GPU-BLAST: using graphics processors to accelerate protein sequence alignment

Abstract

Motivation: The Basic Local Alignment Search Tool (BLAST) is one of the most widely used bioinformatics tools. The widespread impact of BLAST is reflected in over 53 000 citations that this software has received in the past two decades, and the use of the word ‘blast’ as a verb referring to biological sequence comparison. Any improvement in the execution speed of BLAST would be of great importance in the practice of bioinformatics, and facilitate coping with ever increasing sizes of biomolecular databases.Results: Using a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BLAST. The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4.Availability: The source code of GPU-BLAST is freely available at http://archimedes.cheme.cmu.edu/biosoftware.html.Contact: sahinidis@cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

“…have implemented REST ful API web services or SOAP to share or integrate data in the form of FTP, HTML, XML, JSON, plain text, or AWK commands [29].Moreover, cloud computing services were offered to handle, analyze, or interpret big datasets through various remote applications/servers. There are many cloud servers such as Cloud BLAST [30], Myrna [31], Cloud Burst [32], Hadoop-BAM [33], GPU-BLAST [34], Hydra [35], Peak Ranger [36],Crossbow [37], etc. were available over cloud for analyzing different types of big datasets [38][39][40][41].…”

Remote GPU Virtualization: Is It Useful?

“…The workloads are composed of the following applications (see Table I): GPU-BLAST [27], LAMMPS [2], mCUDA-MEME [16], GRO-MACS [22], BarraCUDA [14], MUMmerGPU [8], GPU-LIBSVM [3], and NAMD [20]. They have been selected from the list of NVIDIA's Popular GPU-Accelerated Applications Catalog [13] because of their different characteristics.…”

“…Finally, virtual machines were configured to have 4 cores and 12 GB of RAM memory. The applications used in this analysis are LAMMPS [2], CUDA-MEME [16], CUDASW++ [9], and GPU-BLAST [27], being all of them listed in the NVIDIA GPU-Accelerated Applications Catalog [13]. Figure 7 shows the performance of these four applications when executed in the following scenarios:…”

Optimizing Similarity Computations for Ontology Matching - Experiences from GOMMA

“…Thus, after this preprocessing, a concept has a set of sorted token vectors (stvs) representing the n-grams of their string attribute values as integers. For example, the trigrams of c 2 are represented as { [9,10], [11,12,13,14,15,16,17]}.…”

“…The availability of frameworks like CUDA and OpenCL further stimulated the interest in general purpose computation on GPUs [15]. Algorithms like BLAST [17], database joins [8] or duplicate detection/link discovery systems [4,12] have already been adapted for GPU execution. Unfortunately, GPUs and their programming languages like OpenCL have several limitations.…”