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GPGPU flow

Jens Kruger

Tags: Computer science, Review, Visualization

March 9, 2011 by hgpu

Using common graphics hardware for multi-agent traffic simulation with CUDA

David Strippgen, Kai Nagel

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GT, nVidia GeForce GTX 280, Software Engineering

March 8, 2011 by hgpu

Fast heterogeneous computing with CUDA compatible Tesla GPU computing processor (personal supercomputing)

D. Kumar, M. A. Qadeer

Tags: Computer science, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, Tesla

March 8, 2011 by hgpu

Performance and Scalability of GPU-Based Convolutional Neural Networks

Daniel Strigl, Klaus Kofler, Stefan Podlipnig

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Tags: Computer science, CUDA, Neural networks, nVidia

March 8, 2011 by hgpu

Offloading Region Matching of Data Distribution Management with CUDA

Shih Hsiang Lo, Yeh Ching Chung, Fang Ping Pai

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Tags: Computer science, CUDA, Distributed data structures, nVidia, nVidia GeForce 9600 GT

March 8, 2011 by hgpu

IP routing processing with graphic processors

Shuai Mu, Xinya Zhang, Nairen Zhang, Jiaxin Lu, Yangdong Steve Deng, Shu Zhang

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Tags: Bloom filter, Computer science, CUDA, Network communications, Networks, nVidia, nVidia GeForce GTX 280, Software router

March 7, 2011 by hgpu

Application-guided tool development for architecturally diverse computation

Roger D. Chamberlain, Jeremy Buhler, Mark A. Franklin, James H. Buckley

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Tags: Algorithms, Astrophysics, Biology, Computer science, CUDA, Finance, FPGA, Heterogeneous systems, Monte Carlo simulation, nVidia, nVidia GeForce GTX 260, Search, Security

March 7, 2011 by hgpu

Non-blocking programming on multi-core graphics processors: (extended asbtract)

Phuong Hoai Ha, Philippas Tsigas, Otto J. Anshus

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce 8800 GTX, Performance, Programming techniques

March 7, 2011 by hgpu

CUDA-based AES parallelization with fine-tuned GPU memory utilization

Chonglei Mei, Hai Jiang, Jeff Jenness

Tags: Computer science, CUDA, nVidia, Security

March 7, 2011 by hgpu

Designing scalable many-core parallel algorithms for min graphs using CUDA

Quoc-Nam Tran

Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia

March 7, 2011 by hgpu

Object-oriented stream programming using aspects

Mingliang Wang, M. Parashar

Tags: Computer science, CUDA, Hybrid computing, nVidia, nVidia GeForce GTX 280, Programming techniques

March 7, 2011 by hgpu

Object-oriented stream programming using Aspects: a high-productivity programming paradigm for hybrid platforms

Mingliang Wang

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Tags: Computer science, CUDA, Hybrid computing, nVidia, nVidia GeForce GTX 280, Programming techniques, Thesis

March 7, 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

GPGPU flow

Using common graphics hardware for multi-agent traffic simulation with CUDA

Fast heterogeneous computing with CUDA compatible Tesla GPU computing processor (personal supercomputing)

Performance and Scalability of GPU-Based Convolutional Neural Networks

Offloading Region Matching of Data Distribution Management with CUDA

IP routing processing with graphic processors

Application-guided tool development for architecturally diverse computation

Non-blocking programming on multi-core graphics processors: (extended asbtract)

CUDA-based AES parallelization with fine-tuned GPU memory utilization

Designing scalable many-core parallel algorithms for min graphs using CUDA

Object-oriented stream programming using aspects

Object-oriented stream programming using Aspects: a high-productivity programming paradigm for hybrid platforms

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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