high performance computing on graphics processing units: hgpu.org

Aristotle Martin, Geng Liu, William Ladd, Seyong Lee, John Gounley, Jeffrey Vetter, Saumil Patel, Silvio Rizzi, Victor Mateevitsi, Joseph Insley, Amanda Randles

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Tags: AMD Radeon Instinct MI250X, ATI, cfd, CUDA, Fluid dynamics, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia V100, performance portability, SYCL

December 31, 2023 by hgpu

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Stefano Markidis

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Tags: AMD Radeon Instinct MI250X, Computer science, CUDA, HIP, nVidia, nVidia A100, Quantum computing

December 31, 2023 by hgpu

Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay

Konstantinos Parasyris, Giorgis Georgakoudis, Esteban Rangel, Ignacio Laguna, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250X, ATI, Auto-Tuning, Computer science, Heterogeneous systems, nVidia, nVidia V100, OpenMP, Package

December 18, 2023 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

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high performance computing on graphics processing units: hgpu.org

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems: Architectures, Algorithms, and Applications

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

OpenMP offload at the Exascale using Intel GPU Max 1550: evaluation of STREAmS compressible solver

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

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Hybrid quantum programming with PennyLane Lightning on HPC platforms

An Evaluative Comparison of Performance Portability across GPU Programming Models

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay

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

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