high performance computing on graphics processing units: hgpu.org

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Progressive Photon Mapping on GPUs

Stian Aaraas Pedersen

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Overview, Package, Raytracing, Rendering, Thesis

October 18, 2013 by hgpu

OpenACC-based Snow Simulation

Magnus Alvestad Mikalsen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenACC, OpenCL, Performance, Tesla C2070, Tesla K20, Thesis

October 18, 2013 by hgpu

Heterogeneous FTDT for Seismic Processing

Andreas Berg Skomedal

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Tags: CUDA, Earth and Space Sciences, FDTD, Finite-difference time-domain, Geoscience, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package, Seismology, Tesla K20, Thesis

October 18, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

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Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

GPU-Specfic Kalman Filtering and Retrodiction for Large-Scale Target Tracking

Sean Tager

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Tags: Algorithms, Computer science, Filtering, Thesis

October 13, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

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Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

A Parallel Intermediate Representation for Embedded Languages

Ivar Lang

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Tags: Computer science, Data parallelism, nVidia, OpenCL, Package, Tesla M2050, Thesis

October 10, 2013 by hgpu

Scalable Fast Multipole Methods on Heterogeneous Architecture

Q. Hu

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Tesla C1060, Tesla C2050, Thesis

October 9, 2013 by hgpu

Enabling the use of Heterogeneous Computing for Bioinformatics

Ramakrishna Bijanapalli Chakri

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Tags: Bioinformatics, Biology, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, Smith-Waterman algorithm, Thesis

October 8, 2013 by hgpu

Parallel and Distributed Implementations of Multiple and Two-Dimensional Pattern Matching Algorithms

Charalampos S. Kouzinopoulos

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 280, String matching, Thesis

October 8, 2013 by hgpu

GPU Based Generation and Real-Time Rendering of Semi-Procedural Terrain Using Features

Simon Zaaijer

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Real-time graphics, Rendering, Thesis

October 5, 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

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high performance computing on graphics processing units: hgpu.org

Progressive Photon Mapping on GPUs

OpenACC-based Snow Simulation

Heterogeneous FTDT for Seismic Processing

Understanding and Modeling the Synchronization Cost in the GPU Architecture

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

GPU-Specfic Kalman Filtering and Retrodiction for Large-Scale Target Tracking

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

A Parallel Intermediate Representation for Embedded Languages

Scalable Fast Multipole Methods on Heterogeneous Architecture

Enabling the use of Heterogeneous Computing for Bioinformatics

Parallel and Distributed Implementations of Multiple and Two-Dimensional Pattern Matching Algorithms

GPU Based Generation and Real-Time Rendering of Semi-Procedural Terrain Using Features

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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