high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Lu Zheng, Ole J. Mengshoel

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Tags: Artificial intelligence, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 460

September 13, 2013 by hgpu

Simulation and modeling of physical broadcasts

Ahmed Ahmed

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Tags: Cellular automata, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 680, Thesis

September 13, 2013 by hgpu

Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics

C.P.Patidar, Meena Sharma

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GT 610

September 11, 2013 by hgpu

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

Dong Li, Seyong Lee, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, nVidia

September 11, 2013 by hgpu

D5.5.4 – Characterization of Redundancy and Definition of Work Reuse

Stefanos Kaxiras, Georgios Keramidas, Konstantinos Koukos, Iakovos Stamoulis

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650

September 11, 2013 by hgpu

Hardware-Oblivious Parallelism for In-Memory Column-Stores

Max Heimel, Michael Saecker, Holger Pirk, Stefan Manegold, Volker Markl

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Tags: Computer science, Databases, nVidia, nVidia GeForce GTX 460, OpenCL

September 11, 2013 by hgpu

Iterative and Predictive Ray-Traced Collision Detection for Multi-GPU Architectures

Francois Lehericey

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Tags: Algorithms, Collision detection, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 660, OpenCL, Raytracing, Thesis

September 11, 2013 by hgpu

GPU Implementations of Object Detection using HOG Features and Deformable Models

Manato Hirabayashi, Shinpei Kato, Masato Edahiro, Kazuya Takeda, Taiki Kawano, Seiichi Mita

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Tesla K20

September 11, 2013 by hgpu

A Mixed Hierarchical Algorithm for Nearest Neighbor Search

Carlo del Mundo, Mariam Umar

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Tags: Algorithms, Computer science, CUDA, Nearest neighbour, nVidia, Tesla C2075

September 11, 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

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

September 9, 2013 by hgpu

Generation of the Scrambled Halton Sequence Using Accelerators

Emanouil Atanassov, Mariya Durchova

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Tags: Algorithms, Cloud, Computer science, CUDA, nVidia, Tesla M2090

September 4, 2013 by hgpu

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

Mian Lu, Lei Zhang, Huynh Phung Huynh, Zhongliang Ong, Yun Liang, Bingsheng He, Rick Siow Mong Goh, Richard Huynh

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Tags: Benchmarking, Computer science, Intel Phi, MapReduce

September 4, 2013 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

Applications

Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Simulation and modeling of physical broadcasts

Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

D5.5.4 – Characterization of Redundancy and Definition of Work Reuse

Hardware-Oblivious Parallelism for In-Memory Column-Stores

Iterative and Predictive Ray-Traced Collision Detection for Multi-GPU Architectures

GPU Implementations of Object Detection using HOG Features and Deformable Models

A Mixed Hierarchical Algorithm for Nearest Neighbor Search

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

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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