CUDA Papers

A collection of research papers and projects utilizing CUDA technology

Category Archives: Algorithms

Rapid Multipole Graph Drawing on the GPU

Leave a Comment Posted by on November 24, 2010

http://mgarland.org/files/papers/layoutgpu.pdf Abstract As graphics processors become powerful, ubiquitous and easier to program, they have also become more amenable to general purpose high-performance computing, including the computationally expensive task of drawing large graphs. This paper describes a new parallel analysis of the multipole method of graph drawing to support its efficient GPU implementation. We use a […]

Algorithms, Graphs , ,

Efficient Sparse Matrix-Vector Multiplication on CUDA

Leave a Comment Posted by on November 24, 2010

http://mgarland.org/files/papers/nvr-2008-004.pdf Abstract The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) […]

Algorithms, GPU Optimization, Linear Algebra , ,

Efficient Parallel Scan Algorithms for GPUs

Leave a Comment Posted by on November 24, 2010

http://mgarland.org/files/papers/nvr-2008-003.pdf Abstract Scan and segmented scan algorithms are crucial building blocks for a great many data-parallel algorithms. Segmented scan and related primitives also provide the necessary support for the flatten- ing transform, which allows for nested data-parallel programs to be compiled into flat data-parallel languages. In this paper, we describe the design of efficient scan […]

Algorithms, Data Structures, GPU Optimization, Linear Algebra, Sorting , ,

Accelerating Advanced MRI Reconstructions on GPUs

Leave a Comment Posted by on November 24, 2010

http://impact.crhc.illinois.edu/ftp/conference/cf08.stone.pdf Abstract Computational acceleration on graphics processing units(GPUs) can make advanced magnetic resonance imaging(MRI) reconstruction algorithms attractive in clinical settings,thereby improving the quality of MR images across abroad spectrum of applications. At present, MR imaging isoften limited by high noise levels, signi cant imaging artifacts,and/or long data acquisition (scan) times. Advancedimage reconstruction algorithms can mitigate these […]

Image Reconstruction, MRI , , , ,

Accelerating Iterative Field-Compensated MR Image Reconstruction on GPUs

Leave a Comment Posted by on November 24, 2010

http://impact.crhc.illinois.edu/ftp/conference/isbi2010.pdf Abstract We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue […]

Image Reconstruction, MRI , , ,

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Leave a Comment Posted by on November 24, 2010

http://impact.crhc.illinois.edu/ftp/conference/ismrm2010.pdf Abstract Many advanced MRI image acquisition and reconstruction methods see limited application due to high computational cost in MRI. For instance,iterative reconstruction algorithms (e.g. non-Cartesian k-space trajectory, or magnetic field inhomogeneity compensation) can improve image qualitybut suffer from low reconstruction speed. General-purpose computing on graphics processing units (GPU) have demonstrated significantperformance speedups and cost […]

Image Reconstruction, Medical, MRI, Multi-GPU

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architecture

Leave a Comment Posted by on November 24, 2010

http://impact.crhc.illinois.edu/ftp/conference/fgc2010.pdf Abstract Reduction is a common component of many applications,but can often be the limiting factor for parallelization.Previous reduction work has focused on detecting reductionidioms and parallelizing the reduction operationby minimizing data communications or exploiting moredata locality. While these techniques can be useful, theyare mostly limited to simple code structures. In this paper,we propose a […]

Algorithms, GPU Optimization, Reduction

Sparse regularization in MRI iterative reconstruction using GPUs

Leave a Comment Posted by on November 24, 2010

http://impact.crhc.illinois.edu/ftp/conference/xiaolong-2010.pdf Abstract Regularization is a common technique used toimprove image quality in inverse problems such as MR imagereconstruction. In this work, we extend our previous GraphicsProcessing Unit (GPU) implementation of MR imagereconstruction with compensation for susceptibility-induced fieldinhomogeneity effects by incorporating an additional quadraticregularization term. Regularization techniques commonly imposethe prior information that MR images are relatively […]

Image Reconstruction, Medical, MRI , ,

Benchmarking GPUs to Tune Dense Linear Algebra

Leave a Comment Posted by on November 23, 2010

http://portal.acm.org/ft_gateway.cfm?id=1413402&type=pdf&doid2=1413370.1413402 http://www2.computer.org/portal/c/document_library/get_file?folderId=97697&name=DLFE-3337.pdf Abstract We present performance results for dense linear algebra using the 8-series NVIDIA GPUs. Our GEMM routine runs 60% faster than the vendor implementation and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80-90% of the peak GEMM rate. Our parallel LU running on two GPUs […]

Algorithms, Linear Algebra , ,

High Performance Discrete Fourier Transforms on Graphics Processors

Leave a Comment Posted by on November 23, 2010

http://portal.acm.org/ft_gateway.cfm?id=1413373&type=pdf&doid2=1413370.1413373 http://www2.computer.org/portal/c/document_library/get_file?folderId=97697&name=DLFE-3346.pdf Abstract We present novel algorithms for computing Fourier transforms with high performance on GPUs. We present hierarchical, mixed radix FFT algorithms for both power-of-two and non-power-of-two sizes. Our hierarchical FFT algorithms efficiently exploit shared memory on GPUs using a Stockham formulation. We reduce the memory transpose overheads in hierarchical algorithms by combining the […]

FFT

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