high performance computing on graphics processing units: hgpu.org

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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

General-purpose GPU computing: practice and experience

Bartlett S. H. (Scott) Michel

Tags: Computer science, Programming techniques, Review

March 24, 2011 by hgpu

Data Visualization and Mining using the GPU

Sudipto Guha

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Tags: Computer science, Data mining, Rendering, Review, Visualization

March 23, 2011 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

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

General-purpose GPU computing: practice and experience

Data Visualization and Mining using the GPU

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Most viewed papers (last 30 days)