high performance computing on graphics processing units: hgpu.org

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Scalable Multi-GPU Simulation of Long-Range Molecular Dynamics

Marcos Novalbos, Jaime Gonzalez, Miguel A. Otaduy, Roberto Martinez Benito, Alberto Sanchez

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Tags: Algorithms, Biomolecules, Chemistry, CUDA, Electrostatic potential, Electrostatics, FFT, Molecular dynamics, Multilevel summation, N-body simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan

September 15, 2014 by hgpu

Accelerated Variance Reduction Methods on GPU

Chuan-Hsiang Han, Yu-Tuan Lin

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Tags: Algorithms, Computational finance, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 690, Option pricing, Probability

September 15, 2014 by hgpu

Scalable Parallel Tridiagonal Algorithms with Diagonal Pivoting and Their Optimization for Many-Core Architectures

Li-Wen Chang

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

September 15, 2014 by hgpu

Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi

Jianbin Fang, Ana Lucia Varbanescu, Baldomero Imbernon, Jose M. Cecilia, Horacio Perez-Sanchez

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Tags: Bioinformatics, Biology, CUDA, Electrostatics, Intel Xeon Phi, nVidia, Performance, Pharmaceuticals, Tesla K20

September 13, 2014 by hgpu

Parallel CYK Membership Test on GPUs

Kyoung-Hwan Kim, Sang-Min Choi, Hyein Lee, Ka Lok Man, Yo-Sub Han

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti

September 13, 2014 by hgpu

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Makoto Sadahiro

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Tags: Acoustics, CUDA, Finite difference, Fortran, nVidia, Physics, Tesla K20, Thesis

September 13, 2014 by hgpu

Performance and Power Optimization of GPU Architectures for General-purpose Computing

Yue Wang

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Tags: Computer science, CUDA, GPGPU-sim, Heterogeneous systems, nVidia, Thesis

September 13, 2014 by hgpu

Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Jin Wang, Sudhakar Yalamanchili

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX Titan, Tesla K20, Tesla K40

September 11, 2014 by hgpu

Parallelized Seeded Region Growing using CUDA

Seongjin Park, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, Bohyoung Kim

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Tags: Algorithms, Computer science, CUDA, HLSL, Medicine, nVidia, nVidia GeForce GTX 285

September 11, 2014 by hgpu

Enhancing R with Advanced Compilation Tools and Methods

Duncan Temple Lang

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Tags: Computer science, CUDA, Mathematical Software, nVidia, Programming Languages

September 11, 2014 by hgpu

Parallel Multi-dimensional Range Query Processing with R-Trees on GPU

Jinwoong Kim, Beomseok Nam

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Tags: Algorithms, Computer science, CUDA, Databases, Indexing, nVidia, Tesla M2090

September 9, 2014 by hgpu

Improvements to Physically Based Cloth Simulation

Tsz Ho Wong

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Tags: 3D Graphics and Realism, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 780, Review, Thesis

September 9, 2014 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

Scalable Multi-GPU Simulation of Long-Range Molecular Dynamics

Accelerated Variance Reduction Methods on GPU

Scalable Parallel Tridiagonal Algorithms with Diagonal Pivoting and Their Optimization for Many-Core Architectures

Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi

Parallel CYK Membership Test on GPUs

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Performance and Power Optimization of GPU Architectures for General-purpose Computing

Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Parallelized Seeded Region Growing using CUDA

Enhancing R with Advanced Compilation Tools and Methods

Parallel Multi-dimensional Range Query Processing with R-Trees on GPU

Improvements to Physically Based Cloth Simulation

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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