high performance computing on graphics processing units: hgpu.org

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High-throughput Execution of Hierarchical Analysis Pipelines on Hybrid Cluster Platforms

George Teodoro, Tony Pan, Tahsin M. Kurc, Jun Kong, Lee A. D. Cooper, Joel H. Saltz

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Tags: Computer science, CUDA, nVidia, Optimization, Prefetch, Task scheduling, Tesla M2090

September 18, 2012 by hgpu

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

George Teodoro, Tony Pan, Tahsin Kurc, Jun Kong, Lee Cooper, Joel Saltz

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Tags: Algorithms, Computer science, CUDA, Distance transform, Heterogeneous systems, Image processing, nVidia, Tesla M2090

September 18, 2012 by hgpu

A Simulation Framework for Scheduling Performance Evaluation on CPU-GPU Heterogeneous System

Flavio Vella, Igor Neri, Osvaldo Gervasi, Sergio Tasso

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Operating systems

September 17, 2012 by hgpu

A GPU operations framework for WattDB

Vitor Uwe Reus

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GT 430, Thesis

September 17, 2012 by hgpu

Parallel hybrid SAT solving using OpenCL

Sander Beckers, Gorik De Samblanx, Floris De Smedt, Toon Goedeme, Lars Struyf, Joost Vennekens

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Tags: Algorithms, Computer science, nVidia, OpenCL, SAT, Tesla C2075

September 17, 2012 by hgpu

Parallel Benefit on Different Programming Paradigms

Chau-Yi Chou, Sheng-Hsiu Kuo, Chih-Wei Hsieh, Tsung-Che Tsai, Hsi-Ya Chang

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GT 200, OpenMP, Performance

September 16, 2012 by hgpu

Real-time Kd-tree Based Importance Sampling of Environment Maps

Serkan Ergun, Murat Kurt, Aydin Ozturk

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, KD-tree, Monte Carlo integration, nVidia, nVidia GeForce GTX 480

September 15, 2012 by hgpu

Reducing thread divergence in a GPU-accelerated branch-and-bound algorithm

Imen Chakroun, Mohand Mezmaz, Nouredine Melab, Ahcene Bendjoudi

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Tags: Algorithms, Computer science, Data parallelism, nVidia, Optimization, Tesla C2050

September 15, 2012 by hgpu

Efficient computation of condition estimates for linear least squares problems

Marc Baboulin, Serge Gratton, Remi Lacroix, Alan Laub

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Tesla C2075

September 15, 2012 by hgpu

Parallelize L-BFGS-B on the GPU

Yun Fei, Wenping Wang, Bin Wang

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Tags: Algorithms, Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GTX 580, Package, Tessellation, Voronoi diagram

September 14, 2012 by hgpu

An Optimized Parallel IDCT on Graphics Processing Units

Biao Wang, Mauricio Alvarez-Mesa, Chi Ching Chi, Ben Juurlink

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Tags: Computer science, H.264/AVC, nVidia, nVidia GeForce GT 430, nVidia GeForce GTS 450, nVidia GeForce GTX 560 Ti, OpenCL

September 14, 2012 by hgpu

Parallel Ray Tracing Simulations with MATLAB for Dynamic Lens Systems

Nicolai Wengert, Dan Negrut

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 540 M, nVidia GeForce GTX 480, Raytracing, Tesla C2050

September 14, 2012 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

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high performance computing on graphics processing units: hgpu.org

High-throughput Execution of Hierarchical Analysis Pipelines on Hybrid Cluster Platforms

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

A Simulation Framework for Scheduling Performance Evaluation on CPU-GPU Heterogeneous System

A GPU operations framework for WattDB

Parallel hybrid SAT solving using OpenCL

Parallel Benefit on Different Programming Paradigms

Real-time Kd-tree Based Importance Sampling of Environment Maps

Reducing thread divergence in a GPU-accelerated branch-and-bound algorithm

Efficient computation of condition estimates for linear least squares problems

Parallelize L-BFGS-B on the GPU

An Optimized Parallel IDCT on Graphics Processing Units

Parallel Ray Tracing Simulations with MATLAB for Dynamic Lens Systems

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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