high performance computing on graphics processing units: hgpu.org

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CUDA-accelerated Hierarchical K-means

Che-Chun Lee, Kuan-Yu Chu

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Tags: Clustering, CUDA, Image processing, nVidia, Tesla C1060

July 1, 2012 by hgpu

Fast Universal Background Model (UBM) Training on GPUs using Compute Unified Device Architecture (CUDA)

M. Azhari, C. Ergun

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 570

April 21, 2012 by hgpu

Multicore Processing for Classification and Clustering Algorithms

V. Vaitheeshwaran, Kapil Kumar Nagwanshi, T. V. Rao

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Clustering, Computer science, CUDA, Data mining, nVidia, nVidia GeForce 8800 GTX, OpenCL

April 21, 2012 by hgpu

Maximize Performance on GPUs Using the Rake-based Optimization: A Case Study

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL

April 18, 2012 by hgpu

Multicore Processing for Clustering Algorithms

RekhanshRao, Kapil Kumar Nagwanshi, SipiDubey

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Clustering, Computer science, Data mining, nVidia, OpenCL

March 29, 2012 by hgpu

GPU-based parallel collision detection for fast motion planning

Jia Pan, Dinesh Manocha

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Tags: Clustering, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

March 22, 2012 by hgpu

Iterative Statistical Kernels on Contemporary GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Cloud, Clustering, Computer science, nVidia, OpenCL, Sparse matrix, Tesla C1060, Tesla C2070, Tesla M2050

March 15, 2012 by hgpu

Reflective Shadow Map Clustering for Real-Time Global Illumination

Roman Prutkin, Anton Kaplanyan, Carsten Dachsbacher

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Tags: 3D Graphics and Realism, Clustering, Computer science, DirectX, Image generation, nVidia, nVidia GeForce GTX 560 Ti

February 15, 2012 by hgpu

Graph Coarsening and Clustering on the GPU

B. O. Fagginger Auer, R. H. Bisseling

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Source codes

Tags: Algorithms, Clustering, Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

February 15, 2012 by hgpu

Ensemble K-means on multi-core architectures

Girish Ravunnikutty, Rejith George Joseph, Sanjay Ranka, Alin Dobra

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Tags: Algorithms, ATI, ATI FirePro V7800, Clustering, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL

February 10, 2012 by hgpu

The GPU Enhanced Parallel Computing for Large Scale Data Clustering

Xiaohui Cui, Jesse St. Charles, Justin Beaver, Thomas E. Potok

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

January 24, 2012 by hgpu

GPGPU and Multi-Core Architectures for Computing Clustering Coefficients of Irregular Graphs

A. Leist, K.A. Hawick, D.P. Playne

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Tags: Algorithms, Cell processor, Clustering, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480

January 24, 2012 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

CUDA-accelerated Hierarchical K-means

Fast Universal Background Model (UBM) Training on GPUs using Compute Unified Device Architecture (CUDA)

Multicore Processing for Classification and Clustering Algorithms

Maximize Performance on GPUs Using the Rake-based Optimization: A Case Study

Multicore Processing for Clustering Algorithms

GPU-based parallel collision detection for fast motion planning

Reflective Shadow Map Clustering for Real-Time Global Illumination

Graph Coarsening and Clustering on the GPU

Ensemble K-means on multi-core architectures

The GPU Enhanced Parallel Computing for Large Scale Data Clustering

GPGPU and Multi-Core Architectures for Computing Clustering Coefficients of Irregular Graphs

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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