high performance computing on graphics processing units: hgpu.org

hgpu.org » AMD Radeon Instinct MI250X

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Milo Lurati, Stijn Heldens, Alessio Sclocco, Ben van Werkhoven

View

Tags: AMD Radeon Instinct MI250X, AMD Radeon Pro W6600, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia RTX A4000, Package, Performance, Python

July 28, 2024 by hgpu

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

Johannes Pekkilä, Oskar Lappi, Fredrik Robertsén, Maarit J. Korpi-Lagg

View

Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Energy-efficient computing, HIP, nVidia, nVidia A100, nVidia V100, Package, Performance, PyTorch, Stencil computation

June 16, 2024 by hgpu

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

Martin Karp

View

Tags: AMD Radeon Instinct MI250X, ATI, cfd, Fluid dynamics, FPGA, Heterogeneous systems, Numerical simulation, nVidia, nVidia A100, nVidia P100, nVidia V100, OpenCL, Spectral elements, Thesis

May 12, 2024 by hgpu

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

Andrey Alekseenko, Szilárd Páll, Erik Lindahl

View

Tags: AMD Radeon Instinct MI250X, ATI, Biomolecules, Computational Physics, Computer science, Heterogeneous systems, Molecular dynamics, Molecular simulation, Performance, SYCL

May 5, 2024 by hgpu

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

Francesco Salvadore, Giacomo Rossi, Srikanth Sathyanarayana, Matteo Bernardini

View

Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, cfd, Compression, Fluid dynamics, Intel, Intel Data Center GPU Max 1550, nVidia, nVidia A100, OpenMP

April 14, 2024 by hgpu

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

John Tramm, Paul Romano, Patrick Shriwise, Amanda Lund, Johannes Doerfert, Patrick Steinbrecher, Andrew Siegel, Gavin Ridley

View

Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenMP, Package, performance portability

March 24, 2024 by hgpu

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

Xinyi Li, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, Ganesh Gopalakrishnan

View

Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, Hardware Architecture, HPC, Matrix multiplication, nVidia, nVidia A100, nVidia H100, nVidia V100, PTX

March 10, 2024 by hgpu

Hybrid quantum programming with PennyLane Lightning on HPC platforms

Ali Asadi, Amintor Dusko, Chae-Yeun Park, Vincent Michaud-Rioux, Isidor Schoch, Shuli Shu, Trevor Vincent, Lee James O'Riordan

View

Tags: AMD Radeon Instinct MI250X, ATI, HPC, nVidia, nVidia A100, OpenMP, Package, Physics, Quantum Physics

March 10, 2024 by hgpu

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

View

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

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

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

View

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

View

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

View

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

December 18, 2023 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

XaaS containers

Acceleration as a Service (XaaS) Source Containers

CASS: Cuda-Amd aSSembly

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Cluser of smartphones for edge computing application using TensorFlow

Low-cost edge computing using upcycled smartphones

SYCL Container

Exploring SYCL for batched kernels with memory allocations

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us:

contact@hpgu.org