GPGPU

In this paper we describe a successful effort to accelerate the two-point angular correlation function---a basic statistics tool used in the field of cosmology to characterize the distribution of the matter and energy in the Universe---by using an ...

The visualization of molecular orbitals (MOs) is important for analyzing the results of quantum chemistry simulations. The functions describing the MOs are computed on a three-dimensional lattice, and the resulting data can then be used for plotting ...

Modeling the interactions of biological molecules, or docking, is critical to both understanding basic life processes and to designing new drugs. Here we describe the GPU-based acceleration of a recently developed, complex, production docking code. We ...

Optical Quadrature Microscopy (OQM) is a process which uses phase data to capture information about the sample being studied. OQM is part of an imaging framework developed by the Optical Science Laboratory at Northeastern University. In one particular ...

This paper presents a performance analysis of an accelerated 2-D rigid image registration implementation that employs the Compute Unified Device Architecture (CUDA) programming environment to take advantage of the parallel processing capabilities of ...

This paper describes the use of CUDA to accelerate the Linpack benchmark on heterogenous clusters, where both CPUs and GPUs are used in synergy with minor or no modifications to the original source code. A host library intercepts the calls to DGEMM and ...

The Compute Unified Device Architecture (CUDA) has become a de facto standard for programming NVIDIA GPUs. However, CUDA places on the programmer the burden of packaging GPU code in separate functions, of explicitly managing data transfer between the ...

Optimizing program execution targeted for Graphics Processing Units (GPUs) can be very challenging. Our ability to efficiently map serial code to a GPU or stream processing platform is a time consuming task and is greatly hampered by a lack of detail ...

QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least ...

In this paper we describe a GPU parallelization of the 3D finite difference computation using CUDA. Data access redundancy is used as the metric to determine the optimal implementation for both the stencil-only computation, as well as the discretization ...

As computational power increases, tele-immersive applications are an emerging trend. These applications make extensive demands on computational resources through their heavy use of real-time 3D reconstruction algorithms. Since computer vision developers ...

Even though graphics processors (GPUs) are becoming increasingly popular for general purpose computing, current (and likely near future) generations of GPUs do not provide hardware support for detecting soft/hard errors in computation logic or memory ...