Category: Life Science
In this work, the Nussinov algorithm is analyzed but from the CUDA GPU programming perspective. The algorithm is radically redesigned in order to utilize the highly parallel NUMA architecture of the GPU.
Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN) provides a significant performance improvement for nearest neighbour computation in large-scale networks.
Using computational techniques, researchers have shown how a protein responsible for the maturation of the virus releases itself to initiate infection, This work has been carried out using GPUGRID.net, a voluntary distributed computing platform leveraging GPU accelerators to deliver “virtual supercomputing” performance.
Background Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to…
Computational biophysics research group of Professor Samuel Cho from Wake Forest University developed a novel parallel Verlet neighbor list algorithm for performing coarse-grained MD simulations of biologically relevant systems.
IPV is an interactive protein visualizer based on a ray-tracing engine. Targeting high quality images and ease of interaction, IPV uses the latest GPU computing acceleration techniques, combined with natural user interfaces such as Kinect and Wiimotes.
The simulations, run using graphical processors (GPUs), were used to investigate the effect of conformational change upon binding of the NA inhibitors oseltamivir and zanamivir in the wild-type and the IR and IRHY mutant strains
Authors present BINDSURF, a novel VS methodology that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases.
ICA is one of the de-facto standard methods for source separation and removal of noise and artifacts. In neuroscience, it has been widely used for EEG, fMRI and invasive electrophysiology. In all these neuroimaging methods, technology has increased the data volume, improving spatial and temporal resolution.
Our GPU software delivers haplotyping and imputation accuracies comparable to competing programs at a fraction of the computational cost and peak memory demand.