high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla T10P

November 27, 2010 by hgpu

The Virtual Marathon: Parallel Computing Supports Crowd Simulations

Erdal Yilmaz, Veysi Isler, Yasemin Y. Cetin

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Tags: Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Visualization

November 27, 2010 by hgpu

Accuracy and performance of graphics processors: A Quantum Monte Carlo application case study

Jeremy S. Meredith, Gonzalo Alvarez, Thomas A. Maier, Thomas C. Schulthess, Jeffrey S. Vetter

Tags: Computer science, Monte Carlo simulation, Performance

November 27, 2010 by hgpu

Triangular matrix inversion on Graphics Processing Unit

Florian Ries, Tommaso De Marco, Matteo Zivieri, Roberto Guerrieri

Tags: Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 26, 2010 by hgpu

Fast Disk Encryption through GPGPU Acceleration

Giovanni Agosta, Alessandro Barenghi, Fabrizio D. Santis, Andrea D. Biagio, Gerardo Pelosi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Package, Security

November 26, 2010 by hgpu

On the energy efficiency of graphics processing units for scientific computing

S. Huang, S. Xiao, W. Feng

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

November 26, 2010 by hgpu

Compute Unified Device Architecture Application Suitability

Wen M. Hwu, Christopher Rodrigues, Shane Ryoo, John Stratton

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

November 25, 2010 by hgpu

A Superresolution Framework for High-Accuracy Multiview Reconstruction

Bastian Goldlucke, Daniel Cremers

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Tags: Computer science, Optimization, PDEs

November 25, 2010 by hgpu

Novel Computing Architectures

Volodymyr Kindratenko

Tags: Cell processor, Computer science, FPGA, Review

November 25, 2010 by hgpu

A particle-based method for viscoelastic fluids animation

Yuanzhang Chang, Kai Bao, Youquan Liu, Jian Zhu, Enhua Wu

Tags: 3D Graphics and Realism, Computer science, Fluid dynamics, Rendering

November 25, 2010 by hgpu

Designing and optimizing compute kernels on NVIDIA GPUs

Damir A. Jamsek

Tags: CAD, Computer science, CUDA, nVidia

November 25, 2010 by hgpu

Fast minimum spanning tree for large graphs on the GPU

Vibhav Vineet, Pawan Harish, Suryakant Patidar, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Tesla S1070

November 25, 2010 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

The Virtual Marathon: Parallel Computing Supports Crowd Simulations

Accuracy and performance of graphics processors: A Quantum Monte Carlo application case study

Triangular matrix inversion on Graphics Processing Unit

Fast Disk Encryption through GPGPU Acceleration

On the energy efficiency of graphics processing units for scientific computing

Compute Unified Device Architecture Application Suitability

A Superresolution Framework for High-Accuracy Multiview Reconstruction

Novel Computing Architectures

A particle-based method for viscoelastic fluids animation

Designing and optimizing compute kernels on NVIDIA GPUs

Fast minimum spanning tree for large graphs on the GPU

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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

Agentic Code Optimization via Compiler-LLM Cooperation

Device Virtual Machine (DVM)

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

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