high performance computing on graphics processing units: hgpu.org

Recent advancements in cryo-electron microscopy (cryoEM) have made it technically possible to determine the three-dimensional (3D) structures of macromolecular complexes at atomic resolution. However, processing the large amount of data needed for atomic resolution reconstructions requires either accessing to very expensive computer clusters or waiting for weeks of continuous computation in a personal computer (PC). […]

Maximum-likelihood (ML) estimation has very desirable properties for reconstructing 3D volumes from noisy cryo-EM images of single macromolecular particles. Current implementations of ML estimation make use of the Expectation-Maximization (EM) algorithm or its variants. However, the EM algorithm is notoriously computation-intensive, as it involves integrals over all orientations and positions for each particle image. We […]

Functional magnetic resonance imaging allows non-invasive measurements of brain dynamics and has already been used for neurofeedback experiments, which relies on real time data processing. The limited computational resources that are typically available for this have hindered the use of connectivity analysis in this context. A basic, but already computationally demanding analysis method of neural […]

In this article, we look at early, recent, and state-of-the-art methods for registration of medical images using a range of high-performance computing (HPC) architectures including symmetric multiprocessing (SMP), massively multiprocessing (MMP), and architectures with distributed memory (DM), and nonuniform memory access (NUMA). The article is designed to be self-sufficient. We will take the time to […]

A very efficient implementation of a lattice Boltzmann (LB) kernel in 3D on a graphical processing unit using the compute unified device architecture interface developed by nVIDIA is presented. By exploiting the explicit parallelism offered by the graphics hardware, we obtain an efficiency gain of up to two orders of magnitude with respect to the […]

In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous computing power by integrating multiple cores, each with wide vector units. There has been much work to exploit modern processor architectures for database primitives like scan, sort, join and aggregation. However, unlike other primitives, tree search presents significant challenges due to […]

Is CUDA the parallel programming model that application developers have been waiting for?

A large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our […]

Real-time graphics applications require memory organizations featuring parallel pixel access and low-cost implementation. This work bases on a nonlinear skew mapping scheme and exploits the correlation between consecutive requests for pixels to design an efficient parallel memory organization. The mapping achieves parallel access, of mn pixels in various shapes, to the memory organized with mn […]

BACKGROUND:Many analyses of microarray association studies involve permutation and bootstrap resampling, and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. RESULTS:We have developed a CUDA based implementation, permGPU, that employs graphics processing […]

In this paper, we propose an acceleration of collapsed variational Bayesian (CVB) inference for latent Dirichlet allocation (LDA) by using Nvidia CUDA compatible devices. While LDA is an efficient Bayesian multi-topic document model, it requires complicated computations for parameter estimation in comparison with other simpler document models, e.g. probabilistic latent semantic indexing, etc. Therefore, we […]

PURPOSE: To accelerate the simultaneous algebraic reconstruction technique (SART) with motion compensation for speedy and quality computed tomography reconstruction by exploiting CUDA-enabled GPU. METHODS: Two core techniques are proposed to fit SART into the CUDA architecture: (1) a ray-driven projection along with hardware trilinear interpolation, and (2) a voxel-driven back-projection that can avoid redundant computation […]