This paper proposes and evaluates CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA sequences in multi-GPU platforms, using the exact Smith-Waterman (SW) algorithm. In the first phase of CUDAlign 4.0, a huge Dynamic Programming (DP) matrix is computed by multiple GPUs, which asynchronously communicate border elements to the right neighbor in order to find the optimal score. After that, the traceback phase of SW is executed. The efficient parallelization of the traceback phase is very challenging because of the high amount of data dependency, which particularly impacts the performance and limits the application scalability. In order to obtain a multi-GPU highly parallel traceback phase, we propose and evaluate a new parallel traceback algorithm called Incremental Speculative Traceback (IST), which pipelines the traceback phase, speculating incrementally over the values calculated so far, producing results in advance. With CUDAlign 4.0, we were able to calculate SW matrices with up to 60 Peta cells, obtaining the optimal local alignments of all Human and Chimpanzee homologous chromosomes, whose sizes range from 26 Millions of Base Pairs (MBP) up to 249 MBP. As far as we know, this is the first time such comparison was made with the SW exact method. We also show that the IST algorithm is able to reduce the traceback time from 2.15¿ up to 21.03¿, when compared with the baseline traceback algorithm. The human¿chimpanzee chromosome 5 comparison (180 MBP¿183 MBP) attained 10,370.00 GCUPS (Billions of Cells Updated per Second) using 384 GPUs, with a speculation hit ratio of 98.2%.

Peer Reviewed