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How GPUs Can Improve the Quality of Magnetic Resonance Imaging

Sam S. Stone, Haoran Yi, Justin P. Haldar, Wen-mei W. Hwu, Bradley P. Sutton, and Zhi-Pei Liang

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Tags: CUDA, FFT, Image processing, Image reconstruction, Magnetic resonance imaging, Medicine, MRI, nVidia, nVidia GeForce 8800 GTX

February 20, 2011 by hgpu

GPU-based ultra fast IMRT plan optimization

Chunhua Men, Xuejun Gu, Dongju Choi, Amitava Majumdar, Ziyi Zheng, Klaus Mueller, Steve B. Jiang

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Tags: CUDA, Image processing, Magnetic resonance imaging, Medicine, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla C1060, Tesla S1070

December 21, 2010 by hgpu

Cartesian SENSE and k-t SENSE reconstruction using commodity graphics hardware

Michael S. Hansen, David Atkinson, Thomas S. Sorensen

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Tags: Image processing, Image reconstruction, Magnetic resonance imaging, Medicine, MRI

December 12, 2010 by hgpu

An optimised radial basis function algorithm for fast non-rigid registration of medical images

R. J. Lapeer, S. K. Shah, R. S. Rowland

Tags: Computed tomography, CT, Image registration, Magnetic resonance imaging, Medicine, Tomography

November 19, 2010 by hgpu

Correlation analysis on GPU systems using NVIDIA’s CUDA

Daniel Gembris, Markus Neeb, Markus Gipp, Andreas Kugel, Reinhard Manner

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Tags: Computer science, CUDA, FPGA, Magnetic resonance imaging, Medicine, nVidia, nVidia GeForce 8800 GTX

November 17, 2010 by hgpu

Stochastic DT-MRI Connectivity Mapping on the GPU

Tim McGraw, Mariappan Nadar

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Tags: Computer science, Diffusion tensor, GLSL, Magnetic resonance imaging, nVidia, nVidia GeForce 8800 GTX, OpenGL, Stochastic tractography

October 28, 2010 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

How GPUs Can Improve the Quality of Magnetic Resonance Imaging

GPU-based ultra fast IMRT plan optimization

Cartesian SENSE and k-t SENSE reconstruction using commodity graphics hardware

An optimised radial basis function algorithm for fast non-rigid registration of medical images

Correlation analysis on GPU systems using NVIDIA’s CUDA

Stochastic DT-MRI Connectivity Mapping on the GPU

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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