Improved GPU Implementations of the Pair-HMM Forward Algorithm for DNA Sequence Alignment
Enliang Li, Subho S. Banerjee, Sitao Huang, Ravishankar K. Iyer, Deming Chen
With the rise of Next-Generation Sequencing (NGS) technology, clinical sequencing services become more accessible but are also facing new challenges. The surging demand motivates developments of more efficient algorithms for computational genomics and their hardware acceleration. In this work, we use GPU to accelerate the DNA variant calling and its related alignment problem. The Pair-Hidden Markov Model (Pair-HMM) is one of the most popular and compute-intensive models used in variant calling. As a critical part of the Pair-HMM, the forward algorithm is not only a computational but data-intensive algorithm. Multiple previous works have been done in efforts to accelerate the computation of the forward algorithm by the massive parallelization of the workload. In this paper, we bring advanced GPU implementations with various optimizations, such as efficient host-device communication, task parallelization, pipelining, and memory management, to tackle this challenging task. Our design has shown a speedup of 783x comparing to the Java baseline on Intel single-core CPU, 31.88x to the C++ baseline on IBM Power8 multicore CPU, and 1.53x - 2.21x to the previous state-of-the-art GPU implementations over various genomics datasets.