high performance computing on graphics processing units: hgpu.org

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Parallel Neural Network Training with OpenCL

Nenad Krpan, Domagoj Jakobovic

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Tags: Algorithms, Computer science, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTS 250, OpenCL, Optimization

June 23, 2012 by hgpu

Massively Parallel Neural Encoding and Decoding of Visual Stimuli

Aurel A. Lazar, Yiyin Zhou

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Neural networks, nVidia, Tesla M2050

May 29, 2012 by hgpu

Parallel Approach for Time Series Analysis with General Regression Neural Networks

J.C. Cuevas-Tello, R.A. Gonzalez-Grimaldo, O. Rodriguez-Gonzalez, H.G. Perez-Gonzalez, O. Vital-Ochoa

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Tags: Algorithms, Astrophysics, CUDA, MPI, Neural networks, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, Physics

May 14, 2012 by hgpu

Implementation of Massive Artificial Neural Networks with CUDA

Domen Verber

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 595, Tesla C1060

April 17, 2012 by hgpu

Accelerating large-scale simulations of cortical neuronal network development

Fumitaka Kawasaki, Michael Stiber

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Tags: CNN, Computer science, CUDA, Neural networks, Neuroscience, nVidia, Tesla C1060

March 23, 2012 by hgpu

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Jun Igarashi, Osamu Shouno, Tomoki Fukai, Hiroshi Tsujino

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 280, Visualization

February 9, 2012 by hgpu

Accelerating Outlier Detection with Uncertain Data using Graphics Processors

Takazumi Matsumoto, Edward Hung

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Tags: Cluster analysis, Computer science, Data mining, Neural networks, nVidia, nVidia GeForce GT 440, OpenCL, Probability

February 5, 2012 by hgpu

Efficient Computation of SOM for Outage Database

Petr Gajdos, Michal Kratky, David Bednar, Radim Baca, Radomir Gono, Jiri Walder

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Tags: Computer science, CUDA, Databases, Neural networks, nVidia, Self-organizing map, Tesla C1060, Visualization

February 5, 2012 by hgpu

Experiences in Data-Parallel Simulation and Analysis of Complex Systems with Irregular Graph Structures

Arno Leist

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Tags: Algorithms, Cell processor, Computer science, CUDA, Graph theory, Neural networks, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, Phase transition, Thesis

January 23, 2012 by hgpu

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

Hazel McKendrick

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Tags: CUDA, High Energy Physics - Experiment, Machine learning, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050, Thesis

January 23, 2012 by hgpu

GPU-based implementation of a cerebellar spiking network model for realtime robot control

Tadashi Yamazaki, Jun Igarashi

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580

January 17, 2012 by hgpu

Parallel Implementations of Hopfield Neural Networks On GPU

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 480, Optimization

January 13, 2012 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

Parallel Neural Network Training with OpenCL

Massively Parallel Neural Encoding and Decoding of Visual Stimuli

Parallel Approach for Time Series Analysis with General Regression Neural Networks

Implementation of Massive Artificial Neural Networks with CUDA

Accelerating large-scale simulations of cortical neuronal network development

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Accelerating Outlier Detection with Uncertain Data using Graphics Processors

Efficient Computation of SOM for Outage Database

Experiences in Data-Parallel Simulation and Analysis of Complex Systems with Irregular Graph Structures

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

GPU-based implementation of a cerebellar spiking network model for realtime robot control

Parallel Implementations of Hopfield Neural Networks On GPU

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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