high performance computing on graphics processing units: hgpu.org

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Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

Tim Idzenga, Evghenii Gaburov, Willem Vermin, Jan Menssen, Chris L. de Korte

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Tags: Algorithms, CUDA, Image processing, nVidia, OpenMP, Package, Tesla K20, Ultrasound

February 8, 2014 by hgpu

State-Based Gauss-Seidel Framework for Real-time 2D Ultrasound Image Sequence Denoising on GPUs

Banpot Dolwithayakul, Chantana Chantrapornchai, Noppadol Chumchob

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GTX 480, OpenMP, Ultrasound

February 8, 2014 by hgpu

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Ji-Hoon Kang, Syung-Og An, Shin-Jin Kang, Seok-Hun Kim, SooKyun Kim

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Tags: 3D Graphics and Realism, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTX 285, OpenGL, Raytracing, Rendering

February 8, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

D.P. Playne, K.A. Hawick, M.G.B. Johnson

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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, Tesla K20

February 8, 2014 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 2014 by hgpu

Depth Map Based Superresolution Method in 3D Reconstruction

Gabriel Bernardino

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Tesla C1060, Thesis

February 5, 2014 by hgpu

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Ali Maroosi, Ravie Chandren Muniyandi, Elankovan A. Sundararajan, Abdullah Mohd Zin

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Tags: Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce GTX 480

February 5, 2014 by hgpu

Bone structure analysis on multiple GPGPUs

Peter Arbenz, Cyril Flaig, Daniel Kellenberger

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Tags: CUDA, Medicine, nVidia, Tesla K20

February 5, 2014 by hgpu

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Angeles Navarro, Antonio Vilches, Francisco Corbera, Rafael Asenjo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla S2050

February 4, 2014 by hgpu

Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA

Dariusz Mrozek, Milosz Brozek, Bozena Malysiak-Mrozek

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 560 Ti, Package, Tesla C1060

February 4, 2014 by hgpu

Fast Acceleration of 2D Wave Propagation Simulations Using Modern Computational Accelerators

Wei Wang, Lifan Xu, John Cavazos, Howie H. Huang, Matthew Kay

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, OpenACC, OpenCL, Optimization, Package

February 4, 2014 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

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

State-Based Gauss-Seidel Framework for Real-time 2D Ultrasound Image Sequence Denoising on GPUs

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Developmental Directions in Parallel Accelerators

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Depth Map Based Superresolution Method in 3D Reconstruction

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Bone structure analysis on multiple GPGPUs

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA

Fast Acceleration of 2D Wave Propagation Simulations Using Modern Computational Accelerators

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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