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hgpu.org » Optical trapping

Using GPUs for Realtime Prediction of Optical Forces on Microsphere Ensembles

Sujal Bista, Sagar Chowdhury, Satyandra K. Gupta, Amitabh Varshney

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Tags: CUDA, nVidia, nVidia GeForce GTX 470, Optical trapping, Physics, Raytracing

November 29, 2012 by hgpu

Speeding Up Particle Trajectory Simulations under Moving Force Fields using GPUs

Robert Patro, John P. Dickerson, Sujal Bista, Satyandra K. Gupta, Amitabh Varshney

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Tags: CUDA, nVidia, Optical trapping, Physics, Tesla S1070

December 28, 2011 by hgpu

Combined acoustic and optical trapping

G. Thalhammer, R. Steiger, M. Meinschad, M. Hill, S. Bernet, M. Ritsch-Marte

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Tags: ATI, ATI Radeon HD 5850, Mesoscale and Nanoscale Physics, Optical trapping, Physics

November 4, 2011 by hgpu

Real-time optical manipulation of micron sized structures using GPU generated holograms

S. Bianchi, R. Di Leonardo

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Tags: Computer holography, CUDA, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX 260, Optical trapping, Physics, Soft Condensed Matter

November 2, 2010 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

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

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

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

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high performance computing on graphics processing units: hgpu.org

Using GPUs for Realtime Prediction of Optical Forces on Microsphere Ensembles

Speeding Up Particle Trajectory Simulations under Moving Force Fields using GPUs

Combined acoustic and optical trapping

Real-time optical manipulation of micron sized structures using GPU generated holograms

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

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

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

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

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

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

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