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An Evaluative Comparison of Performance Portability across GPU Programming Models

Joshua H. Davis, Pranav Sivaraman, Isaac Minn, Konstantinos Parasyris, Harshitha Menon, Giorgis Georgakoudis, Abhinav Bhatele

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Computer science, CUDA, Heterogeneous systems, HIP, MPI, nVidia, nVidia V100, OpenACC, OpenMP, Performance, performance portability, SYCL

February 18, 2024 by hgpu

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Anton Rydahl, Joseph Huber, Ethan Luis McDonough, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, Numerical Analysis, OpenMP, Performance, Tesla A100, Tesla V100

December 18, 2023 by hgpu

Compute units in OpenMP: Extensions for heterogeneous parallel programming

Marc Gonzàlez-Tallada, Enric Morancho

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Computer science, Heterogeneous systems, OpenMP, Task scheduling

August 28, 2023 by hgpu

ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels

Ali TehraniJamsaz, Alok Mishra, Akash Dutta, Abid M. Malik, Barbara Chapman, Ali Jannesari

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Tags: AMD Radeon Instinct Mi50, ATI, Computer science, HPC, Machine learning, Neural networks, nVidia, nVidia V100, OpenMP, Performance

April 16, 2023 by hgpu

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis de Supinski

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, OpenMP, performance portability, Tesla P100, Tesla V100

March 19, 2023 by hgpu

OpenMP Advisor

Alok Mishra, Abid M. Malik, Meifeng Lin, Barbara Chapman

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, OpenMP, Tesla A100, Tesla K80, Tesla V100

January 15, 2023 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

Keren Zhou, Laksono Adhianto, Jonathon Anderson, Aaron Cherian, Dejan Grubisic, Mark Krentel, Yumeng Liu, Xiaozhu Meng, John Mellor-Crummey

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Tags: AMD Radeon Instinct Mi50, ATI, Code generation, Computer science, HPC, nVidia, OpenCL, Performance, Tesla V100

September 19, 2021 by hgpu

Design, Implementation and Test of Efficient GPU to GPU Communication Methods

Stepan Vanecek

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Tags: AMD Radeon Instinct Mi50, ATI, Computer science, Heterogeneous systems, HIP, MPI, nVidia DGX-1, Thesis

January 3, 2021 by hgpu

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Cade Brown, Ahmad Abdelfattah, Stanimire Tomov, Jack Dongarra

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Tags: AMD, AMD Radeon Instinct MI25, AMD Radeon Instinct Mi50, Benchmarking, Computer science, Heterogeneous systems, HIP, HPC, Linear Algebra, Performance, Portability

August 23, 2020 by hgpu

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Szilárd Páll, Artem Zhmurov, Paul Bauer, Mark Abraham, Magnus Lundborg, Alan Gray, Berk Hess, Erik Lindahl

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Tags: Algorithms, AMD Radeon Instinct Mi50, AMD Vega FE, ATI, Chemistry, CUDA, Heterogeneous systems, Molecular dynamics, MPI, nVidia, nVidia GeForce RTX 2080, nVidia Quadro P 6000, OpenCL, Package, Tesla V100

June 21, 2020 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

An Evaluative Comparison of Performance Portability across GPU Programming Models

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Compute units in OpenMP: Extensions for heterogeneous parallel programming

ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

OpenMP Advisor

Benchmarking optimization algorithms for auto-tuning GPU kernels

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

Design, Implementation and Test of Efficient GPU to GPU Communication Methods

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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