignment module by 32% on average. The github for the MHM2 integration is here: https://github.com/leannmlindsey/mhm2.git
ADEPT-T provides a driver function that separates CUDA code from the main application which enables easy use and integeration in existing applications, effectively providing a drop in replacement for CPU libraries. The driver also enables balancing of alignments across all the GPUs available on a system.
mkdir build
cd build
cmake CMAKE_BUILD_TYPE=Release ..
make
./program_gpu dna ../test-data/dna-reference.fasta ../test-data/dna-query.fasta ./out_file
./program_gpu aa ../test-data/protein-reference.fasta ../test-data/protein-query.fasta ./out_file
If you need help modifying the library to match your specific use-case or for other issues and bug reports please open an issue or reach out at leann.lindsey@utah.edu
Paper under review
GPU accelerated Smith-Waterman for performing batch alignments (GPU-BSW) Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
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