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A GPU-based Framework for Real-time Free Viewpoint Television

Kyrylo Shegeda

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Tags: Computer science, CUDA, GLSL, nVidia, nVidia Quadro FX 1700, nVidia Quadro FX 5800, Rendering, Thesis

November 3, 2014 by hgpu

Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs

Wangdong Yang, Kenli Li, Zeyao Mo, Keqin Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Probability, Sparse matrix

November 3, 2014 by hgpu

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

Lai-Huei Wang

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Tags: Computer vision, CUDA, Design space exploration, DSP, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 680, Signal processing, Thesis

October 31, 2014 by hgpu

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Achal Shah, Angshul Majumdar

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Tags: Algorithms, CUDA, Machine learning, nVidia, nVidia GeForce GTX 680, Signal processing

October 29, 2014 by hgpu

Efficient Particle-Mesh Spreading on GPUs

Xiangyu Guo, Xing Liu, Peng Xu, Zhihui Du, Edmond Chow

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Tesla K40

October 29, 2014 by hgpu

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Lokendra Singh Panwar

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computational Complexity, Computer science, CUDA, Finite difference, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Seismology, Tesla C1060, Tesla C2050, Thesis

October 29, 2014 by hgpu

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Amirsaman Farrokhpanah, Hanif Montazeri, Javad Mostaghimi

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, Tesla K20

October 27, 2014 by hgpu

Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

Michelle Perry

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Tags: Bayesian, CUDA, High Energy Physics - Phenomenology, Neural networks, nVidia, nVidia GeForce GT 320 M, Physics, Tesla M2050

October 27, 2014 by hgpu

Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Source codes

Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

Shousheng Liu, Ge Chen, Chunyong Ma, Yong Han

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Tags: ATI, ATI Radeon HD 7750, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Rendering

October 25, 2014 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

A GPU-based Framework for Real-time Free Viewpoint Television

Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Efficient Particle-Mesh Spreading on GPUs

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Testing and Exposing Weak Graphics Processing Unit Memory Models

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

Contract-Based General-Purpose GPU Programming

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

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