high performance computing on graphics processing units: hgpu.org

In this paper we present the results of parallelizing two life sciences applications, Markov random fields-based (MRF) liver segmentation and HMMER’s Viterbi algorithm, using GPUs. We relate our experiences in porting both applications to the GPU as well as the techniques and optimizations that are most beneficial. The unique characteristics of both algorithms are demonstrated […]

Dense matrix inversion is a basic procedure in many linear algebra algorithms. A computationally arduous step in most dense matrix inversion methods is the inversion of triangular matrices as produced by factorization methods such as LU decomposition. In this paper, we demonstrate how triangular matrix inversion (TMI) can be accelerated considerably by using commercial Graphics […]

Mutual information (MI)-based image registration is effective in registering medical images, but it is computationally expensive. This paper accelerates MI-based image registration by dividing computation of mutual information into spatial transformation and histogram-based calculation, and performing 3D spatial transformation and trilinear interpolation on graphic processing unit (GPU). The 3D floating image is downloaded to GPU […]

We present the design and performance analysis of a GPU-optimized implementation of a disk encryption application employing the XTS mode of operation applied together with the Twofish algorithm within the well-known TrueCrypt suite. We show how to correctly tune the design parameters, including data allocation, thread packing, and parallelization strategy. Overall, our implementation of TrueCrypt […]

This paper presents the porting of 2D and 3D Navier-Stokes equations solvers for unstructured grids, from the CPU to the Graphics Processing Unit (GPU; NVIDIA’s Ge-Force GTX 280 and 285), using the CUDA language. The performance of the GPU implementations, with single, double or mixed precision arithmetic operations, is compared to that of the CPU […]

The availability of easily programmable manycore CPUs and GPUs has motivated investigations into how to best exploit their tremendous computational power for scientific computing. Here we demonstrate how a systems biology application – detection and tracking of white blood cells in video microscopy – can be accelerated by 200times using a CUDA-capable GPU. Because the […]

Given the rapid growth in computational requirements for medical image analysis, Graphics Processing Units (GPUs) have begun to be utilized to address these demands. But even though GPUs are well-suited to the underlying processing associated with medical image reconstruction, extracting the full benefits of moving to GPU platforms requires significant programming effort, and presents a […]

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability […]

The graphics processing unit (GPU) has emerged as a computational accelerator that dramatically reduces the time to discovery in high-end computing (HEC). However, while today’s state-of-the-art GPU can easily reduce the execution time of a parallel code by many orders of magnitude, it arguably comes at the expense of significant power and energy consumption. For […]

This paper introduces how to optimize a practical Prestack Kirchhoff Time Migration program by Compute Unified Device Architecture (CUDA) on General Purpose GPU (GPGPU). A few useful optimization methods on GPGPU are demostrated, such as how to increase the kernel thread numbers on GPU cores, and how to utilize the memory streams to overlap GPU […]

A molecular structural mechanics approach to carbon nanotubes on graphics processing units (GPUs) is reported. As a powerful parallel and relatively low cost processor, the GPU is used to accelerate the computations of the molecular structural mechanics approach. The data structures, matrix-vector multiplication algorithm, texture reduction algorithm, and ICCG method on the GPU are presented. […]

Graphics processing units (GPUs) can provide excellent speedups on some, but not all, general-purpose workloads. Using a set of computational GPU kernels as examples, the authors show how to adapt kernels to utilize the architectural features of a GeForce 8800 GPU and what finally limits the achievable performance.