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Face Recognition: A Tutorial on Computational Aspects

Alexander Alling, Nathaniel Powers, Tolga Soyata

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 760, nVidia GeForce GTX 780, Tutorial

January 10, 2015 by hgpu

Dynamic Feature-Adaptive Subdivision

H. Schafer, J. Raab, B. Keinert, M. Meyer, M. Stamminger, M. Niessner

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Tags: Computational Geometry, Computer science, DirectX, Interactive application, nVidia, nVidia GeForce GTX 980, Rendering

January 10, 2015 by hgpu

Digital Signal Processing using Stream High Performance Computing: A 512-input Broadband Correlator for Radio Astronomy

J. Kocz, L.J Greenhill, B.R. Barsdell, D. Price, G. Bernardi, S. Bourke, M.A. Clark, J. Craig, M. Dexter, J. Dowell, T. Eftekhari, S. Ellingson, G. Hallinan, J. Hartman, A. Jameson, D. MacMahon, G. Taylor, F. Schinzel, D. Werthimer

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Tags: Astrophysics, CUDA, FPGA, GPU cluster, Heterogeneous systems, Instrumentation and Methods for Astrophysics, nVidia, Package, Signal processing, Tesla K20

January 10, 2015 by hgpu

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Puya Memarzia, Farshad Khunjush

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Tags: Benchmarking, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 570

January 10, 2015 by hgpu

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Package

January 10, 2015 by hgpu

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Geoffrey Ndu, Mikel Lujan, Javier Navaridas

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Tags: Benchmarking, Computer science, FPGA, OpenCL, Package

January 8, 2015 by hgpu

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

Albert Haque

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Tags: CUDA, Machine learning, Medicine, Neural networks, nVidia, nVidia GeForce GTX 750 Ti, Package

January 8, 2015 by hgpu

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Leonard Truong, Chick Markley, Armando Fox

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Tags: Code generation, Computer science, OpenCL, Python

January 8, 2015 by hgpu

Simulating and Visualizing Real-Time Crowds on GPU Clusters

Benjamin Hernandez, Hugo Perez, Isaac Rudomin, Sergio Ruiz, Oriam DeGyves, Leonel Toledo

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Rendering, Tesla M2090, Visualization

January 8, 2015 by hgpu

Performance and Power Comparisons Between Nvidia and ATI GPUs

Ying Zhang, Lu Peng, Bin Li, Jih-Kwon Peir, Jianmin Chen

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

January 8, 2015 by hgpu

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Sparsh Mittal

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Tags: Embedded high-performance computing, Energy-efficient computing, FPGA, GPU, Power-efficient computing

January 7, 2015 by sparsh0mittal

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Jonathan Wong, Ellen Kuhl, Eric Darve

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

January 5, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Face Recognition: A Tutorial on Computational Aspects

Dynamic Feature-Adaptive Subdivision

Digital Signal Processing using Stream High Performance Computing: A 512-input Broadband Correlator for Radio Astronomy

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Image Super-Resolution Using Deep Convolutional Networks

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Simulating and Visualizing Real-Time Crowds on GPU Clusters

Performance and Power Comparisons Between Nvidia and ATI GPUs

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

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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