high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr

View

Tags: Bayesian, CUDA, Machine learning, Mathematics, nVidia, nVidia GeForce GTX Titan, Programming Languages, Statistics

December 13, 2013 by hgpu

Learning Random Forests on the GPU

Yisheng Liao, Alex Rubinsteyn, Russell Power, Jinyang Li

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX Titan, Package, Tesla C2075

December 11, 2013 by hgpu

Adaptive implementation selection in the SkePU skeleton programming library

Usman Dastgeer, Lu Li, Christoph Kessler

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Machine learning, nVidia, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

November 19, 2013 by hgpu

SAGE: Self-Tuning Approximation for Graphics Engines

Mehrzad Samadi, Janghaeng Lee, D. Anoushe Jamshidi, Amir Hormati, Scott Mahlke

View

Download (PDF)

Tags: Algorithms, Code generation, Compilers, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 560, Performance

November 14, 2013 by hgpu

Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Tiark Rompf, Martin Odersky, Kunle Olukotun

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla C2050

October 15, 2013 by hgpu

GPU-Framework for Teamwork Action Recognition

Mohamed Elhoseiny, Hossam Faheem, Taymour Nazmy, Eman Shaaban

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce 9500 GT, OpenCV

October 15, 2013 by hgpu

OpenCL Task Partitioning in the Presence of GPU Contention

Dominik Grewe, Zheng Wang, and Michael F.P. O'Boyle

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Intel, Intel HD Graphics 4000, Machine learning, OpenCL, Task scheduling

September 25, 2013 by hgpu

Fast k-NNG construction with GPU-based quick multi-select

Ivan Komarov, Ali Dashti, Roshan D'Souza

View

Download (PDF)

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

September 25, 2013 by hgpu

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs

Furong Huang, Niranjan U N, Mohammad Umar Hakeem, Prateek Verma, Animashree Anandkumar

View

Download (PDF)

Tags: Computer science, CUDA, CULA, Machine learning, nVidia, nVidia Quadro K4000, nVidia Quadro K5000

September 9, 2013 by hgpu

Accelerating Random Forests on CPUs and GPUs for Object-Class Image Segmentation

Benedikt Waldvogel

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, Package, Tesla K20, Thesis

August 16, 2013 by hgpu

Deep learning with COTS HPC systems

Adam Coates, Brody Huval, Tao Wang, David J. Wu, Andrew Y. Ng, Bryan Catanzaro

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 680

June 19, 2013 by hgpu

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

Klaus Kofler, Ivan Grasso, Biagio Cosenza, Thomas Fahringer

View

Download (PDF)

Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Task scheduling

April 21, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

Learning Random Forests on the GPU

Adaptive implementation selection in the SkePU skeleton programming library

SAGE: Self-Tuning Approximation for Graphics Engines

Domain-Specific Languages for Heterogeneous Parallel Computing

GPU-Framework for Teamwork Action Recognition

OpenCL Task Partitioning in the Presence of GPU Contention

Fast k-NNG construction with GPU-based quick multi-select

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs

Accelerating Random Forests on CPUs and GPUs for Object-Class Image Segmentation

Deep learning with COTS HPC systems

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

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

Most viewed papers (last 30 days)