high performance computing on graphics processing units: hgpu.org

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Multi-GPU Load Balancing for In-Situ Simulation and Visualization

Yong Cao, Robert Hagan

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Tags: Computer science, nVidia, nVidia GeForce GTX 295, Rendering, Task scheduling, Visualization

December 27, 2013 by hgpu

BIDMach: Large-scale Learning with Zero Memory Allocation

John Canny, Huasha Zhao

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

December 25, 2013 by hgpu

Building Multiclass Nonlinear Classifiers with GPUs

Ignacio Arnaldo, Kalyan Veeramachaneni, Una-May O'Reilly

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 690

December 25, 2013 by hgpu

Efficiency analysis of a physical problem: Different parallel computational approaches for a dynamical integrator evolution

Adriana Gaudiani, Alejandro Soba, M. Florencia Carusela

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 560 Ti, Performance

December 25, 2013 by hgpu

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Tetsuya Odajima, Taisuke Boku, Mitsuhisa Sato, Toshihiro Hanawa, Yuetsu Kodama, Raymond Namyst, Samuel Thibault, Olivier Aumage

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Package, Performance, Task scheduling, Tesla M2090

December 25, 2013 by hgpu

A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction

Martin R. Oswald, Daniel Cremers

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Tags: Computer science, Computer vision, CUDA, nVidia, Tesla C2070

December 25, 2013 by hgpu

Transparent Checkpoint-Restart for Hardware-Accelerated 3D Graphics

Samaneh Kazemi, Rohan Garg, Gene Cooperman

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Tags: Computer science, nVidia, nVidia GeForce GT 650 M, OpenGL, Operating systems

December 24, 2013 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 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

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

Liming Xiao, Yao Zheng, Wenqi Tang, Guangchao Yao, Li Ruan

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480

December 23, 2013 by hgpu

Enabling High Performance Computing in Cloud Infrastructure using Virtualized GPUs

Andrew J. Younge, John Paul Walters, Steve Crago, Geoffrey C. Fox

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Tags: Cloud, Computer science, CUDA, nVidia, Tesla C2075, Tesla K20, Virtualization

December 23, 2013 by hgpu

Hardware Acceleration Technologies in Computer Algebra: Challenges and Impact

Sardar Anisul Haque

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Sorting, Sparse matrix, Tesla C2050, Tesla M2050, Thesis

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

Multi-GPU Load Balancing for In-Situ Simulation and Visualization

BIDMach: Large-scale Learning with Zero Memory Allocation

Building Multiclass Nonlinear Classifiers with GPUs

Efficiency analysis of a physical problem: Different parallel computational approaches for a dynamical integrator evolution

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction

Transparent Checkpoint-Restart for Hardware-Accelerated 3D Graphics

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Large-Scale Paralleled Sparse Principal Component Analysis

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

Hardware Acceleration Technologies in Computer Algebra: Challenges and Impact

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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