high performance computing on graphics processing units: hgpu.org

hgpu.org » Clustering

Behavioral graph fraud detection in E-commerce

Hang Yin, Zitao Zhang, Zhurong Wang, Yilmazcan Ozyurt, Weiming Liang, Wenyu Dong, Yang Zhao, Yinan Shan

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Tags: Clustering, Finance, Machine learning, Neural networks, Security

October 23, 2022 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal

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Tags: Algorithms, Clustering, Deep learning, Finance, nVidia, Risk Management, Tesla M40

January 17, 2021 by hgpu

Performance portability through machine learning guided kernel selection in SYCL libraries

John Lawson

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Tags: AMD R9 Nano, ATI, Auto-Tuning, Benchmarking, Clustering, Computer science, Machine learning, Package, Performance, performance portability, SYCL

September 6, 2020 by hgpu

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Matheus Souza, Lucas Maciel, Pedro Henrique Penna, Henrique Freitas

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Tags: Clustering, Computer science, Energy-efficient computing, FPGA, Intel Xeon Phi, Machine learning, OpenCL

March 10, 2019 by hgpu

Heterogeneous Distributed Big Data Clustering on Sparse Grids

David Pfander, Gregor Daiss, Dirk Pfluger

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Tags: Clustering, Computer science, Data mining, Distributed computing, Heterogeneous systems, Machine learning, MPI, nVidia, OpenCL, Package, performance portability, Tesla P100

February 10, 2019 by hgpu

Efficient and Scalable k-Means on GPUs

Clemens Lutz, Sebastian Bress, Tilmann Rabl, Steffen Zeuch, Volker Markl

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

September 9, 2018 by hgpu

Parallel Matching and Clustering Algorithms on GPUs

Md. Naim

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K40, Thesis

October 21, 2017 by hgpu

Progressive Clustering of Big Data with GPU Acceleration and Visualization

Jun Wang, Eric Papenhausen, Bing Wang, Sungsoo Ha, Alla Zelenyuk, Klaus Mueller

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Tags: Algorithms, big data, Clustering, Computer science, CUDA, nVidia, Tesla K20, Visualization

September 9, 2017 by hgpu

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Ramin Javadi, Saleh Ashkboos

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M

February 18, 2017 by hgpu

Clustering Throughput Optimization on the GPU

Michael Gowanlock, Cody M. Rude, David M. Blair, Justin D. Li, Victor Pankratius

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Tags: Algorithms, Clustering, Computer science, CUDA, Databases, nVidia, OpenMP, Tesla K20

February 5, 2017 by hgpu

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

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Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 by hgpu

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FDAS: Fourier Domain Acceleration Search, FPGA-accelerated with OpenCL

Pulsar search acceleration using FPGAs and OpenCL templates

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Reeber: a library for shared- and distributed-memory parallel computation of merge trees

Fast Merge Tree Computation via SYCL

pocl: Portable Computing Language

Efficient OpenCL system integration of non-blocking FPGA accelerators

OneFlow: a deep learning framework designed to be user-friendly, scalable and efficient

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

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high performance computing on graphics processing units: hgpu.org

Behavioral graph fraud detection in E-commerce

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

Performance portability through machine learning guided kernel selection in SYCL libraries

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Efficient and Scalable k-Means on GPUs

Parallel Matching and Clustering Algorithms on GPUs

Progressive Clustering of Big Data with GPU Acceleration and Visualization

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Clustering Throughput Optimization on the GPU

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Recent source codes

FDAS: Fourier Domain Acceleration Search, FPGA-accelerated with OpenCL

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Reeber: a library for shared- and distributed-memory parallel computation of merge trees

pocl: Portable Computing Language

OneFlow: a deep learning framework designed to be user-friendly, scalable and efficient

Python Suite for Advanced General Ensemble Simulations

GPU load-balancing library for regular and irregular computations

Shine compiler for the RISE language

Cramming the training of a (BERT-type) language model into limited compute

oneDNN: oneAPI Deep Neural Network Library

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