high performance computing on graphics processing units: hgpu.org

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A comparison of HPC-based quantum computing simulators using Quantum Volume

Lourens van Niekerk, Dhiraj Kumar, Aasish Kumar Sharma, Tino Meisel, Martin Leandro Paleico, Christian Boehme

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Tags: Benchmarking, CUDA, nVidia, nVidia A100, OpenCL, Overview, Physics, Quantum computing, Review

January 6, 2025 by hgpu

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

February 17, 2016 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

IterApp: Iterative Application Skeleton

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

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high performance computing on graphics processing units: hgpu.org

A comparison of HPC-based quantum computing simulators using Quantum Volume

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

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

IterApp: Iterative Application Skeleton

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

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