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A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Multidimensional scaling, nVidia, nVidia GeForce GT 740 M, Pattern recognition

September 26, 2015 by hgpu

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Sungin Park, Soo-Yong Shin, Kyu-Baek Hwang

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Tags: Bioinformatics, Biology, CUDA, Genetics, Multidimensional scaling, nVidia, Package

June 21, 2013 by hgpu

Visualization of large multidimensional data sets by using multi-core CPU, GPU and MPI cluster

Piotr Pawliczek, Witold Dzwinel, David A. Yuen

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Tags: Algorithms, Computer science, CUDA, Data mining, MPI, Multidimensional scaling, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 8800 Ultra, nVidia GeForce 9500 GT, nVidia GeForce 9800 GT, nVidia GeForce GT 330 M, nVidia GeForce GTX 260, nVidia GeForce GTX 460, nVidia GeForce GTX 480, Tesla M2050, Visualization

October 6, 2012 by hgpu

Scattering Points in Parallel Coordinates

Xiaoru Yuan, Peihong Guo, He Xiao, Hong Zhou, Huamin Qu

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Tags: Computer science, CUDA, Multidimensional scaling, nVidia, Tesla C870, Visualization

November 28, 2010 by hgpu

Graphics Processing Units and High-Dimensional Optimization

Hua Zhou, Kenneth Lange, Marc A. Suchard

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Tags: Block relaxation, CUBLAS, CUDA, EM and MM algorithms, Mathematics, Multidimensional scaling, Nonnegative matrix factorization, nVidia, nVidia GeForce GTX 280, PET scanning, Statistics

October 30, 2010 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Visualization of large multidimensional data sets by using multi-core CPU, GPU and MPI cluster

Scattering Points in Parallel Coordinates

Graphics Processing Units and High-Dimensional Optimization

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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