high performance computing on graphics processing units: hgpu.org

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GPUQP: query co-processing using graphics processors

Rui Fang, Bingsheng He, Mian Lu, Ke Yang, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, Package, Review

December 4, 2010 by hgpu

GPU Computing: Programming a Massively Parallel Processor

Ian Buck

Tags: Computer science, CUDA, High-level Languages, nVidia, Review

December 4, 2010 by hgpu

GPU-friendly warped display for scope-maintained video surveillance

Wang, Guangyu, Wong, Tien-Tsin, Heng, Pheng-Ann

Tags: Image processing, Review

December 4, 2010 by hgpu

Streaming architectures and technology trends

John Owens

Tags: Computer science, Review

December 1, 2010 by hgpu

Taking the graphics processor beyond graphics

D. Geer

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Tags: Computer science, Review

November 30, 2010 by hgpu

Scientific computation for simulations on programmable graphics hardware

Robert Strzodka, Michael Doggett, Andreas Kolb

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Tags: Computer science, Numerical simulation, Review

November 30, 2010 by hgpu

A survey of point-based techniques in computer graphics

Leif Kobbelt, Mario Botsch

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Rendering, Review

November 29, 2010 by hgpu

A survey of BRDF models for computer graphics

Murat Kurt, Dave Edwards

Tags: 3D Graphics and Realism, Computer science, Image processing, Review

November 27, 2010 by hgpu

Novel Computing Architectures

Volodymyr Kindratenko

Tags: Cell processor, Computer science, FPGA, Review

November 25, 2010 by hgpu

State-of-the-art in heterogeneous computing

Andre R. Brodtkorb, Christopher Dyken, Trond R. Hagen, Jon M. Hjelmervik, Olaf O. Storaasli

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Tags: Computer science, Energy-efficient computing, Review

November 22, 2010 by hgpu

Scalable parallel programming with CUDA

John Nickolls, Ian Buck, Michael Garland, Kevin Skadron

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Tags: Computer science, CUDA, nVidia, Programming techniques, Review

November 17, 2010 by hgpu

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware

Robert J. Brunner, Volodymyr V. Kindratenko, Adam D. Myers

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Tags: Algorithms, Astrophysics, Computer science, FPGA, Review

November 13, 2010 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

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

BoltzGen:Toward Universal Binder Design

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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high performance computing on graphics processing units: hgpu.org

GPUQP: query co-processing using graphics processors

GPU Computing: Programming a Massively Parallel Processor

GPU-friendly warped display for scope-maintained video surveillance

Streaming architectures and technology trends

Taking the graphics processor beyond graphics

Scientific computation for simulations on programmable graphics hardware

A survey of point-based techniques in computer graphics

A survey of BRDF models for computer graphics

Novel Computing Architectures

State-of-the-art in heterogeneous computing

Scalable parallel programming with CUDA

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware

Recent source codes

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

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

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

BoltzGen:Toward Universal Binder Design

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

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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