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Kinematic Modelling of Disc Galaxies using Graphics Processing Units

Georgios Bekiaris, Karl Glazebrook, Christopher J. Fluke, Roberto Abraham

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Tags: Algorithms, Astrophysics, CUDA, nVidia, Package, Tesla C2070, Tesla K40

October 8, 2015 by hgpu

CVC: The Contourlet Video Compression algorithm for real-time applications

Stamos Katsigiannis, Georgios Papaioannou, Dimitris Maroulis

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GXT 570, YCoCg

October 6, 2015 by hgpu

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

Azzam Haidar, Stanimire Tomov, Piotr Luszczek, Jack Dongarra

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Jetson TK1, Package

October 6, 2015 by hgpu

Parallel Graph Algorithms on the Xeon Phi Coprocessor

Dennis Felsing

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Tags: Algorithms, Computer science, Graph theory, Intel Xeon Phi, OpenMP, Thesis, Visualization

October 6, 2015 by hgpu

Optimizing GPU-accelerated Group-By and Aggregation

Tomas Karnagel, Rene Mueller, Guy M. Lohman

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan

October 6, 2015 by hgpu

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Rahul Garg

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Tags: AMD Radeon HD 8650, ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Computer science, Intel HD Graphics 4000, Matlab, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Package, Python, Tesla C2050, Thesis

October 6, 2015 by hgpu

Tuned and GPU-accelerated parallel data mining from comparable corpora

Krzysztof Wolk, Krzysztof Marasek

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Tags: Algorithms, Computer science, Data mining, Machine learning, NLP, nVidia, nVidia GeForce GTX 660

October 3, 2015 by hgpu

Fast Algorithms for Convolutional Neural Networks

Andrew Lavin

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Tags: Algorithms, CNN, Computer science, Deep learning, FFT, Image recognition, Machine learning, Neural networks

October 3, 2015 by hgpu

Brute-Force k-Nearest Neighbors Search on the GPU

Shengren Li, Nina Amenta

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Tags: Computer science, CUDA, Matrix multiplication, Nearest neighbour, nVidia, nVidia GeForce GTX 460, Sorting

October 3, 2015 by hgpu

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

Ang Li, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Package, Sparse direct solvers, Tesla K20

September 30, 2015 by hgpu

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

Mujeeb B Jimoh

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan, Security, Thesis

September 29, 2015 by hgpu

The Dynamical Kernel Scheduler – Part 1

Andreas Adelmann, Uldis Locans, Andreas Suter

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Tags: Algorithms, Computational Physics, CUDA, FFT, Intel Xeon Phi, Monte Carlo simulation, nVidia, OpenCL, OpenMP, Physics

September 29, 2015 by hgpu

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

Applications

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

CVC: The Contourlet Video Compression algorithm for real-time applications

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

Parallel Graph Algorithms on the Xeon Phi Coprocessor

Optimizing GPU-accelerated Group-By and Aggregation

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Tuned and GPU-accelerated parallel data mining from comparable corpora

Fast Algorithms for Convolutional Neural Networks

Brute-Force k-Nearest Neighbors Search on the GPU

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

The Dynamical Kernel Scheduler – Part 1

Recent source codes

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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