Biological Sequence alignment is a fundamental application in bioinformatics. It can be used to identify functionally conserved sequences and find evolutionary relationships between species. To compare entire genomes from different species, biologists increasingly need alignment methods that are efficient enough to handle long sequences, and accurate enough to correctly align the conserved biological features between distant species. Global alignments are important because they reveal the shared order of biological features in the compared species, and produce a more accurate alignment at the base-pair level when the features are in the same order. The best known global alignment algorithm is Needleman-Wunsch, later, BitPAl, a bit parallel algorithm for general, integer scoring global algorithm, provides a new implementation of Needleman-Wunsch algorithm (BitNW). Compared with original Needleman-Wunsch algorithm, BitNW is significantly faster by exploiting bit parallelism. A number of parallel strategies have been proposed to accelerate exact alignment methods. However, most of them failed to align long biological sequences due to quadratic time complexity. In this paper, we propose SLPal, a fast bit-parallel algorithm for accelerating long DNA sequence comparison on Intel manycore and multi-core architectures. In order to fully exploit the computing power of many cores and the 512-bit vector processing units (VPUs), we use a two-level parallelism scheme: coarsegrained thread level and fine-grained VPU level approaches. In thread level, the alignment scoring matrix will be split into small tiles and multiple threads will process these small tiles currently by using Intel TBB library. In the VPU level, the computing kernels are implemented using the Single Instruction Multiple Data (SIMD) instructions, thus, 16 independent integers reside in a 512-bit vector register can be processed simultaneously. The evaluation reveals that our algorithm achieves a stable performance for all benchmark data and yields a performance of up to 511.7 (617.2) GCUPS on a server with single Xeon Phi 7210 processor (dual Xeon Gold 614820-core processors). Furthermore, our test shows that SLPal can align two sequences with about 5 million bps in 50 seconds on our server equipped with dual Xeon Gold 6148 CPUs.

The Weather Research and Forecasting (WRF) Model is one of the widely-used mesoscale numerical weather prediction system and is designed for both atmospheric research and operational forecasting applications. However, it is an extremely time-consuming application: running a single simulation takes researchers days to weeks as the simulation size scales up and computing demands grow. In this paper, we port and optimize the whole WRF model to the Sunway TaihuLight supercomputer at a large scale. For the dynamic core in WRF, we present a domain-specific tool, namely, SWSLL, which is a directive-based compiler tool for the Sunway many-core architecture to convert the stencil computation into optimized parallel code. We also apply a decomposition strategy for SWSLL to improve the memory locality and decrease the number of off-chip memory accesses. For physical parameterizations, we explore the thread-level parallelization using OpenACC directives via reorganizations of data layouts and loops to achieve high performance. We present the algorithms and implementations and demonstrate the optimizations of a real-world complicated atmospheric modeling on the Sunway TaihuLight supercomputer. Evaluation results reveal that for the widely used benchmark with a horizontal resolution of 2.5 km, the speedup of 4.7 can be achieved by using the proposed algorithm and optimization strategies for the whole WRF model. In terms of strong scalability, our implementation scales well to hundreds of thousands of heterogeneous cores on Sunway TaihuLight.

MOTIVATION: Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. RESULTS: BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4. AVAILABILITY AND IMPLEMENTATION: BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modern high throughput sequencing platforms can produce large amounts of short read DNA data at low cost. Error correction is an important but time-consuming initial step when processing this data in order to improve the quality of downstream analyses. In this paper, we present a Scalable Parallel Error CorrecToR designed to improve the throughput of DNA error correction for Illumina reads on various parallel platforms. Our design is based on a k-spectrum approach where a Bloom filter is frequently probed as a key operation and is optimized towards AVX-512-based multi-core CPUs, Xeon Phi many-cores (both KNC and KNL), and heterogeneous compute clusters. A number of architecture-specific optimizations are employed to achieve high performance such as memory alignment, vectorized Bloom filter probing, and a stack-based iteration to eliminate recursion. Our experiments show that our optimizations result in speedups of up to 2.8, 5.2, and 9.3 on a CPU (Xeon W-2123), a KNC-based Xeon Phi (31S1P), and a KNL-based Xeon Phi (7210), respectively, compared to a multi-threaded CPU reference implementation for the error correction stage. Furthermore, when executed on the same hardware, SPECTR achieves a speedup of up to 1.7, 2.1, 2.4, and 6.4, compared to the state-of-the-art tools Lighter, BLESS2, RECKONER, and Musket, respectively. In addition, our MPI implementation exhibits an efficiency of around 86% when executed on 32 nodes of the Tianhe-2 supercomputer. SPECTR is available at https://github.com/Xu-Kai/SPECTR.

BACKGROUND: Various indexing techniques have been applied by next generation sequencing read mapping tools. The choice of a particular data structure is a trade-off between memory consumption, mapping throughput, and construction time. RESULTS: We present the succinct hash index - a novel data structure for read mapping which is a variant of the classical q-gram index with a particularly small memory footprint occupying between 3.5 and 5.3 GB for a human reference genome for typical parameter settings. The succinct hash index features two novel seed selection algorithms (group seeding and variable-length seeding) and an efficient parallel construction algorithm, which we have implemented to design the FEM (Fast(F) and Efficient(E) read Mapper(M)) mapper. FEM can return all read mappings within a given edit distance. Our experimental results show that FEM is scalable and outperforms other state-of-the-art all-mappers in terms of both speed and memory footprint. Compared to Masai, FEM is an order-of-magnitude faster using a single thread and two orders-of-magnitude faster when using multiple threads. Furthermore, we observe an up to 2.8-fold speedup compared to BitMapper and an order-of-magnitude speedup compared to BitMapper2 and Hobbes3. CONCLUSIONS: The presented succinct index is the first feasible implementation of the q-gram index functionality that occupies around 3.5 GB of memory for a whole human reference genome. FEM is freely available at https://github.com/haowenz/FEM .

The availability and amount of sequenced genomes have been rapidly growing in recent years because of the adoption of next-generation sequencing (NGS) technologies that enable high-throughput short-read generation at highly competitive cost. Since this trend is expected to continue in the foreseeable future, the design and implementation of efficient and scalable NGS bioinformatics algorithms are important to research and industrial applications. In this paper, we introduce S-Aligner–a highly scalable read mapper designed for the Sunway Taihu Light supercomputer and its fourth-generationShenWei many-core architecture (SW26010). S-Aligner employs a combination of optimization techniques to overcome both the memory-bound and the compute-bound bottlenecks in the read mapping algorithm. In order to make full use of the compute power of Sunway Taihu Light, our design employs three levels of parallelism: (1) internode parallelism using MPI based on a task-grid pattern, (2) intranode parallelism using multithreading and asynchronous data transfer to fully utilize all 260 cores of the SW26010 many-core processor, and (3) vectorization to exploit the available 256-bit SIMD vector registers. Moreover, we have employed asynchronous access patterns and data-sharing strategies during file I/O to overcome bandwidth limitations of the network file system. Our performance evaluation demonstrates that S-Aligner scales almost linearly with approximately 95% efficiency for up to 13,312 nodes (concurrently harnessing more than 3 millioncompute cores). Furthermore, our implementation on a single node outperforms the established RazerS3 mapper running on a platform with eight Intel Xeon E7-8860v3 CPUs while achieving highly competitive alignment accuracy.

The progress of next-generation sequencing has a major impact on medical and genomic research. This technology can now produce billions of short DNA fragments (reads) in a single run. One of the most demanding computational problems used by almost every sequencing pipeline is short-read alignment; i.e. determining where each fragment originated from in the original genome. Most current solutions are based on a seed-and-extend approach, where promising candidate regions (seeds) are first identified and subsequently extended in order to verify whether a full high-scoring alignment actually exists in the vicinity of each seed. Seed verification is the main bottleneck in many state-of-the-art aligners and thus finding fast solutions is of high importance. We present a parallel un gapped-alignment-featured seed verification (PUNAS) algorithm, a fast filter for effectively removing the majority of false positive seeds, thus significantly accelerating the short-read alignment process. PUNAS is based on bit-parallelism and takes advantage of SIMD vector units of modern microprocessors. Our implementation employs a vectorize-and-scale approach supporting multi-core CPUs and many-core Knights Landing (KNL)-based Xeon Phi processors. Performance evaluation reveals that PUNAS is over three orders-of-magnitude faster than seed verification with the Smith-Waterman algorithm and around one order-of-magnitude faster than seed verification with the banded version of Myers bit-vector algorithm. Using a single thread it achieves a speedup of up to 7.3, 27.1, and 11.6 compared to the shifted Hamming distance filter on a SSE, AVX2, and AVX-512 based CPU/KNL, respectively. The speed of our framework further scales almost linearly with the number of cores. PUNAS is open-source software available at https://github.com/Xu-Kai/PUNASfilter.

BACKGROUND: Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. RESULTS: This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency. CONCLUSIONS: Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .