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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

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Daniel Junger, Christian Hundt, Jorge Gonzalez-Dominguez, Bertil Schmidt

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan X

September 3, 2016 by hgpu

OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms

Zhaoyi Meng, Alice Koniges, Yun (Helen) He, Samuel Williams, Thorsten Kurth, Brandon Cook, Jack Deslippe, Andrea L. Bertozzi

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Tags: Clustering, Computer science, Intel Xeon Phi, Machine learning, OpenMP

June 9, 2016 by hgpu

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

Face.evoLVe: A High-Performance Face Recognition Library

OpenCLDecompiler

A method for decompilation of AMD GCN kernels to OpenCL

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

StreamBlocks: A compiler for heterogeneous dataflow computing

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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2021

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Virtually in Barcelona, Spain

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24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

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13-15

Shenzhen, China

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Bangkok, Thailand

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020

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high performance computing on graphics processing units: hgpu.org

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

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms

Recent source codes

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

OpenCLDecompiler

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

StreamBlocks: A compiler for heterogeneous dataflow computing

Bempp-cl: a boundary element method library

futhark-stencil-benchmarks

POCL: Portable Computing Language

K-FAC: PyTorch Distributed K-FAC Preconditioner

pystencils

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Most viewed papers (last 30 days)

Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020