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Ensemble K-Means on Modern Many Core Hardware

Girish Ravunnikutty

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Tags: Bioinformatics, Clustering, Computer science, Data mining, Machine learning, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Pattern recognition, Thesis

July 28, 2012 by hgpu

Approximate Principal Direction Trees

Mark McCartin-Lim, Andrew McGregor, Rui Wang

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Tags: Computer science, Data Structures and Algorithms, Machine learning, nVidia, nVidia GeForce GTX 470

June 25, 2012 by hgpu

An Investigation into Concurrent Expectation Propagation

David Hall, Alex Kantchelian

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Tags: Algorithms, ATI, ATI Radeon HD 6490 M, Computer science, Machine learning, OpenCL, Physics

June 20, 2012 by hgpu

Genetic Algorithm Modeling with GPU Parallel Computing Technology

S. Cavuoti, M. Garofalo, M. Brescia, A. Pescape, G. Longo, G. Ventre

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Tags: Algorithms, Astrophysics, Computer science, CUDA, Data mining, Genetic programming, Machine learning, nVidia, nVidia GeForce GT 540 M

April 21, 2012 by hgpu

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

James Bergstra, Nicolas Pinto, David Cox

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Tags: Computer science, CUDA, Filtering, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Tesla C1060, Tesla C2070

March 29, 2012 by hgpu

GPU Computing for Machine Learning Algorithms

Mauro Garofalo

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Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GT 540 M, Thesis

February 28, 2012 by hgpu

CudaRF: A CUDA-based Implementation of Random Forests

Hakan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Machine learning, nVidia, nVidia GeForce GT 220

February 9, 2012 by hgpu

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

Hazel McKendrick

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Tags: CUDA, High Energy Physics - Experiment, Machine learning, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050, Thesis

January 23, 2012 by hgpu

Leveraging on High-Performance Computing and Cloud Technologies in Digital Libraries: A Case Study

Peter Wittek, Sandor Daranyi

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Tags: Algorithms, Cloud, Computer science, Machine learning, MapReduce, Text mining

January 20, 2012 by hgpu

Combining approximate inference methods for efficient learning on large computer clusters

Zhenwen Dai, Jacquelyn A. Shelton, Jorg Bornschein, Abdul Saboor Sheikh, Jorg Lucke

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Tags: Algorithms, Computational Complexity, Computer science, GPU cluster, Machine learning, MPI, nVidia, nVidia GeForce GTX 480, OpenCL, Python

January 19, 2012 by hgpu

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang Muller-Wittig

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Tags: Algorithms, Biology, Computational biology, CUDA, Image processing, Machine learning, nVidia, Package, Tesla C2050

January 13, 2012 by hgpu

Torch7: A Matlab-like Environment for Machine Learning

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 460

December 31, 2011 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

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high performance computing on graphics processing units: hgpu.org

Ensemble K-Means on Modern Many Core Hardware

Approximate Principal Direction Trees

An Investigation into Concurrent Expectation Propagation

Genetic Algorithm Modeling with GPU Parallel Computing Technology

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

GPU Computing for Machine Learning Algorithms

CudaRF: A CUDA-based Implementation of Random Forests

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

Combining approximate inference methods for efficient learning on large computer clusters

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Torch7: A Matlab-like Environment for Machine Learning

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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