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GPU Coprocessing for Wireless Network Simulation

Scott Bai, David M. Nicol

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Tags: Computer science, CUDA, KD-tree, Networks, nVidia, nVidia GeForce 9800 GX2, Raytracing, Wireless baseband architecture

February 23, 2011 by hgpu

Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

S. Popov,J. Gunther,H. P. Seidel,P. Slusallek

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Tags: Computer science, CUDA, KD-tree, nVidia, nVidia GeForce 8800 GTX, Raytracing

October 27, 2010 by hgpu

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

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

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

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