Accelerated Profile HMM Searches

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches

Implementing and Accelerating HMMER3 Protein Sequence Search on CUDA-Enabled GPU

The recent emergence of multi-core CPU and many-core GPU architectures has made parallel computing more accessible. Hundreds of industrial and research applications have been mapped onto GPUs to further utilize the extra computing resource. In bioinformatics, HMMER is a set of widely used applications for sequence analysis based on Hidden Markov Model. One of the tools in HMMER, hmmsearch, and the Smith-Waterman algorithm are two important tools for protein sequence analysis that use dynamic programming. Both tools are particularly well-suited for many-core GPU architecture due to the parallel nature of sequence database searches. After studying the existing research on CUDA acceleration in bioinformatics, this thesis investigated the acceleration of the key Multiple Segment Viterbi algorithm in HMMER version 3. A fully-featured CUDA-enabled protein database search tool cudaHmmsearch was designed, implemented and optimized. We demonstrated a variety of optimization strategies that are useful for general purpose GPU-based applications. Based on our optimization experience in parallel computing, six steps were summarized for optimizing performance using CUDA programming. We made comprehensive tests and analysis for multiple enhancements in our GPU kernels in order to demonstrate the effectiveness of selected approaches. The performance analysis showed that GPUs are able to deal with intensive computations, but are very sensitive to random accesses to the global memory. The results show that our implementation achieved 2.5x speedup over the single-threaded HMMER3 CPU SSE2 implementation on average

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms