high performance computing on graphics processing units: hgpu.org

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HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

Michael Riera, Erfan Bank Tavakoli, Masudul Hassan Quraishi, Fengbo Ren

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Tags: Computer science, FPGA, Heterogeneous systems, HPC, MPI, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, performance portability

November 29, 2020 by hgpu

Adaptive Data Migration in Load-Imbalanced HPC Applications

Parsa Amini

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, load balancing, nVidia, Package, Tesla P100, Tesla V100, Thesis

November 15, 2020 by hgpu

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, OpenCL, Tesla K20, Thesis

November 1, 2020 by hgpu

Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)

Sarah AlAhmadi, Thaha Mohammed, Aiiad Albeshri, Iyad Katib, Rashid Mehmood

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Tags: Computer science, Heterogeneous systems, HPC, Sparse matrix

October 18, 2020 by hgpu

On the performance of a highly-scalable Computational Fluid Dynamics code on AMD, ARM and Intel processors

Pablo Ouro, Unai Lopez-Novoa, Martyn Guest

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Tags: ARM, cfd, Fluid dynamics, HPC, Performance

October 18, 2020 by hgpu

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Jianqi Lai, Hang Yu, Zhengyu Tian, Hua Li

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Tags: cfd, CUDA, Fluid dynamics, GPU cluster, HPC, MPI, nVidia, nVidia GeForce GTX 1070, OpenMP, Tesla V100

September 27, 2020 by hgpu

Accelerating High-Order Stencils on GPUs

Ryuichi Sai, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo, Jie Meng

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Tags: Computer science, HPC, nVidia, nVidia Quadro NVS 510, Package, Performance, Stencil computation, Tesla P100, Tesla V100

September 13, 2020 by hgpu

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

Twinkle Jain, Gene Cooperman

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Tags: Computer science, CUDA, HPC, nVidia, nVidia Quadro K600, Package, Tesla V100

August 30, 2020 by hgpu

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Charlene Yang

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Tags: Computer science, HPC, nVidia, OpenACC, Optimization, Package, Performance, Tesla V100

August 30, 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

Compyle: a Python package for parallel computing

Aditya Bhosale, Prabhu Ramachandran

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Tags: Code generation, Computer science, CUDA, HPC, nVidia, OpenCL, Package, Python, Tesla P100

July 19, 2020 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

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

Adaptive Data Migration in Load-Imbalanced HPC Applications

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)

On the performance of a highly-scalable Computational Fluid Dynamics code on AMD, ARM and Intel processors

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Accelerating High-Order Stencils on GPUs

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

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

Compyle: a Python package for parallel computing

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

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)