BGI, the world’s largest genomics institute, has slashed the time to analyze batches of DNA sequencing data from nearly four days to just six hours using a NVIDIA Tesla GPU-based server farm. The speed up is considered a critically important step in determining, in an affordable manner, the chemical building blocks that make up a DNA molecule.
The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment.
In the paper, we present a numerical method for the simulation of brain tumors proliferation and we demonstrate the acceleration of this method in the context of a state of the art many-core GPU.
Computer tomography (CT) has wide application in medical imaging and reverse engineering. Due to the limited number of projections used in reconstructing the volume, the resulting 3D data is typically noisy. Contouring such data, for surface extraction, yields surfaces with localised artifacts of complex topology.
In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency.
Fast and efficient statistically based image reconstruction is highly demanded for state-of-art high-resolution PET scanners. The system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction.
Performance improvements for iterative electron tomography reconstruction using graphics processing units
Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude.
Our study employs graphics processing unit technology to accelerate the diffusion model simulation. We tailor, implement, analyze, and test several parallel ADI algorithms on the highly parallel computational and data architecture of the GPU.
A new hybrid imaging-treatment modality, the MRI-Linac, involves the irradiation of the patient in the presence of a strong magnetic field. This field acts on the charged particles, responsible for depositing dose, through the Lorentz force.
The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose.