Search Results for "cpu benchmark linux"

Important: Groimp migrates to Gitlab. You can find the latest code at "https://gitlab.com/grogra/groimp/". The version on Sourceforge will not be updated anymore. The modelling platform GroIMP is designed as an integrated platform which incorporates modelling, visualisation and interaction. It exhibits several features which makes itself suitable for the field of biological or ALife modelling: The “modelling backbone” consists in the language XL. It is fully integrated, e.g., the...

slimfastq is a cli application that compresses/decompresses fastq files. It features: * High compression ratio * Relatively low cpu/memory usage * Truly lossless compression/decompression

This project, initially developed at University College London, contains programs to perform rigid, affine and non-linear registration of nifti or analyse images. Two versions of the algorithms are included, a CPU- and a GPU- (using CUDA) based implementation.

Counts NGS read alignments against GTF annotations in a multithreaded and scalable fashion. Benchmark: 8 core 1M annotations for 2Gb sorted reads ~30 seconds compared to ~28 minutes for bedtools multicov. Files include: fast_count_multi - reports all counts and RPKM, multithreading support fast_count_deseq - reports gene counts in deseq compatible format, multithreading support fast_count - reports all counts with no multithreading support. usage ./fast_count_multi...

Drug name recognition and normalisation/grounding to DrugBank ids and standard names. Package provides 2 taggers: 1. DrugTagger - CRF-based with DrugBank presence feature (see feature set for details). 2. DrugnameGazetteer - gazetteer/dictionary-based. Dictionary created from DrugBank.ca database. Both taggers include grounding/normalisation to DrugBank ids and standard names. Feature set: Word, Word-1, Word+1, Word-1_Word, Word_Word+1, DrugBankPresence, POS DrugBankPresence...

Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic...

We introduce ARDEN (Artificial Reference Driven Estimation of false positives in NGS data), a novel benchmark that estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It allows the computation of error rates specifically for a dataset and the construction of a ROC-curve. Thereby, it can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation.

GeneNetWeaver (GNW) is an open-source tool for in silico benchmark generation and performance profiling of network inference methods. GNW was used to generate the community-wide DREAM3, DREAM4 and DREAM5 In Silico Challenges.

TRMSim-WSN (Trust and Reputation Models Simulator for Wireless Sensor Networks) is a Java-based simulator aimed to test Trust and Reputation models for WSNs. It provides several Trust and Reputation models and new ones can be easily added.

This is a multi-threaded Java program that tries to maximise CPU utilization by separating and distributing the computational part of the work from the file reading and writing part by using separate threads.