high performance computing on graphics processing units: hgpu.org

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Partial Parallelization of the Successive Projections Algorithm using Compute Unified Device Architecture

Lauro Cassio Martins de Paula, Anderson da Silva Soares, Telma Woerle de Lima, Wellington Santos Martins, Arlindo Rodrigues Galvao Filho, Clarimar Jose Coelho

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 550 Ti

December 8, 2013 by hgpu

A GPU-based Multiresolution Pipeline for Compressed Volume Rendering

Julian Lamas-Rodriguez, Francisco Arguello, Dora B. Heras

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Rendering, Visualization

December 8, 2013 by hgpu

Waste Not… Efficient Co-Processing of Relational Data

Holger Pirk, Stefan Manegold, Martin Kersten

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Tags: Algorithms, Computer science, Databases, nVidia, nVidia GeForce GTX 680, OpenCL

December 8, 2013 by hgpu

GPU-Accelerated Crack Path Computation Based on a Phase Field Approach for Brittle Fracture

Alexander Schluter, Adrian Willenbucher, Charlotte Kuhn

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Tags: Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Tesla C2075

December 8, 2013 by hgpu

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

Nam-Luc Tran, Quentin Dugauthier, Sabri Skhiri

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Tags: Clustering, Computer science, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, Tesla K20

December 6, 2013 by hgpu

A Quantitative Comparison of Emulated Shared Memory Architectures to Current Multicore CPUs and GPUs

Erik Hansson, Erik Alnervik, Christoph Kessler, Martti Forsell

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Tags: Computer science, CUDA, nVidia, Performance, Tesla M2050

December 6, 2013 by hgpu

Parallel tree-ensemble algorithms for GPUs using CUDA

Karl Jansson, Hakan Sundell, Henrik Bostrom

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Tags: Algorithms, Computer science, CUDA, nVidia, Tesla K20

December 6, 2013 by hgpu

Speeding up the small progress measures algorithm for parity games using the GPU

P.J.A. Bootsma

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GT 555 M, nVidia GeForce GTX 660 Ti, Thesis

December 6, 2013 by hgpu

Similarity Search in Metric Spaces on Parallel multi-core and multi-GPU Platforms

Ricardo Javier Barrientos Rojel

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, Package, Search strategies, Tesla C1060, Tesla M2070, Thesis

December 6, 2013 by hgpu

A Fast Implementation of Parallel Discrete-Event Simulation on GPGPU

Janche Sang, Che-Rung Lee, Vernon Rego, Chung-Ta King

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Tags: Algorithms, Computer science, CUDA, nVidia, Tesla M2070, Thrust

December 6, 2013 by hgpu

Computing OpenSURF on OpenCL and General Purpose GPU

Wanglong Yan, Xiaohua Shi, Xin Yan, Lina Wang

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Tags: Algorithms, ATI, ATI Radeon HD 6850, Computer science, Computer vision, Memory model, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 660, OpenCL

December 4, 2013 by hgpu

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

Kazumasa Ikeuchi, Janaka Wijekoon, Shinichi Ishida, Hiroaki Nishi

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, Software router, String matching

December 4, 2013 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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Partial Parallelization of the Successive Projections Algorithm using Compute Unified Device Architecture

A GPU-based Multiresolution Pipeline for Compressed Volume Rendering

Waste Not… Efficient Co-Processing of Relational Data

GPU-Accelerated Crack Path Computation Based on a Phase Field Approach for Brittle Fracture

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

A Quantitative Comparison of Emulated Shared Memory Architectures to Current Multicore CPUs and GPUs

Parallel tree-ensemble algorithms for GPUs using CUDA

Speeding up the small progress measures algorithm for parity games using the GPU

Similarity Search in Metric Spaces on Parallel multi-core and multi-GPU Platforms

A Fast Implementation of Parallel Discrete-Event Simulation on GPGPU

Computing OpenSURF on OpenCL and General Purpose GPU

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

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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