high performance computing on graphics processing units: hgpu.org

hgpu.org » AMD Vega FE

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

View

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

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