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A Multi-Stage CUDA Kernel for Floyd-Warshall

Ben Lund, Justin W Smith

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Tags: Computer science, CUDA, nVidia, Optimization, Performance, Programming techniques, Tesla C1060

January 18, 2011 by hgpu

Fast GPGPU Data Rearrangement Kernels using CUDA

Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Performance, Tesla C1060

January 18, 2011 by hgpu

Parallelization of Weighted Sequence Comparison by using EBWT

Binay Kumar Pandey, Shashank Srikant, Dr. Rajdeep Niyogi, Dr. Ankush Mittal

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia Quadro FX 3700

January 18, 2011 by hgpu

GPGPUs in computational finance: Massive parallel computing for American style options

Gilles Pages, Benedikt Wilbertz

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Tags: Computational finance, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Probability

January 18, 2011 by hgpu

Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision

Peter H. Colberg, Felix Hofling

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Tags: Computational Physics, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Package, Physics, Soft Condensed Matter

January 18, 2011 by hgpu

GPU-based Collision Detection for Deformable Parameterized Surfaces

Alexander Gress, Michael Guthe, Reinhard Klein

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Tags: 3D Graphics and Realism, Collision detection, Computer science, nVidia, nVidia GeForce 7800 GTX, Shaders

January 17, 2011 by hgpu

Visual Simulation of Flow

Arie E. Kaufman, Ye Zhao

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Tags: Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, nVidia GeForce FX 5950 Ultra

January 17, 2011 by hgpu

Solving dense linear systems on platforms with multiple hardware accelerators

Enrique S. Quintana-Orti, Francisco D. Igual, Enrique S. Quintana-Orti, Robert A. van de Geijn

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Tags: Computer science, CUDA, GPU cluster, Linear Algebra, nVidia, Package, Task scheduling, Tesla S870

January 17, 2011 by hgpu

CheCUDA: A Checkpoint/Restart Tool for CUDA Applications

Hiroyuki Takizawa, Katsuto Sato, Kazuhiko Komatsu,

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8600 M GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280

January 17, 2011 by hgpu

Evaluating GPUs for network packet signature matching

Randy Smith, Neelam Goyal, Justin Ormont, Karthikeyan Sankaralingam, Cristian Estan

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Tags: Computer science, CUDA, Networking and Internet Architecture, Networks, nVidia, nVidia GeForce 8800 GTX

January 17, 2011 by hgpu

Real-Time Non-rigid Registration of Medical Images on a Cooperative Parallel Architecture

Yixun Liu, Andriy Fedorov, Ron Kikinis, Nikos Chrisochoides

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Tags: Bioinformatics, CUDA, Image registration, Medicine, nVidia, nVidia GeForce 8800 GT

January 17, 2011 by hgpu

Acceleration of Acoustic Emission Signal Processing Algorithms using CUDA Standard

Lubomir Riha, Radislav Smid

Tags: Acoustics, CUDA, nVidia, Signal processing

January 17, 2011 by hgpu

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

A Multi-Stage CUDA Kernel for Floyd-Warshall

Fast GPGPU Data Rearrangement Kernels using CUDA

Parallelization of Weighted Sequence Comparison by using EBWT

GPGPUs in computational finance: Massive parallel computing for American style options

Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision

GPU-based Collision Detection for Deformable Parameterized Surfaces

Visual Simulation of Flow

Solving dense linear systems on platforms with multiple hardware accelerators

CheCUDA: A Checkpoint/Restart Tool for CUDA Applications

Evaluating GPUs for network packet signature matching

Real-Time Non-rigid Registration of Medical Images on a Cooperative Parallel Architecture

Acceleration of Acoustic Emission Signal Processing Algorithms using CUDA Standard

Recent source codes

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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