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hgpu.org » Applications » Computer science

OpenGL application live migration with GPU acceleration in personal cloud

Yunbiao Lin, Wei Wang, Kyle Gui

Tags: Algorithms, Computer science, OpenGL, Software Engineering, Virtualization

February 25, 2011 by hgpu

Achieving a single compute device image in OpenCL for multiple GPUs

Jungwon Kim, Honggyu Kim, Joo H. Lee, Jaejin Lee

Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Optimization

February 25, 2011 by hgpu

Statistical Power Consumption Analysis and Modeling for GPU-based Computing

Xiaohan Ma, Mian Dong, Lin Zhong, Zhigang Deng

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GT

February 24, 2011 by hgpu

Barra, a Modular Functional GPU Simulator for GPGPU

Sylvain Collange, David Defour, David Parello

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Package, Performance

February 24, 2011 by hgpu

An efficient GPU-based approach for interactive global illumination

Rui Wang, Rui Wang, Kun Zhou, Minghao Pan, Hujun Bao

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Tags: 3D Graphics and Realism, Computer science, KD-tree, nVidia, nVidia GeForce GTX 280, Rendering

February 24, 2011 by hgpu

Cloth Simulation on the GPU

Cyril Zeller

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce 6800 Ultra, OpenGL, Presentation, Rendering

February 24, 2011 by hgpu

Accelerating Quantum Monte Carlo Simulations with Emerging Architectures

Akila Gothandaraman

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Tags: Computer science, CUDA, FPGA, nVidia, QMC, Tesla C1060, Tesla C870, Thesis

February 24, 2011 by hgpu

Parallel Cloth Simulation Using OpenMP and CUDA

Gillian David Sims

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Tags: Computer science, Conjugate gradient solver, CUBLAS, CUDA, nVidia, nVidia GeForce 9800 GTX, OpenMP, Rendering, Thesis

February 24, 2011 by hgpu

Many-core GPU computing with NVIDIA CUDA

Mark Harris

Tags: Computer science, CUDA, High-level Languages, nVidia, Programming Languages

February 24, 2011 by hgpu

Iterative GPGPU Linear Solvers for Sparse Matrices

Filip Vesely

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Tags: Brook, Computer science, nVidia, nVidia GeForce 8600 M GS, RapidMind, Sparse linear iterative solvers, Thesis

February 24, 2011 by hgpu

Fast Implementation of Two Hash Algorithms on nVidia CUDA GPU

Gorka Lerchundi Osa

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce 9400 GT, Thesis

February 24, 2011 by hgpu

Dense Matrix Algebra on the GPU

Adam Moravanszky

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Tags: ATI, ATI Radeon 9500 Pro, ATI Radeon 9700 Pro, Computer science, DirectX, Linear Algebra, Matrix multiplication

February 24, 2011 by hgpu

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

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

Hunting CUDA Bugs at Scale with cuFuzz

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high performance computing on graphics processing units: hgpu.org

Applications

OpenGL application live migration with GPU acceleration in personal cloud

Achieving a single compute device image in OpenCL for multiple GPUs

Statistical Power Consumption Analysis and Modeling for GPU-based Computing

Barra, a Modular Functional GPU Simulator for GPGPU

An efficient GPU-based approach for interactive global illumination

Cloth Simulation on the GPU

Accelerating Quantum Monte Carlo Simulations with Emerging Architectures

Parallel Cloth Simulation Using OpenMP and CUDA

Many-core GPU computing with NVIDIA CUDA

Iterative GPGPU Linear Solvers for Sparse Matrices

Fast Implementation of Two Hash Algorithms on nVidia CUDA GPU

Dense Matrix Algebra on the GPU

Recent source codes

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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

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

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

True 4-Bit Quantized CNN Training on CPU

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

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