high performance computing on graphics processing units: hgpu.org

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Detecting Data Races on OpenCL Kernels with Symbolic Execution

Dino Distefano, Jeremy Dubreil

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Tags: Computer science, OpenCL, Programming Languages

August 15, 2013 by hgpu

The Yin and Yang of Processing Data Warehousing Queries on GPU Devices

Yuan Yuan, Rubao Lee, Xiaodong Zhang

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Tags: Analytical model, ATI, ATI Radeon HD 7970, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package

August 14, 2013 by hgpu

GPU Acceleration of a Basket Option Pricing Engine

Sean Trainor, Danny Crookes

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Tags: Algorithms, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 670, OpenCL

August 14, 2013 by hgpu

A Haptic Device Interface for Medical Simulations using OpenCL

Mattias Machwirth

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Tags: Algorithms, ATI, ATI Radeon HD 5750, ATI Radeon HD 7610 M, ATI Radeon HD 7970, Medicine, OpenCL, Thesis, Ultrasound, Visualization

August 14, 2013 by hgpu

Exploiting the Parallelism of Heterogeneous Systems using Dataflow Graphs on Top of OpenCL

Lars Schor, Andreas Tretter, Tobias Scherer, Lothar Thiele

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Tags: ATI, ATI Radeon HD 7750, Computer science, Graph theory, Heterogeneous systems, nVidia, nVidia GeForce GTX 670, nVidia Quadro K 2000 M, OpenCL, Task scheduling

August 13, 2013 by hgpu

Barrier Invariants: A Shared State Abstraction for the Analysis of Data-Dependent GPU Kernels

Nathan Chong, Alastair F. Donaldson, Paul H.J. Kelly, Jeroen Ketema, Shaz Qadeer

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Tags: Algorithms, Computer science, CUDA, OpenCL, Package, Performance

August 13, 2013 by hgpu

Executing Process Networks on Heterogeneous Platforms using OpenCL

Tobias Scherer

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Tags: ATI, ATI Radeon HD 7750, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 670, nVidia Quadro K 2000 M, OpenCL, Thesis

August 13, 2013 by hgpu

Molecular Dynamics Simulation of Multi-Scale Flows on GPUs

S. Mulla, S. Ivekovic, J.M. Reese

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Tags: Biology, Chemistry, CUDA, Molecular dynamics, nVidia, OpenCL, Tesla M2075

August 12, 2013 by hgpu

Analyzing Optimization Techniques for Power Efficiency on Heterogeneous Platforms

Yash Ukidave, David Kaeli

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Tags: Algorithms, ATI, ATI Radeon HD 7770, Computer science, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, Power-efficient computing

August 10, 2013 by hgpu

A GPU implementation of massively parallel direction splitting for the incompressible Navier-Stokes equations

Adam L. Preuss

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Tags: Algorithms, Fluid dynamics, Laplace and Poisson equation, nVidia, OpenCL, OpenGL, Poisson equation, Tesla M2075, Thesis

August 9, 2013 by hgpu

A GPGPU based program to solve the TDSE in intense laser fields through the finite difference approach

Cathal O Broin, L.A.A Nikolopoulos

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Computational Physics, Finite difference, OpenCL, Physics, Quantum Physics

August 9, 2013 by hgpu

Automatic Skeleton-Based Compilation through Integration with an Algorithm Classification

Cedric Nugteren, Pieter Custers, Henk Corporaal

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, OpenCL, OpenMP

August 7, 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

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high performance computing on graphics processing units: hgpu.org

Detecting Data Races on OpenCL Kernels with Symbolic Execution

The Yin and Yang of Processing Data Warehousing Queries on GPU Devices

GPU Acceleration of a Basket Option Pricing Engine

A Haptic Device Interface for Medical Simulations using OpenCL

Exploiting the Parallelism of Heterogeneous Systems using Dataflow Graphs on Top of OpenCL

Barrier Invariants: A Shared State Abstraction for the Analysis of Data-Dependent GPU Kernels

Executing Process Networks on Heterogeneous Platforms using OpenCL

Molecular Dynamics Simulation of Multi-Scale Flows on GPUs

Analyzing Optimization Techniques for Power Efficiency on Heterogeneous Platforms

A GPU implementation of massively parallel direction splitting for the incompressible Navier-Stokes equations

A GPGPU based program to solve the TDSE in intense laser fields through the finite difference approach

Automatic Skeleton-Based Compilation through Integration with an Algorithm Classification

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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