high performance computing on graphics processing units: hgpu.org

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Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, CUDA, Data parallelism, Memory model, nVidia, nVidia GeForce GTX 285, Review

June 17, 2011 by hgpu

Recent trends in software and hardware for GPGPU computing: A comprehensive survey

B. Neelima, P.S. Raghavendra

Tags: Computer science, Programming techniques, Review

May 31, 2011 by hgpu

A Micro-benchmark Suite for AMD GPUs

Ryan Taylor, Xiaoming Li

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Tags: ATI, ATI IL, ATI Stream, Benchmarking, Computer science, Presentation, Review, RV770, RV870

May 30, 2011 by hgpu

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

I.M. Williams

Tags: Computer science, Performance, Review

May 29, 2011 by hgpu

The GPU enters computing’s mainstream

Michael Macedonia

Tags: ATI, Computer science, nVidia, Review

May 16, 2011 by hgpu

K3 Moore’s Law in the Era of GPU Computing

Chien-Ping Lu

Tags: Computer science, Hardware Architecture, nVidia, Review

May 15, 2011 by hgpu

Fermi GF100 GPU Architecture

Craig M. Wittenbrink, Emmett Kilgariff, Arjun Prabhu

Tags: Computer science, CUDA, Hardware Architecture, nVidia, Review

May 15, 2011 by hgpu

Why does PHM matter? – Nvidia’s GPU problems reviewed

P.K. Rundle

Tags: Medicine, nVidia, Review

May 13, 2011 by hgpu

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

Chris Gregg, Kim Hazelwood

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Tags: Computer science, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GT 330 M, nVidia GeForce GTX 480, Performance, Review, Tesla C2050

May 11, 2011 by hgpu

Graphics hardware & GPU computing: past, present, and future

David Luebke

Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce GTX 280, Review

May 4, 2011 by hgpu

Tutorial 3: Methodologies and Performance Impacts of General Purpose Computing on GPUs

Shinichi Yamagiwa

Tags: Computer science, Review, Tutorial

April 20, 2011 by hgpu

Rendering of 3D Dynamic Virtual Environments

Salvatore Catanese, Emilio Ferrara, Giacomo Fiumara, Francesco Pagano

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Tags: 3D Graphics and Realism, Computer science, DirectX, Games, OpenGL, Review, Virtual reality

March 29, 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

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

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Recent trends in software and hardware for GPGPU computing: A comprehensive survey

A Micro-benchmark Suite for AMD GPUs

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

The GPU enters computing’s mainstream

K3 Moore’s Law in the Era of GPU Computing

Fermi GF100 GPU Architecture

Why does PHM matter? – Nvidia’s GPU problems reviewed

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

Graphics hardware & GPU computing: past, present, and future

Tutorial 3: Methodologies and Performance Impacts of General Purpose Computing on GPUs

Rendering of 3D Dynamic Virtual Environments

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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