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Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Parallel Spectral Graph Partitioning on CUDA

Orhan Firat, Alptekin Temizel

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Tags: Algorithms, CUDA, Image processing, Machine learning, nVidia, Tesla C2070

February 23, 2014 by hgpu

Fast American Basket Option Pricing on a multi-GPU Cluster

Michael Benguigui, Francoise Baude

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Tags: Finance, GPU cluster, Machine learning, nVidia, OpenCL, Option pricing, Tesla M2050, Tesla M2075, Tesla S1070

February 17, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Implementing Neural Networks Efficiently

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla M2090

December 29, 2013 by hgpu

BIDMach: Large-scale Learning with Zero Memory Allocation

John Canny, Huasha Zhao

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Source codes

Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 690

December 25, 2013 by hgpu

Large-Scale Paralleled Sparse Principal Component Analysis

W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Numerical Analysis, nVidia, Tesla C2050

December 24, 2013 by hgpu

Single Server Multi-GPU Training of ConvNets

Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato

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Tags: Computer science, Computer vision, Machine learning, Neural networks

December 23, 2013 by hgpu

Fast Training of Convolutional Networks through FFTs

Michael Mathieu, Mikael Henaff, Yann LeCun

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Tags: Algorithms, Computer science, Computer vision, CUDA, FFT, Machine learning, nVidia

December 23, 2013 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Exploring the power of GPU’s for training Deep Belief Networks

Parallel Support Vector Machines in Practice

Parallel Spectral Graph Partitioning on CUDA

Fast American Basket Option Pricing on a multi-GPU Cluster

Implementing the Projected Spatial Rich Features on a GPU

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Implementing Neural Networks Efficiently

BIDMach: Large-scale Learning with Zero Memory Allocation

Large-Scale Paralleled Sparse Principal Component Analysis

Single Server Multi-GPU Training of ConvNets

Fast Training of Convolutional Networks through FFTs

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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