high performance computing on graphics processing units: hgpu.org

Programming

Hybrid Multicore Algorithms for Some Semi-Numerical Applications and Graphs

Dip Sankar Banerjee

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Tags: Algorithms, Cell processor, Computer science, CUDA, Distributed computing, List ranking, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, OpenCL, Sorting, Thesis

January 19, 2015 by hgpu

SW#db: GPU-accelerated exact sequence similarity database search

Matija Korpar, Martin Sosic, Dino Blazeka, Mile Sikic

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Tags: Algorithms, Biology, CUDA, Databases, Next-Generation sequencing, nVidia, nVidia GeForce GTX 690, nVidia GeForce GTX 780, Package, Smith-Waterman algorithm

January 16, 2015 by hgpu

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

Amlan Chatterjee

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Sorting, Tesla K20, Thesis

January 16, 2015 by hgpu

GPU Processing for UAS-Based LFM-CW Stripmap SAR

Craig Stringham, David G. Long

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Tags: Algorithms, CUDA, Image processing, Motion compensation, nVidia, nVidia GeForce GTX 285, SAR

January 16, 2015 by hgpu

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

Mahmut Murat Gocmen

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Tags: Algorithms, CUBLAS, CUDA, FEM, Finite element method, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 280, Package, Tesla C2075, Thesis

January 16, 2015 by hgpu

A Novel Computational Model for GPUs with Applications to Efficient Algorithms

Atsushi Koike, Kunihiko Sadakane

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Tags: Algorithms, Computer science, CUDA, nVidia, Sorting, Tesla C1060, Tesla K20

January 15, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla K40

January 15, 2015 by hgpu

High Performance GPU-based Fourier Volume Rendering

Marwan Abdellah, Ayman Eldeib, Amr Sharawi

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, nVidia Quadro K5000, OpenGL, Rendering, Visualization

January 15, 2015 by hgpu

Simulating and Visualizing Real-Time Crowds on GPU Clusters

Benjamin Hernandez, Hugo Perez, Isaac Rudomin, Sergio Ruiz, Oriam DeGyves, Leonel Toledo

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Rendering, Tesla M2090, Visualization

January 8, 2015 by hgpu

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Jonathan Wong, Ellen Kuhl, Eric Darve

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

January 5, 2015 by hgpu

Hierarchical DAG Scheduling for Hybrid Distributed Systems

Wei Wu, Aurelien Bouteiller, George Bosilca, Mathieu Faverge, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40, Tesla M2090

January 5, 2015 by hgpu

GPU-based acceleration of free energy calculations in solid state physics

Michal Januszewski, Andrzej Ptok, Dawid Crivelli, Bartlomiej Gardas

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Tags: Algorithms, Computational Physics, CUDA, Fortran, nVidia, Physics, Tesla K40

January 2, 2015 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

Programming

SW#db: GPU-accelerated exact sequence similarity database search

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

GPU Processing for UAS-Based LFM-CW Stripmap SAR

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

A Novel Computational Model for GPUs with Applications to Efficient Algorithms

Batched Matrix Computations on Hardware Accelerators Based on GPUs

High Performance GPU-based Fourier Volume Rendering

Simulating and Visualizing Real-Time Crowds on GPU Clusters

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Hierarchical DAG Scheduling for Hybrid Distributed Systems

GPU-based acceleration of free energy calculations in solid state physics

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

Most viewed papers (last 30 days)