high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce FX 7800

Practical considerations for GPU-accelerated CT

Klaus Mueller, Fang Xu

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Tags: Computed tomography, CT, Image processing, Medicine, nVidia, nVidia GeForce FX 7800, OpenGL

May 19, 2011 by hgpu

ZAME: Interactive Large-Scale Graph Visualization

N. Elmqvist, Thanh-Nghi Do, H. Goodell, N. Henry, J. D. Fekete

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Tags: Computer science, nVidia, nVidia GeForce FX 7800, OpenGL, Visualization

December 12, 2010 by hgpu

Particle-Based Fluid Simulation on the GPU

Kyle Hegeman, Nathan Carr, Gavin Miller

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Tags: Fluid dynamics, nVidia, nVidia GeForce FX 7800

November 8, 2010 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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

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high performance computing on graphics processing units: hgpu.org

Practical considerations for GPU-accelerated CT

ZAME: Interactive Large-Scale Graph Visualization

Particle-Based Fluid Simulation on the GPU

Recent source codes

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

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

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