high performance computing on graphics processing units: hgpu.org

hgpu.org » Portability

CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, Heterogeneous systems, HPC, Intel, Intel Ponte Vecchio Max 1100, nVidia, oneAPI, Portability, SYCL, Tesla V100

March 10, 2024 by hgpu

An approach to performance portability through generic programming

Andreas Hadjigeorgiou, Christodoulos Stylianou, Michele Weiland, Dirk Jacob Verschuur, Jacob Finkenrath

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Performance, performance portability, Portability, Tesla V100

November 12, 2023 by hgpu

Direct GPU Compilation and Execution for Host Applications with OpenMP Parallelism

Shilei Tian, Joseph Huber, Konstantinos Parasyris, Barbara Chapman, Johannes Doefert

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Tags: Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Portability

September 11, 2022 by hgpu

Experience Report: Writing A Portable GPU Runtime with OpenMP 5.1

Shilei Tian, Jon Chesterfield, Johannes Doerfert, Barbara Chapman

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Tags: Computer science, CUDA, HIP, LLVM, MPI, nVidia, OpenMP, Portability, Tesla V100

June 20, 2021 by hgpu

Early Experiences Migrating CUDA codes to oneAPI

Manuel Costanzo, Enzo Rucci, Carlos García Sanchez, Marcelo Naiouf

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce RTX 2070, Portability, SYCL

June 6, 2021 by hgpu

Portable high-order finite element kernels I: Streaming Operations

Noel Chalmers, Tim Warburton

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Tags: AMD, AMD Radeon Instinct MI60, AMD Radeon VII, Computer science, CUDA, FEM, HIP, Mathematical Software, nVidia, nVidia GeForce GTX Titan V, OCCA, Portability, Tesla V100

October 18, 2020 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

High productivity multi-device exploitation with the Heterogeneous Programming Library

Moises Vinas, Basilio B. Fraguela, Diego Andrade, Ramon Doallo

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, load balancing, nVidia, OpenCL, Package, Portability, Tesla K20, Tesla M2050

December 22, 2016 by hgpu

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Erik Zenker, Benjamin Worpitz, Rene Widera, Axel Huebl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, performance portability, Portability, Tesla K20, Tesla K80

March 1, 2016 by hgpu

Performance Portability Study of Linear Algebra Kernels in OpenCL

Karl Rupp, Philippe Tillet, Florian Rudolf, Josef Weinbub, Tibor Grasser, Ansgar Jungel

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Tags: AMD FirePro W9000, ATI, ATI Radeon HD 5850, Computer science, Intel Xeon Phi, Linear Algebra, nVidia, nVidia GeForce GTX 285, OpenCL, Performance, Portability, Tesla K20

September 3, 2014 by hgpu

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

Jianbin Fang, Henk Sips, Ana Lucia Varbanescu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, Local memory, nVidia, OpenCL, Performance, Portability, Tesla C1060, Tesla C2050, Tesla K20

July 29, 2014 by jfang

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

CuPBoP: Making CUDA a Portable Language

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

An approach to performance portability through generic programming

Direct GPU Compilation and Execution for Host Applications with OpenMP Parallelism

Experience Report: Writing A Portable GPU Runtime with OpenMP 5.1

Early Experiences Migrating CUDA codes to oneAPI

Portable high-order finite element kernels I: Streaming Operations

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

High productivity multi-device exploitation with the Heterogeneous Programming Library

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Performance Portability Study of Linear Algebra Kernels in OpenCL

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

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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