high performance computing on graphics processing units: hgpu.org

hgpu.org » Review

Programmability: Design Costs and Payoffs using AMD GPU Streaming Languages and Traditional Multi-Core Libraries

Rick Weber, Robert J. Harrison, Gregory D. Peterson

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Tags: ATI CAL, ATI Radeon HD 4870, ATI Stream, Brook, Computer science, Review

February 19, 2011 by hgpu

Graphics Processing Units and Genetic Programming: An overview

W. B. Langdon

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Tags: Bioinformatics, Computer science, CUDA, Genetic programming, nVidia, Review

February 15, 2011 by hgpu

GPU-powered tools boost molecular visualization

Matthieu Chavent, Bruno Levy, Michael Krone, Katrin Bidmon, Jean-Philippe P. Nomine, Thomas Ertl, Marc Baaden

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Tags: Bioinformatics, Biology, Molecular modeling, nVidia, Review, Visualization

February 12, 2011 by hgpu

Energy-efficient algorithms

Susanne Albers

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Tags: Algorithms, Computer science, Energy-efficient computing, Green, Review

February 11, 2011 by hgpu

Comparing FPGAs to Graphics Accelerators and the Playstation 2 Using a Unified Source Description

Lee W. Howes, Paul Price, Oskar Mencer, Olav Beckmann, Oliver Pell

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Tags: Cg, Computer science, FPGA, nVidia, nVidia GeForce 6800 Ultra, Playstation, Review

February 10, 2011 by hgpu

Cg: a system for programming graphics hardware in a C-like language

William R. Mark, R. Steven Glanville, Kurt Akeley, Mark J. Kilgard

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Tags: 3D Graphics and Realism, Cg, Code generation, Compilers, Computer science, High-level Languages, nVidia, Package, Review

February 10, 2011 by hgpu

QP: A Heterogeneous Multi-Accelerator Cluster

Michael Showerman, Jeremy Enos, Avneesh Pant, Volodymyr Kindratenko, Craig Steffen, Robert Pennington, Wen-mei Hwu

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Tags: Computer science, CUDA, FPGA, GPU cluster, nVidia, nVidia Quadro FX 5600, Review

February 4, 2011 by hgpu

Introduction to GPU programming for EDA

John F. Croix, Sunil P. Khatri

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Tags: Computational Engineering, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Presentation, Review

February 1, 2011 by hgpu

Scientific Computing on Heterogeneous Architectures

A. R. Brodtkorb

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Review, Thesis

January 31, 2011 by hgpu

Introduction to GPGPU, a hardware and software background

Guillaume Colin de Verdiere

Tags: Computer science, Programming techniques, Review

January 16, 2011 by hgpu

Thousand core chips: a technology perspective

Shekhar Borkar

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Tags: Architecture, Computer science, Review

January 7, 2011 by hgpu

Data-parallel computing

Chas. Boyd

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Tags: Computer science, Data parallelism, Review

January 5, 2011 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Programmability: Design Costs and Payoffs using AMD GPU Streaming Languages and Traditional Multi-Core Libraries

Graphics Processing Units and Genetic Programming: An overview

GPU-powered tools boost molecular visualization

Energy-efficient algorithms

Comparing FPGAs to Graphics Accelerators and the Playstation 2 Using a Unified Source Description

Cg: a system for programming graphics hardware in a C-like language

QP: A Heterogeneous Multi-Accelerator Cluster

Introduction to GPU programming for EDA

Scientific Computing on Heterogeneous Architectures

Introduction to GPGPU, a hardware and software background

Thousand core chips: a technology perspective

Data-parallel computing

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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