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Methodology of control and supervision of web connected mobile robots with CUDA technology application

Janusz Bedkowski, Andrzej Maslowski

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia Quadro FX 1600 M, nVidia Quadro FX 3700

December 25, 2011 by hgpu

A Scalable End-to-End Optimized Real-Time Image-Based Rendering Framework on Graphics Hardware

Sammy Rogmans, Jiangbo Lu, Gauthier Lafruit

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Tags: Computer science, nVidia, nVidia GeForce 7900 GTX, nVidia GeForce 8800 GT, nVidia Quadro FX 1600 M, Presentation, Rendering

June 20, 2011 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

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

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

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

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

BoltzGen:Toward Universal Binder Design

BoltzGen:Toward Universal Binder Design

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

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

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