high performance computing on graphics processing units: hgpu.org

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Design and optimization of DBSCAN Algorithm based on CUDA

Bingchen Wang, Chenglong Zhang, Lei Song, Lianhe Zhao, Yu Dou, Zihao Yu

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K10

June 10, 2015 by hgpu

Cryptanalysis of the McEliece Cryptosystem on GPGPUs

Louis Fogel, Hnin Pwint Phyu

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Tags: Computer science, CUDA, nVidia, Security, Tesla K20, Thesis

June 8, 2015 by hgpu

Bi-directional Path Tracing on GPU

Vilem Otte

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 720 M, nVidia GeForce GTX 580, Package, Raytracing, Rendering, Thesis

June 8, 2015 by hgpu

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla K40

June 7, 2015 by hgpu

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Avadhesh Pratap Singh, Dhirendra Pratap Singh

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, Sequence alignment, Tesla C2075

June 7, 2015 by hgpu

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

Michael Vollmer, Bo Joel Svensson, Eric Holk, Ryan Newton

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Tags: Computer science, CUDA, nVidia, Performance, Search strategies, Tesla K40

June 7, 2015 by hgpu

The implementation and optimization of Bitonic sort algorithm based on CUDA

Qi Mu, Liqing Cui, Yufei Song

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Tags: Algorithms, Computer science, CUDA, nVidia, Sorting

June 7, 2015 by hgpu

Accelerated Nodal Discontinuous Galerkin Simulations for Reverse Time Migration with Large Clusters

Axel Modave, Amik St-Cyr, Wim A. Mulder, Tim Warburton

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, CUDA, Geophysics, MPI, nVidia, nVidia GeForce GTX 980, OpenCL, Physics, Tesla K20, Tesla K40

June 5, 2015 by hgpu

Blocks and Fuel: Frameworks for deep learning

Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, MPI, Neural networks, nVidia, Package, Python, Symbolic Computation, Visualization

June 5, 2015 by hgpu

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

Pall Szilard, Mark James Abraham, Carsten Kutzner, Berk Hess, Erik Lindahl

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Tags: Algorithms, Biomolecules, Computer science, CUDA, Heterogeneous systems, Molecular dynamics, Molecular simulation, MPI, nVidia, Tesla C2070, Tesla K20

June 5, 2015 by hgpu

Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators

Fredrik Andersson, Marcus Carlsson, Viktor V. Nikitin

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Tags: Algorithms, CUDA, FFT, Image processing, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 770, Radon transform

June 5, 2015 by hgpu

Genetically Improved BarraCUDA

W. B. Langdon, Brian Yee Hong Lam

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Tags: Algorithms, Benchmarking, Biology, CUDA, Genomics, nVidia, nVidia GeForce GT 730, Package, Tesla K20, Tesla K40, Tesla K80

June 1, 2015 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Design and optimization of DBSCAN Algorithm based on CUDA

Cryptanalysis of the McEliece Cryptosystem on GPGPUs

Bi-directional Path Tracing on GPU

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

The implementation and optimization of Bitonic sort algorithm based on CUDA

Accelerated Nodal Discontinuous Galerkin Simulations for Reverse Time Migration with Large Clusters

Blocks and Fuel: Frameworks for deep learning

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators

Genetically Improved BarraCUDA

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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