Multiple Sequence Alignment (MSA) is an important operation in Bioinformatics, used to simultaneously compare 3 or more sequences. The MSA problem was proven NP-Hard, so strategies have been proposed to reduce the search space and solve it in parallel. Recently, asymmetric multicore processors (AMPs) have become popular, with performance and energy-efficient cores, like the P-Cores and E-cores from Intel. However, parallel MSA applications have complex access patterns and adapting them for AMPs can be challenging. In this paper, we propose PA-Star21, an asymmetric-aware strategy based on A-Star, which computes optimal MSAs taking asymmetry into account when distributing the search space among threads. Our experimental results show that the proposed optimizations can reduce considerably the average execution time of PA-Star2 achieving a speedup of up to 7.70×. We also show that the asymmetric-aware strategy can reduce the average execution time for one of the hardest sequences set from the BAliBASE benchmark, when compared to the symmetric counterpart. Finally, we show that our approach is energy-efficient.11PA-Star2 is open source and the code is publicly available at1PA-Star2 is open source and the code is publicly available at https://github.com/danielsundfeld/astar_msa

Summary Structural alignments of Ribonucleic acid (RNA) sequences solved by the Sankoff algorithm are computationally expensive and often require constraints to be used in practice. Modern Graphics Processing Units (GPUs) contain more than 1000 cores, which compute in parallel to speed up applications. Here, we present a GPU‐based solution to the RNA structural alignment problem that makes use of precalculated base pair probabilities on the individual sequences. We designed and developed an unconstrained version of the Sankoff algorithm, obtaining the optimal result and calculating the entire four‐dimension dynamic programming matrix (4D DP). Our approach uses a two‐level wavefront strategy to exploit parallelism. The 4D DP matrix is divided in one external matrix (EM) and several internal matrices (IM). We applied wavefront strategies on the EM and IMs in a two‐level hierarchical way. At the first level, the wavefront is applied to the EM, calculating the cells that belong to the same diagonal in parallel. In the second level, since each cell in the EM is itself an IM matrix, the cells that belong to the same IM diagonal are calculated in parallel. The results obtained with real RNA sequences show that our GPU version is capable of outperforming a multicore CPU version of the unconstrained version of the Sankoff algorithm. Compared with the CPU‐based version running on 32 cores, our approach is able to achieve a speedup of 7.81x on the NVidia Tesla P100. In this case, the execution time was reduced from 6 hours and 18 minutes (32 cores) to 48 minutes and 20 seconds (GPU).

In this paper, we propose and evaluate CUDASankoff, a solution to the RNA structural alignment problem based on the Sankoff algorithm in Graphics Processing Units (GPUs). To our knowledge, this is the first time the Sankoff algorithm is implemented in GPU. In our solution, we show how to linearize the Sankoff 4-dimensional dynamic programming (4D DP) matrix and we propose a two-level wavefront approach to exploit the parallelism. The results were obtained with two different NVidia GPUs, comparing sets of real RNA sequences with lengths from 46 to 281 nucleotides. We show that our GPU approach is up to 24 times faster than a 16-core CPU solution in the 281 nucleotide Sankoff execution.

MOTIVATION: Structured RNAs can be hard to search for as they often are not well conserved in their primary structure and are local in their genomic or transcriptomic context. Thus, the need for tools which in particular can make local structural alignments of RNAs is only increasing. RESULTS: To meet the demand for both large-scale screens and hands on analysis through web servers, we present a new multithreaded version of Foldalign. We substantially improve execution time while maintaining all previous functionalities, including carrying out local structural alignments of sequences with low similarity. Furthermore, the improvements allow for comparing longer RNAs and increasing the sequence length. For example, lengths in the range 2000-6000 nucleotides improve execution up to a factor of five. AVAILABILITY AND IMPLEMENTATION: The Foldalign software and the web server are available at http://rth.dk/resources/foldalign CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose and evaluate a parallel solution for the exact Multiple Sequence Alignment problem based on the A-Star algorithm. In our parallel solution, we use a multi-index data structure, templates and a locality-sensitive hash function. The results were collected in two machines (4 cores and 32 cores), with real and synthetic sequence sets ranging from 3 to 14 sequences. We show that our parallel solution executes 2.89× and 4.77× faster than a state-of-the-art parallel MSA tool, with a proportional increase in memory usage, when comparing 2 hard instances of the benchmark Bali base reference set 1.