Search results for "Parallel computing"
showing 10 items of 189 documents
Parallel and Space-Efficient Construction of Burrows-Wheeler Transform and Suffix Array for Big Genome Data
2016
Next-generation sequencing technologies have led to the sequencing of more and more genomes, propelling related research into the era of big data. In this paper, we present ParaBWT, a parallelized Burrows-Wheeler transform (BWT) and suffix array construction algorithm for big genome data. In ParaBWT, we have investigated a progressive construction approach to constructing the BWT of single genome sequences in linear space complexity, but with a small constant factor. This approach has been further parallelized using multi-threading based on a master-slave coprocessing model. After gaining the BWT, the suffix array is constructed in a memory-efficient manner. The performance of ParaBWT has b…
Accelerating metagenomic read classification on CUDA-enabled GPUs.
2016
Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed. We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced k-mers (…
mD3DOCKxb: An Ultra-Scalable CPU-MIC Coordinated Virtual Screening Framework
2017
Molecular docking is an important method in computational drug discovery. In large-scale virtual screening, millions of small drug-like molecules (chemical compounds) are compared against a designated target protein (receptor). Depending on the utilized docking algorithm for screening, this can take several weeks on conventional HPC systems. However, for certain applications including large-scale screening tasks for newly emerging infectious diseases such high runtimes can be highly prohibitive. In this paper, we investigate how the massively parallel neo-heterogeneous architecture of Tianhe-2 Supercomputer consisting of thousands of nodes comprising CPUs and MIC coprocessors that can effic…
SWhybrid: A Hybrid-Parallel Framework for Large-Scale Protein Sequence Database Search
2017
Computer architectures continue to develop rapidly towards massively parallel and heterogeneous systems. Thus, easily extensible yet highly efficient parallelization approaches for a variety of platforms are urgently needed. In this paper, we present SWhybrid, a hybrid computing framework for large-scale biological sequence database search on heterogeneous computing environments with multi-core or many-core processing units (PUs) based on the Smith- Waterman (SW) algorithm. To incorporate a diverse set of PUs such as combinations of CPUs, GPUs and Xeon Phis, we abstract them as SIMD vector execution units with different number of lanes. We propose a machine model, associated with a unified …
Extending PluTo for Multiple Devices by Integrating OpenACC
2018
For many years now, processor vendors increased the performance of their devices by adding more cores and wider vectorization units to their CPUs instead of scaling up the processors' clock frequency. Moreover, GPUs became popular for solving problems with even more parallel compute power. To exploit the full potential of modern compute devices, specific codes are necessary which are often coded in a hardware-specific manner. Usually, the codes for CPUs are not usable for GPUs and vice versa. The programming API OpenACC tries to close this gap by enabling one code-base to be suitable and optimized for many devices. Nevertheless, OpenACC is rarely used by `standard programmers' and while dif…
FMapper: Scalable read mapper based on succinct hash index on SunWay TaihuLight
2022
Abstract One of the most important application in bioinformatics is read mapping. With the rapidly increasing number of reads produced by next-generation sequencing (NGS) technology, there is a need for fast and efficient high-throughput read mappers. In this paper, we present FMapper – a highly scalable read mapper on the TaihuLight supercomputer optimized for its fourth-generation ShenWei many-core architecture (SW26010). In order to fully exploit the computational power of the SW26010, we employ dynamic scheduling of tasks, asynchronous I/O and data transfers and implement a vectorized version of the banded Myers algorithm tailored to the 256 bit vector registers of the SW26010. Our perf…
An FPGA aligner for short read mapping
2012
The rapid growth of short read datasets poses a new challenge to the mapping of short reads to a reference genome in terms of sensitivity and execution speed. In this work, we present a parallel architecture for short read mapping utilizing field programmable gate array (FPGA)-based hardware. The computation intensive semi-global alignment and the hash table lookup operations are mapped onto an FPGA. The proposed Align Core is implemented with a parallel block structure to gain computational efficiency. We present a new parallel block-wise alignment structure to approximate the conventional dynamic programming algorithm. The performance of our FPGA aligner is compared to the GASSST and BWA …
Metabolomic Changes after Coffee Consumption: New Paths on the Block
2021
Scope Several studies suggest that regular coffee consumption may help preventing chronic diseases, but the impact of daily intake and the contribution of coffee metabolites in disease prevention are still unclear. The present study aimed at evaluating whether and how different patterns of coffee intake (one cup of espresso coffee/day, three cups of espresso coffee/day, one cup of espresso coffee/day and two cocoa-based products containing coffee two times per day) might impact endogenous molecular pathways. Methods and results A three-arm, randomized, cross-over trial was performed in 21 healthy volunteers who consumed each treatment for one month. Urine samples were collected to perform u…
CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing
2014
We present CUSHAW2-GPU to accelerate the CUSHAW2 algorithm using compute unified device architecture (CUDA)-enabled GPUs. Two critical GPU computing techniques, namely intertask hybrid CPU-GPU parallelism and tile-based Smith-Waterman map backtracking using CUDA, are investigated to facilitate fast alignments. By aligning both simulated and real reads to the human genome, our aligner yields comparable or better performance compared to BWA-SW, Bowtie2, and GEM. Furthermore, CUSHAW2-GPU with a Tesla K20c GPU achieves significant speedups over the multithreaded CUSHAW2, BWA-SW, Bowtie2, and GEM on the 12 cores of a high-end CPU for both single-end and paired-end alignment.
Big Data in metagenomics: Apache Spark vs MPI.
2020
The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when…