CUDASW++ statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

CUDASW++ specifications

Information


Unique identifier OMICS_29248
Name CUDASW++
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C
Parallelization CUDA
License Apache License version 2.0, GNU General Public License version 2.0
Computer skills Advanced
Version 3.1.1
Stability Beta
Maintained Yes

Download


sourceforge.png

Versioning


No version available

Maintainers


  • person_outline Bertil Schmidt
  • person_outline Yongchao Liu

Publications for CUDASW++

CUDASW++ citations

 (9)
library_books

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

2017
Comput Struct Biotechnol J
PMCID: 5581845
PMID: 28883909
DOI: 10.1016/j.csbj.2017.07.004

[…] ork removed query length limitations which is often required by mapping the problem set onto a texture. With the 8-bit video SIMD instruction introduced in the Kepler architecture, the 3.1 version of CUDASW++ achieves over 130 GCUPS on a single Nvidia Tesla K40c, which is at least 3 × faster than the 8-core CPUs without AVX2 support. More over, the CUDASW++ 3.1 could cooperate CPUs and GPUs to wor […]

library_books

Parallel algorithms for large scale biological sequence alignment on Xeon Phi based clusters

2016
BMC Bioinformatics
PMCID: 4959381
PMID: 27455061
DOI: 10.1186/s12859-016-1128-0

[…] 3.1 carries out parallel database searching by invoking the SWIPE [] program. It employs CUDA PTX SIMD video instructions to gain the data parallelism at the GPU side. The database size supported by CUDASW++ 3.1 is less than the memory size available on the GPU. Neither SWAPHI nor CUDASW++ 3.1 supports clusters.For single-node tests, we have used the N2 node (see Table ) as test platform. In our […]

library_books

Accelerating Smith Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU GPU Collaborative System

2015
Int J Genomics
PMCID: 4629039
PMID: 26568953
DOI: 10.1155/2015/761063

[…] anced by the GSW algorithm proposed by Striemer and Akoglu [] in 2009. Ligowski and Rudnicki [] also presented another SW algorithm for the protein database search on GPU in 2009. Liu et al. proposed CUDASW++1.0 [] and CUDASW++2.0 [] for protein database search in 2009 and 2010, respectively. In CUDASW++1.0, they defined the intertask parallelization (abbreviated to ITE) and intratask parallelizat […]

library_books

Improving the Mapping of Smith Waterman Sequence Database Searches onto CUDA Enabled GPUs

2015
Biomed Res Int
PMCID: 4538332
PMID: 26339591
DOI: 10.1155/2015/185179

[…] ge problems, now available in many PCs, laptops, workstations, and supercomputers. Because of the availability and the popularity, GPUs have been used to implement the Smith-Waterman algorithm, where CUDASW++ is the leading research that provides the fast, publicly available solution to the exact Smith-Waterman algorithm on commodity hardware [–]. CUDASW++ 3.0 is the latest version, which couples […]

library_books

An Improved Distance Matrix Computation Algorithm for Multicore Clusters

2014
Biomed Res Int
PMCID: 4074972
PMID: 25013779
DOI: 10.1155/2014/406178

[…] utations. It employs SSE-based vector execution units as accelerators and employs CUDA PTX SIMD video instructions to gain more data parallelism beyond the SIMT execution model. Evaluation shows that CUDASW++ 3.0 gains a performance improvement over CUDASW++ 2.0 up to 2.9 and 3.2, with a maximum performance of 119.0 and 185.6 GCUPS, on a single-GPU GeForce GTX 680 and a dual-GPU GeForce GTX 690 gr […]

library_books

Exploiting GPUs in Virtual Machine for BioCloud

2013
Biomed Res Int
PMCID: 3654629
PMID: 23710465
DOI: 10.1155/2013/939460

[…] chanism and focused on the sharing of a GPU among VMs, their overheads are not negligible. is the evaluation results of several biological applications. In this evaluation, we ran the barraCUDA [], CUDASW++ [], MUMmerGPU [], and CUDA-MEME []. The x-axis denotes the workloads and biological applications, and the y-axis is normalized execution time which includes disk I/O, CPU computation, and GPU […]

Citations

Looking to check out a full list of citations?

CUDASW++ institution(s)
Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany

CUDASW++ reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review CUDASW++