high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Accelerating MapReduce on a coupled CPU-GPU architecture

Linchuan Chen, Xin Huo, Gagan Agrawal

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Tags: APU, ATI, ATI Radeon HD 6550, Computer science, Data mining, Heterogeneous systems, Machine learning, MapReduce, OpenCL

November 20, 2012 by hgpu

Speeding up the evaluation of evolutionary learning systems using GPGPUs

Maria Franco, Natalio Krasnogor, Jaume Bacardit

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Tags: BioHEL, Evolutionary Computations, LCS, Learning Classifier System, Machine learning, Tesla C1060

November 1, 2012 by mariaux

Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations

Max Grossman, Sameer Kulkarni

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Tags: Algorithms, ATI, Computer science, Heterogeneous systems, Machine learning, OpenCL, Optimization

October 9, 2012 by hgpu

Optimising Unstructured Mesh Computational Fluid Dynamics Applications on Multicores via Machine Learning and Code Transformation

Roxana Rusitoru

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Tags: Algorithms, Fluid dynamics, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla M2050, Thesis

September 28, 2012 by hgpu

Autotuning Wavefront Abstractions for Heterogeneous Architectures

Siddharth Mohanty, Murray Cole

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL

September 21, 2012 by hgpu

Evolutionary Clustering on CUDA

Pavel Kromer, Jan Platos, Vaclav Snasel

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Tags: Algorithms, Clustering, Computer science, CUDA, Machine learning, nVidia, Tesla C2050

September 20, 2012 by hgpu

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Paola Arce, Cristian Maureira, Roberto Bonvallet, Cesar Fernandez

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Tags: Algorithms, CUBLAS, CUDA, Finance, Linear Algebra, Machine learning, Neural networks, nVidia, Tesla M2050

September 20, 2012 by hgpu

Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)

Tony Kam-Thong

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Tags: Biology, CUDA, Genetics, Machine learning, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, Package, Thesis

September 16, 2012 by hgpu

GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs

Ahmed Shamsul Arefin, Carlos Riveros, Regina Berretta, Pablo Moscato

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Tags: Bioinformatics, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Package, Pattern recognition, Tesla C2050

September 5, 2012 by hgpu

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

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high performance computing on graphics processing units: hgpu.org

Accelerating MapReduce on a coupled CPU-GPU architecture

Speeding up the evaluation of evolutionary learning systems using GPGPUs

Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations

Optimising Unstructured Mesh Computational Fluid Dynamics Applications on Multicores via Machine Learning and Code Transformation

Autotuning Wavefront Abstractions for Heterogeneous Architectures

Evolutionary Clustering on CUDA

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)

GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs

Ensemble K-Means on Modern Many Core Hardware

Approximate Principal Direction Trees

An Investigation into Concurrent Expectation Propagation

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

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