high performance computing on graphics processing units: hgpu.org

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GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

Yohei Miki, Masayuki Umemura

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX Titan X, Tesla K20, Tesla M2090

October 29, 2016 by hgpu

Efficient Random Sampling – Parallel, Vectorized, Cache-Efficient, and Online

Peter Sanders, Sebastian Lamm, Lorenz Hubschle-Schneider, Emanuel Schrade, Carsten Dachsbacher

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Mathematical Software, nVidia, nVidia GeForce GTX 980 Ti, Package

October 22, 2016 by hgpu

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

Vishist Sharma

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Sparse matrix, Tesla M2050, Thesis

October 22, 2016 by hgpu

CuMF_SGD: Fast and Scalable Matrix Factorization

Xiaolong Xie, Wei Tan, Liana L. Fong, Yun Liang

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Tags: Computer science, CUDA, Factorization, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package

October 22, 2016 by hgpu

OpenMP, OpenMP/MPI, and CUDA/MPI C programs for solving the time-dependent dipolar Gross-Pitaevskii equation

Vladimir Loncar, Luis E. Young-S., Srdjan Skrbic, Paulsamy Muruganandam, Sadhan K. Adhikari, Antun Balaz

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Tags: Condensed matter, CUDA, MPI, nVidia, OpenMP, Package, Physics, Tesla M2090

October 22, 2016 by hgpu

Efficient molecular dynamics simulations with many-body potentials on graphics processing units

Zheyong Fan, Wei Chen, Ville Vierimaa, Ari Harju

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Tags: Computational Physics, CUDA, Materials Science, Molecular dynamics, nVidia, Physics, Tesla K40

October 15, 2016 by hgpu

Reordering strategy for blocking optimization in sparse linear solvers

Gregoire Pichon, Mathieu Faverge, Pierre Ramet, Jean Roman

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse direct solvers, Tesla M2070

October 15, 2016 by hgpu

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

Daniel Hernandez-Juarez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, Antonio Manuel Lopez

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Package

October 15, 2016 by hgpu

GPU-accelerated real-time stixel computation

Daniel Hernandez-Juarez, Antonio Espinosa, David Vazquez, Antonio Manuel Lopez, Juan Carlos Moure

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Package

October 15, 2016 by hgpu

Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization

Adnan Agbaria, David Minor, Natan Peterfruend, Eyal Rozenberg, Ofer Rosenberg, Roman Talyansky

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 780 Ti

October 12, 2016 by hgpu

Understanding Latency Hiding on GPUs

Vasily Volkov

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

October 12, 2016 by hgpu

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu

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Tags: Algorithms, Computer science, CUDA, Information Retrieval, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X

October 12, 2016 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

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high performance computing on graphics processing units: hgpu.org

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

Efficient Random Sampling – Parallel, Vectorized, Cache-Efficient, and Online

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

CuMF_SGD: Fast and Scalable Matrix Factorization

OpenMP, OpenMP/MPI, and CUDA/MPI C programs for solving the time-dependent dipolar Gross-Pitaevskii equation

Efficient molecular dynamics simulations with many-body potentials on graphics processing units

Reordering strategy for blocking optimization in sparse linear solvers

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

GPU-accelerated real-time stixel computation

Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization

Understanding Latency Hiding on GPUs

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

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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