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A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Stephen Won

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Tags: Computer vision, CUDA, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, SIFT, Signal processing, Thesis

February 12, 2013 by hgpu

Parallel SIFT-detector implementation for images matching

Anton I. Vasilyev, Andrey A. Boguslavskiy, Sergey M. Sokolov

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Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 260, SIFT

January 24, 2012 by hgpu

BFROST: Binary Features from Robust Orientation Segment Tests accelerated on the GPU

Jaco Cronje

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Pattern recognition, SIFT

January 5, 2012 by hgpu

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Arturo Ribes, Senshan Ji, Arnau Ramisa, Ramon Lopez De Mantaras

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, SIFT

December 11, 2011 by hgpu

GMM based Fisher vector calculation on GPGPU

Erik Bodzsar, Balint Daroczy, Istvan Petras, Andras A. Benczur

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Tags: Clustering, CUDA, Databases, Image processing, nVidia, nVidia GeForce GTX 285, Package, Performance, SIFT

December 5, 2011 by hgpu

The MOPED framework: Object recognition and pose estimation for manipulation

The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

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Tags: Algorithms, Clustering, Computer science, Computer vision, nVidia, nVidia GeForce GTX 260, Package, SIFT

November 18, 2011 by hgpu

EFFEX: an embedded processor for computer vision based feature extraction

Jason Clemons, Andrew Jones, Robert Perricone, Silvio Savarese, Todd Austin

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Tags: Algorithms, Computer science, Computer vision, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 210, nVidia GeForce GTX 260, SIFT

September 20, 2011 by hgpu

Adaptive, real-time visual simultaneous localization and mapping

Brian Clipp, Christopher Zach, Jongwoo Lim, Jan-Michael Frahm, Marc Pollefeys

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Tags: Computer science, Computer vision, CUDA, nVidia, Optical flow, Optimization, SIFT, Video tracking

August 1, 2011 by hgpu

Overview of approaches for accelerating scale invariant feature detection algorithm

Jing Zhang, Hongshi Sang, Xubang Shen

Tags: Computer science, Computer vision, FPGA, SIFT

June 30, 2011 by hgpu

Towards real time vision based UUV navigation using GPU technology

J. Horgan, F. Flannery, D. Toal

Tags: Computer vision, CUDA, Image processing, nVidia, SIFT

May 5, 2011 by hgpu

K-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching

Vincent Garcia, Eric Debreuve, Frank Nielsen, Michel Barlaud

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Tags: Computer vision, CUBLAS, CUDA, Image processing, Nearest neighbour, nVidia, nVidia Quadro FX 3700 M, Package, SIFT

May 5, 2011 by hgpu

Implicit Feature-Based Alignment System for Radiotherapy

Ryoichi Yamakoshi, Kousuke Hirasawa, Haruhisa Okuda, Hiroshi Kage, Kazuhiko Sumi, Hidenobu Sakamoto, Yuri Ivanov, Toshihiro Yanou, Daisaku Suga, Masao Murakami

Tags: Computed tomography, CT, Medicine, Radiotherapy, SIFT, Tomography

April 14, 2011 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

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