high performance computing on graphics processing units: hgpu.org

hgpu.org » Overview

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang

View

Download (PDF)

Tags: Computer science, FPGA, Heterogeneous systems, Linear Algebra, Machine learning, Overview, Sparse matrix

April 14, 2024 by hgpu

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Andreas Herten

View

Download (PDF)

Tags: Computer science, CUDA, Fortran, HIP, HPC, nVidia, Overview, programming model, SYCL

September 17, 2023 by hgpu

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva

View

Download (PDF)

Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Optimization, Overview, Security

August 7, 2022 by hgpu

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Nathan George

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Education, GPU cluster, HPC, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Quadro RTX 5000, Overview

May 24, 2020 by hgpu

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

J. Santamaría, M. L. Rivero-Cejudo, M. A. Martos-Fernández, F. Roca

View

Download (PDF)

Tags: Computer science, Computer vision, Evolutionary Computations, Image registration, Metaheuristics, Neural and Evolutionary Computing, Overview

May 4, 2020 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 2019 by hgpu

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

View

Download (PDF)

Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 by hgpu

A Survey on Agent-based Simulation using Hardware Accelerators

Jiajian Xiao, Philipp Andelfinger, David Eckhoff, Wentong Cai, Alois Knoll

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Overview

July 5, 2018 by hgpu

Acceleration of tensor-product operations for high-order finite element methods

Kasia Swirydowicz, Noel Chalmers, Ali Karakus, Timothy Warburton

View

Download (PDF)

Tags: Computer science, CUDA, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Overview, Performance, Tesla P100

November 7, 2017 by hgpu

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Danny C. Price, Jonathon Kocz, Matthew Bailes, Lincoln J. Greenhill

View

Download (PDF)

Tags: Astrophysics, CUDA, DSP, FPGA, nVidia, Overview, Signal processing

February 7, 2017 by hgpu

Deep Learning on FPGAs: Past, Present, and Future

Griffin Lacey, Graham W. Taylor, Shawki Areibi

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Machine learning, OpenCL, Overview

February 17, 2016 by hgpu

An Introduction to OpenCL C++

AJ Guillon

View

Download (PDF)

Tags: Computer science, OpenCL, Overview, Tutorial

May 22, 2015 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

Benchmarking Deep Learning Models on Jetson TX2

AI Benchmark: All About Deep Learning on Smartphones in 2019

A Survey on Agent-based Simulation using Hardware Accelerators

Acceleration of tensor-product operations for high-order finite element methods

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Deep Learning on FPGAs: Past, Present, and Future

An Introduction to OpenCL C++

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

Most viewed papers (last 30 days)