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Support Vector Machines on GPU with Sparse Matrix Format

Tsung-Kai Lin, Shao-Yi Chien

Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX 470

April 8, 2011 by hgpu

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

A El Zein, E McCreath, Alistair Rendell, Alex Smola

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce 8800 GTX, Presentation, Sparse matrix

March 20, 2011 by hgpu

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Mark Van Heeswijk, Yoan Miche, Erkki Oja, Amaury Lendasse

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Tags: Computer science, CUDA, CULA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 295, Tesla C1060

March 20, 2011 by hgpu

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

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

BoltzGen:Toward Universal Binder Design

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

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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