high performance computing on graphics processing units: hgpu.org

hgpu.org » Anisotropy

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

Weiyi Xia, Maxim Moraru, Ying Wai Li, Timothy Liao, James R. Chelikowsky, Cai-Zhuang Wang

View

Download (PDF)

Source codes

Tags: Anisotropy, Computational Physics, Condensed matter, CUDA, Machine learning, Materials Science, Neural networks, nVidia, nVidia A100, Package, Physics

July 6, 2025 by hgpu

Nucleation Studies on Graphics Processing Units

Benjamin J. Block

View

Download (PDF)

Tags: Anisotropy, ATI, ATI Radeon HD 6970, Biology, Chemistry, Differential equations, Ising model, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Physics, Tesla K20, Thesis

February 6, 2015 by hgpu

Analysis of periodic anisotropic media by means of split-field FDTD method and GPU computing

J. Frances, S. Bleda, M. L. Alvarez, F. J. Martinez, A. Marquez, C. Neipp, A. Belendez

View

Download (PDF)

Tags: Anisotropy, CUDA, FDTD, Finite-difference time-domain, Liquid crystals, nVidia, nVidia GeForce GTX 470, Physics

November 11, 2012 by hgpu

On modelling of anisotropic viscoelasticity for soft tissue simulation: numerical solution and GPU execution

Z. A. Taylor, O. Comas, M. Cheng, J. Passenger, D. J. Hawkes, D. Atkinson, S. Ourselin

View

Download (PDF)

Tags: Anisotropy, CUDA, Finite element method, Medicine, Nonlinear analysis, nVidia GeForce 8800 GTX, Surgical simulation, Viscoelasticity

November 2, 2010 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

Nucleation Studies on Graphics Processing Units

Analysis of periodic anisotropic media by means of split-field FDTD method and GPU computing

On modelling of anisotropic viscoelasticity for soft tissue simulation: numerical solution and GPU execution

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

Most viewed papers (last 30 days)