high performance computing on graphics processing units: hgpu.org

Current many-core GPUs have enormous processing power, and unlocking this power for general-purpose computing is very attractive due to their low cost and efficient power utilization. However, the fine-grained parallelism and the stream-programming model supported by these GPUs require a paradigm shift, especially for algorithm designers. In this paper we present the design of a […]

To be realistic, an urban model must include appropriate numbers of pedestrians, vehicles, and other dynamic entities. Using a parallel computing architecture, researchers simulated a marathon with more than a million participants. To simulate participant behavior, they used fuzzy logic on a GPU to perform millions of inferences in real time.

In large vocabulary continuous speech recognition (LVCSR) the acoustic model computations often account for the largest processing overhead. Our weighted finite state transducer (WFST) based decoding engine can utilize a commodity graphics processing unit (GPU) to perform the acoustic computations to move this burden off the main processor. In this paper we describe our new […]

The tradeoffs of accuracy and performance are as yet an unsolved problem when dealing with Graphics Processing Units (GPUs) as a general-purpose computation device. Their high performance and low cost makes them a desirable target for scientific computation, and new language efforts help address the programming challenges of data parallel algorithms and memory management. But […]

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 […]