high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming » CUDA

Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-design

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, BLAS, Computer science, CUDA, FPGA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2050

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

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

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

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

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Programming

Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-design

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

GPU-accelerated real-time stixel computation

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

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

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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