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Barnes-hut treecode on GPU

Hu Jiang, Qianni Deng

Tags: Algorithms, Computer science, CUDA, N-body simulation, nVidia

April 6, 2011 by hgpu

Implementation of Ant Colony Algorithm Based on GPU

Wang Jiening, Dong Jiankang, Zhang Chunfeng

Tags: Algorithms, Computer science, Path problems

April 6, 2011 by hgpu

FSimGP^2: An Efficient Fault Simulator with GPGPU

Min Li, M.S. Hsiao

Tags: Algorithms, Computer science, CUDA, Fault simulation, nVidia

April 6, 2011 by hgpu

An implementation and its evaluation of password cracking tool parallelized on GPGPU

T. Murakami, R. Kasahara, T. Saito

Tags: Computer science, CUDA, nVidia, Security

April 5, 2011 by hgpu

GpuWars: Design and Implementation of a GPGPU Game

Mark Joselli, Esteban Clua

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Tags: Artificial intelligence, Computer science, CUDA, Game physics, Games, nVidia, nVidia GeForce 8200 M, nVidia GeForce 8800 GS

April 5, 2011 by hgpu

A GPGPU-Based Collision Detection Algorithm

Zou Yisheng, Zhou Xiaoli, Ding Guofu, He Yong, Jia Meiwei

Tags: Collision detection, Computer science

April 5, 2011 by hgpu

A Case Study of SWIM: Optimization of Memory Intensive Application on GPGPU

Wei Yi, Yuhua Tang, Guibin Wang, Xudong Fang

Tags: Computer science, Memory, nVidia, nVidia GeForce GTX 295, Optimization, Performance

April 4, 2011 by hgpu

Many-Thread Aware Prefetching Mechanisms for GPGPU Applications

Jaekyu Lee, Nagesh B. Lakshminarayana, Hyesoon Kim, Richard Vuduc

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce 8800 GT, Performance

April 4, 2011 by hgpu

GPGPU-based Latency Insertion Method: Application to PDN simulations

Y. Inoue, T. Sekine, H. Asai

Tags: Computational Engineering, Computer science

April 4, 2011 by hgpu

Migrating real-time depth image-based rendering from traditional to next-gen GPGPU

S. Rogmans, M. Dumont, G. Lafruit, P. Bekaert

Tags: Computer science, Rendering

April 4, 2011 by hgpu

Parallel connected-component labeling algorithm for GPGPU applications

In-Yong Jung, Chang-Sung Jeong

Tags: Algorithms, Computer science, CUDA, nVidia

April 4, 2011 by hgpu

Exploring GPGPU workloads: Characterization methodology, analysis and microarchitecture evaluation implications

Nilanjan Goswami, Ramkumar Shankar, Madhura Joshi, Tao Li

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Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX

April 4, 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

Barnes-hut treecode on GPU

Implementation of Ant Colony Algorithm Based on GPU

FSimGP^2: An Efficient Fault Simulator with GPGPU

An implementation and its evaluation of password cracking tool parallelized on GPGPU

GpuWars: Design and Implementation of a GPGPU Game

A GPGPU-Based Collision Detection Algorithm

A Case Study of SWIM: Optimization of Memory Intensive Application on GPGPU

Many-Thread Aware Prefetching Mechanisms for GPGPU Applications

GPGPU-based Latency Insertion Method: Application to PDN simulations

Migrating real-time depth image-based rendering from traditional to next-gen GPGPU

Parallel connected-component labeling algorithm for GPGPU applications

Exploring GPGPU workloads: Characterization methodology, analysis and microarchitecture evaluation implications

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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