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

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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

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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

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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

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

November 2, 2010 by hgpu

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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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

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

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