Search Results for "performance"

This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.

...HeatmapGenerator can also be used to make heatmaps in a variety of other non-medical fields. HeatmapGenerator is peer-reviewed published software (http://www.scfbm.org/content/9/1/30). Please cite: [Khomtchouk et al.: "HeatmapGenerator: High performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline." Source Code for Biology and Medicine, 2014 9:30] HeatmapGenerator source code is available at: https://github.com/Bohdan-Khomtchouk/HeatmapGenerator Bohdan Khomtchouk wishes to acknowledge the support of the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. ...

LightSpMV is a novel CUDA-compatible sparse matrix-vector multiplication (SpMv) algorithm using the standard compressed sparse row (CSR) storage format. We have evaluated LightSpMV using various sparse matrices and further compared it to the CSR-based SpMV subprograms in the state-of-the-art CUSP and cuSPARSE. Performance evaluation reveals that on a single Tesla K40c GPU, LightSpMV is superior to both CUSP and cuSPARSE, with a speedup of up to 2.60 and 2.63 over CUSP, and up to 1.93 and 1.79 over cuSPARSE for single and double precision, respectively.

...Perform evaluation on alignment quality reals that CUSHAW3 consistently outperforms CUSHAW2, BWA-MEM, Bowtie2 and GEM in terms of single-end and paired-end alignment. Furthermore, our aligner has demonstrated better paired-end alignment performance than Novalign3 for short-reads with high error rates.

The first parallel Smith-Waterman algorithm exploiting Intel Xeon Phi clusters to accelerate the alignment of long DNA sequences. This algorithm is written in C++ (with a set of SIMD intrinsic extensions), OpenMP and MPI. The performance evaluation revealed that our algorithm achieves very stable performance, and yields a performance of up to 30.1 GCUPS on a single Xeon Phi and up to 111.4 GCUPS on four Xeon Phis sharing a host.

The first parallel algorithm to accelerate the Smith-Waterman protein database search on shared-host multiple Intel Xeon Phi coprocessors. By searching against the UniProtKB/TrEMBL database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on a single Xeon Phi and up to 228.4 GCUPS on four Xeon Phis.

CUSHAW2 is a fast and parallel gapped read alignment to large genomes, such as the human genome. The performance evaluation, by aligning simulated and real datasets to the human genome, shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.

Musket is an efficient multistage k-mer based corrector for Illumina short read data. This corrector employs the k-mer spectrum approach and introduces three correction techniques in a multistage workflow. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing substitution-error-based correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared to all other evaluated correctors as well as a highly competitive overall execution time.

parFE is fully-parallel high-performance finite element code targeted to the modeling of trabecular bones in humans. It employs scalable multigrid solvers for efficient solutions on massively parallel computers.

Tandem mass spectral peptide identification and validation software, similar to X!Tandem, OMSSA, MyriMatch. Suitable for single hosts through large clusters. Written in Python for simplicity, with performance-critical sections in C++.