high performance computing on graphics processing units: hgpu.org

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith

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Tags: ATI, ATI Radeon HD 7970, Biology, Neuroscience, nVidia, nVidia GeForce GTX 280, OpenCL, Package, PyOpenCL, Python

January 9, 2014 by hgpu

The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

E. Garcia, T. Hausotte

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Tags: Algorithms, Bayesian, CUDA, Filtering, nVidia, Package, Particle filtering, Physics, Probability, Tesla C2075

January 6, 2014 by hgpu

PySPH: A Python framework for SPH

Kunal Puri, Prabhu Ramachandran, Pankaj Pandey, Chandrashekhar Kaushik, Pushkar Godbole

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Tags: Fluid dynamics, OpenCL, Package, Python

January 6, 2014 by hgpu

Parallel Irradiance Caching on the GPU

Nathaniel L. Jones

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Tags: Computer science, CUDA, nVidia, nVidia Quadro K4000, OptiX, Package, Raytracing

January 5, 2014 by hgpu

Accelerating Simulation Codes through the GeMTC Framework

Digvijay Singh Gahlot, Scott Krieder, Ioan Raicu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance

January 3, 2014 by hgpu

Nemo: A parallelized Lagrangian particle-tracking model

Benjamin Jones

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Tags: CUDA, Earth and Space Sciences, Fortran, MPI, nVidia, nVidia GeForce GTX 650 M, Package

January 3, 2014 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

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high performance computing on graphics processing units: hgpu.org

VertexAPI2 – A Vertex-Program API for Large Graph Computations on the GPU

Adaptation of an acoustic propagation model to the parallel architecture of a graphics processor

Towards Portable Performance for Explicit Hydrodynamics Codes

k+-buffer: Fragment Synchronized k-buffer

A Framework for Productive, Efficient and Portable Parallel Computing

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Nengo: a Python tool for building large-scale functional brain models

The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

PySPH: A Python framework for SPH

Parallel Irradiance Caching on the GPU

Accelerating Simulation Codes through the GeMTC Framework

Nemo: A parallelized Lagrangian particle-tracking model

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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