high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

Dispersion Simulation and Visualization For Urban Security

Arie E. Kaufman, Feng Qiu, Haik Lorenz, Jianning Wang, Suzanne Yoakum-Stover, Arie Kaufman, Klaus Mueller

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Tags: Computer science, Lattice Boltzmann model, nVidia, nVidia GeForce FX 5950 Ultra, OpenGL, Visualization

February 4, 2011 by hgpu

Depth Enhanced Panoramas

Gleb Bahmutov, Voicu Popescu, Elisha Sacks

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia Quadro FX 3000, OpenGL, Visualization

February 4, 2011 by hgpu

Lattice Based Volumetric Global Illumination

Feng Qiu, Fang Xu, Zhe Fan, Neophytou Neophytos, Arie Kaufman, Klaus Mueller

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Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce 8800 GTX, Rendering, Visualization

February 4, 2011 by hgpu

Phoenix: A Runtime Environment for High Performance Computing on Chip Multiprocessors

Avneesh Pant, Hassan Jafri, Volodymyr Kindratenko

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Tags: Algorithms, Computer science, MPI

February 4, 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

On testing GPU memory for hard and soft errors

Guochun Shi, Jeremy Enos, Michael Showerman, Volodymyr Kindratenko

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Source codes

Tags: Computer science, CUDA, Hardware Architecture, Memory, nVidia, Package, Tesla C1060, Tesla S1070

February 3, 2011 by hgpu

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

J. Enos, C. Steffen, J. Fullop, M. Showerman, G. Shi, K. Esler, V. Kindratenko, J. Stone, J. Phillips

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, Performance

February 3, 2011 by hgpu

3D Information Extraction Based on GPU

Ying Zhang

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 8400 M GS, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 3700, Tesla C1060

February 3, 2011 by hgpu

3D GPU Architecture using Cache Stacking: Performance, Cost, Power and Thermal analysis

Ahmed Al Maashri, Guangyu Sun, Xiangyu Dong, Vijay Narayanan and Yuan Xie

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Tags: Computer science, CUDA, Hardware Architecture, Memory, Memory model, nVidia, nVidia GeForce 6400 GT

February 3, 2011 by hgpu

3D finite element numerical integration on GPUs

Pawel Maciol, Przemyslaw Plaszewski and Krzysztof Banasa

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Tags: Computer science, CUDA, Finite element method, nVidia, nVidia GeForce 8800 GTX

February 3, 2011 by hgpu

Data access optimized applications on the GPU using NVIDIA CUDA

Dheevatsa Mudiger

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Tags: Computer science, CUDA, Data Structures and Algorithms, Fluid dynamics, nVidia, Tesla C1060, Tesla C870, Thesis

February 3, 2011 by hgpu

Software parallel CAVLC encoder based on stream processing

Ju Ren, Yi He, Wei Wu, Mei Wen, Nan Wu, Chunyuan Zhang

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Tags: Computer science, H.264/AVC, nVidia, nVidia GeForce 8800 GTX, Video decoding

February 2, 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

Dispersion Simulation and Visualization For Urban Security

Depth Enhanced Panoramas

Lattice Based Volumetric Global Illumination

Phoenix: A Runtime Environment for High Performance Computing on Chip Multiprocessors

QP: A Heterogeneous Multi-Accelerator Cluster

On testing GPU memory for hard and soft errors

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

3D Information Extraction Based on GPU

3D GPU Architecture using Cache Stacking: Performance, Cost, Power and Thermal analysis

3D finite element numerical integration on GPUs

Data access optimized applications on the GPU using NVIDIA CUDA

Software parallel CAVLC encoder based on stream processing

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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