high performance computing on graphics processing units: hgpu.org

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Exploiting Data Parallelism in GPUs

Yongpeng Zhang

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Tags: Clustering, Computer science, CUDA, Data parallelism, GPU cluster, MPI, nVidia, nVidia GeForce GTX 280, Thesis

October 14, 2012 by hgpu

Lattice Group Models: GPU Acceleration and Numerics

Stefan Brechtken

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Tags: Boltzmann equation, CUDA, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX 580, Optimization

October 14, 2012 by hgpu

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

Scalable GPU Acceleration of B-Spline Signal Processing Operations

Alexander Karantza

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Tags: CUDA, nVidia, Signal processing, Tesla C2070, Thesis

October 13, 2012 by hgpu

Fast Parallel Implementation of Fractional Packing and Covering Linear Programs

Slobodan Jelic, Soren Laue, Domagoj Matijevic, Patrick Wijerama

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Tags: Algorithms, CUDA, Mathematics, nVidia, Tesla C2070

October 13, 2012 by hgpu

.NET High Performance Computing

Hsuan-Hsiu Ou

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GTX 480, Task scheduling, Thesis

October 13, 2012 by hgpu

FPGA-GPU-CPU Heterogenous Architecture for Real-time Cardiac Physiological Optical Mapping

Pingfan Meng, Matthew Jacobsen, Ryan Kastner

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Tags: Algorithms, CUDA, FPGA, Image processing, Medicine, nVidia, nVidia GeForce GTX 590

October 13, 2012 by hgpu

Parallel H-Tree Based Data Cubing on Graphics Processors

Baoyuan Wang, Yizhou Yu

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

October 13, 2012 by hgpu

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Marek Palkowski, Wlodzimierz Bielecki

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Tags: Computer science, CUDA, nVidia, Performance, Tesla S1070

October 13, 2012 by hgpu

Mesh Independent Loop Fusion for Unstructured Mesh Applications

Carlo Bertolli, Adam Betts, Gihan R. Mudalige, Paul H.J. Kelly, Michael B. Giles

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Tags: Computer science, CUDA, nVidia, Programming techniques, Tesla M2050

October 13, 2012 by hgpu

GPU-Based Local-Dimming for Power Efficient Imaging

Chihao Xu, Michael Krause, Jens Kruger

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Tags: Algorithms, Computer science, CUDA, Image generation, nVidia, nVidia GeForce GTX 555 M

October 13, 2012 by hgpu

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Peng Di, Ding Ye, Yu Su, Yulei Sui, Jingling Xue

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C1060, Tesla C2050

October 13, 2012 by hgpu

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Exploiting Data Parallelism in GPUs

Lattice Group Models: GPU Acceleration and Numerics

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Scalable GPU Acceleration of B-Spline Signal Processing Operations

Fast Parallel Implementation of Fractional Packing and Covering Linear Programs

.NET High Performance Computing

FPGA-GPU-CPU Heterogenous Architecture for Real-time Cardiac Physiological Optical Mapping

Parallel H-Tree Based Data Cubing on Graphics Processors

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Mesh Independent Loop Fusion for Unstructured Mesh Applications

GPU-Based Local-Dimming for Power Efficient Imaging

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Recent source codes

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

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

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