high performance computing on graphics processing units: hgpu.org

Jorge Frances Monllor, Sergio Bleda Perez, Sergi Gallego Rico, Cristian Neipp Lopez, Andres Marquez Ruiz, Inmaculada Pascual Villalobos, Augusto Belendez Vazquez

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Tags: CUDA, Electrodynamics, Electrostatic potential, Electrostatics, FDTD, FEM, Finite element method, Finite-difference time-domain, nVidia, nVidia GeForce GTX 470

December 13, 2011 by hgpu

RaVioli: a GPU Supported High-Level Pseudo Real-time Video Processing Library

Katsuhiko Kondo, Takafumi Inaba, Hiroko Sakurai, Masaomi Ohno, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 280

December 13, 2011 by hgpu

Parallel Implementations of Beamforming Design and Filtering for Microphone Array Applications

Jorge Lorente, Gema Pinero, Antonio M. Vidal, Jose Antonio Belloch, Alberto Gonzalez

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Tags: Algorithms, CUDA, Filtering, nVidia, nVidia GeForce GTX 285, Signal processing

December 13, 2011 by hgpu

Collision-Driven Volumetric Deformation on the GPU

G. Aldrich, D. Pinskiy, B. Hamann

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460

December 13, 2011 by hgpu

Lattice Boltzmann Simulation of Binary Mixture Diffusion Using Modern Graphics Processors

Mohammad Amin Safi, Mahmud Ashrafizaadeh, Amir Ali Ashrafizaadeh

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, Optimization, Tesla C1060

December 13, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Power consumption of mixed precision in the iterative solution of sparse linear systems

Rethinking Runtime Verification on Hundreds of Cores: Challenges and Opportunities

Fast Neural Network Training on General Purpose Computers

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Accelerating Live Graph-Cut-Based Object Tracking Using CUDA

Voxelized Minkowski sum computation on the GPU with robust culling

Water Surface Animation using Damped Wave Equation and CUDA Acceleration

Development of an unified FDTD-FEM library for electromagnetic analysis with CPU and GPU computing

RaVioli: a GPU Supported High-Level Pseudo Real-time Video Processing Library

Parallel Implementations of Beamforming Design and Filtering for Microphone Array Applications

Collision-Driven Volumetric Deformation on the GPU

Lattice Boltzmann Simulation of Binary Mixture Diffusion Using Modern Graphics Processors

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

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