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Data Mining Using Graphics Processing Units

Christian Bohm, Robert Noll, Claudia Plant, Bianca Wackersreuther and Andrew Zherdin

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 280

March 5, 2011 by hgpu

Data Mining and Machine Learning in Astronomy

Nicholas M. Ball, Robert J. Brunner

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, Data mining, Instrumentation and Methods for Astrophysics, nVidia, Review

February 27, 2011 by hgpu

Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng and Naren Ramakrishnan

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Tags: Computer science, CUDA, Data mining, Neuroscience, nVidia, nVidia GeForce GTX 280, Programming techniques

January 2, 2011 by hgpu

Parallel hybrid evolutionary algorithms on GPU

The Van Luong, Nouredine Melab, El-Ghazali Talbi

Tags: Computer science, CUDA, Data mining, Metaheuristics, nVidia, nVidia GeForce GTX 280

December 23, 2010 by hgpu

Frequent itemset mining on graphics processors

Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, Qiong Luo

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, nVidia GeForce GTX 280

December 21, 2010 by hgpu

Graphics Hardware based Efficient and Scalable Fuzzy C-Means Clustering

S. A. Arul Shalom, Manoranjan Dash, Minh Tue

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 8800 GTX

December 19, 2010 by hgpu

Accelerating K-Means on the Graphics Processor via CUDA

Mario Zechner, Michael Granitzer

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce 9600 GT

December 18, 2010 by hgpu

Parallel SimRank computation on large graphs with iterative aggregation

Guoming He, Haijun Feng, Cuiping Li, Hong Chen

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Tags: Computer science, CUDA, Data mining, Graph theory, nVidia, nVidia GeForce 9600 GT

December 6, 2010 by hgpu

Density-based clustering using graphics processors

Christian Bohm, Robert Noll, Claudia Plant, Bianca Wackersreuther

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, nVidia GeForce GTX 280

November 21, 2010 by hgpu

Multi-dimensional characterization of temporal data mining on graphics processors

J. Archuleta, Yong Cao, T. Scogland, Wu-Chun Feng

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280

November 19, 2010 by hgpu

Accelerator-Oriented Algorithm Transformation for Temporal Data Mining

Debprakash Patnaik, Sean P. Ponce, Yong Cao, Naren Ramakrishnan

Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 280

November 13, 2010 by hgpu

Data-intensive document clustering on GPU clusters

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok

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Tags: Computer science, CUDA, Data mining, GPU cluster, nVidia

November 7, 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

Data Mining Using Graphics Processing Units

Data Mining and Machine Learning in Astronomy

Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Parallel hybrid evolutionary algorithms on GPU

Frequent itemset mining on graphics processors

Graphics Hardware based Efficient and Scalable Fuzzy C-Means Clustering

Accelerating K-Means on the Graphics Processor via CUDA

Parallel SimRank computation on large graphs with iterative aggregation

Density-based clustering using graphics processors

Multi-dimensional characterization of temporal data mining on graphics processors

Accelerator-Oriented Algorithm Transformation for Temporal Data Mining

Data-intensive document clustering on GPU clusters

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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