Biological sequence alignment is an important research topic in bioinformatics and continues to attract significant efforts. As biological data grow exponentially, however, most of alignment methods face challenges due to their huge computational costs. HMMER, a suite of bioinformatics tools, is widely used for the analysis of homologous protein and nucleotide sequences with high sensitivity, based on profile hidden Markov models (HMMs). Its latest version, HMMER3, introduces a heuristic pipeline to accelerate the alignment process, which is carried out on central processing units (CPUs) and highly optimized. Only a few acceleration results are reported on the basis of HMMER3. In this paper, we propose a five-tiered parallel framework, CUDAMPF++, to accelerate the most computationally intensive stages in HMMER3's pipeline, multiple/single segment Viterbi (MSV/SSV), on a single graphics processing unit (GPU) without any loss of accuracy. As an architecture-aware design, the proposed framework aims to fully utilize hardware resources via exploiting finer-grained parallelism (multi-sequence alignment) compared with its predecessor (CUDAMPF). In addition, we propose a novel method that proactively sacrifices L1 Cache Hit Ratio (CHR) to get improved performance and scalability in return. A comprehensive evaluation shows that the proposed framework outperforms all existing work and exhibits good consistency in performance regardless of the variation of query models or sequence datasets. For MSV (SSV) kernels, the peak performance of CUDAMPF++ is 283.9 (471.7) GCUPS on a single K40 GPU, and impressive speedups ranging from 1.8x (1.7×) to 168.3× (160.7×) are achieved over the CPU-based implementation (16 cores, 32 threads).

BACKGROUND: HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors. RESULTS: A Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance. CONCLUSIONS: CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biochemical pathways is a computationally intensive challenge. Traditional tools, such as ordinary differential equations, partial differential equations, stochastic master equations, and Gillespie type methods, are all limited either by their modeling fidelity or computational efficiency or both. In this work, we present a scalable computational framework based on modeling biochemical reactions in explicit 3D space, that is suitable for studying the behavior of large and complex biological pathways. The framework is designed to exploit parallelism and scalability offered by commodity massively parallel processors such as the graphics processing units (GPUs) and other parallel computing platforms. The reaction modeling in 3D space is aimed at enhancing the realism of the model compared to traditional modeling tools and framework. We introduce the Parallel Select algorithm that is key to breaking the sequential bottleneck limiting the performance of most other tools designed to study biochemical interactions. The algorithm is designed to be computationally tractable, handle hundreds of interacting chemical species and millions of independent agents by considering all-particle interactions within the system. We also present an implementation of the framework on the popular graphics processing units and apply it to the simulation study of JAK-STAT Signal Transduction Pathway. The computational framework will offer a deeper insight into various biological processes within the cell and help us observe key events as they unfold in space and time. This will advance the current state-of-the-art in simulation study of large scale biological systems and also enable the realistic simulation study of macro-biological cultures, where inter-cellular interactions are prevalent.