3,790 research outputs found
GPU Acceleration of Image Convolution using Spatially-varying Kernel
Image subtraction in astronomy is a tool for transient object discovery such
as asteroids, extra-solar planets and supernovae. To match point spread
functions (PSFs) between images of the same field taken at different times a
convolution technique is used. Particularly suitable for large-scale images is
a computationally intensive spatially-varying kernel. The underlying algorithm
is inherently massively parallel due to unique kernel generation at every pixel
location. The spatially-varying kernel cannot be efficiently computed through
the Convolution Theorem, and thus does not lend itself to acceleration by Fast
Fourier Transform (FFT). This work presents results of accelerated
implementation of the spatially-varying kernel image convolution in multi-cores
with OpenMP and graphic processing units (GPUs). Typical speedups over ANSI-C
were a factor of 50 and a factor of 1000 over the initial IDL implementation,
demonstrating that the techniques are a practical and high impact path to
terabyte-per-night image pipelines and petascale processing.Comment: 4 pages. Accepted to IEEE-ICIP 201
GPU acceleration of time-domain fluorescence lifetime imaging
Fluorescence lifetime imaging microscopy (FLIM) plays a significant role in biological sciences, chemistry, and medical research. We propose a Graphic Processing Units (GPUs) based FLIM analysis tool suitable for high-speed and flexible time-domain FLIM applications. With a large number of parallel processors, GPUs can significantly speed up lifetime calculations compared to CPU-OpenMP (parallel computing with multiple CPU cores) based analysis. We demonstrate how to implement and optimize FLIM algorithms on GPUs for both iterative and non-iterative FLIM analysis algorithms. The implemented algorithms have been tested on both synthesized and experimental FLIM data. The results show that at the same precision the GPU analysis can be up to 24-fold faster than its CPU-OpenMP counterpart. This means that even for high precision but time-consuming iterative FLIM algorithms, GPUs enable fast or even real-time analysis
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Analysis of Parallel Montgomery Multiplication in CUDA
For a given level of security, elliptic curve cryptography (ECC) offers improved efficiency over classic public key implementations. Point multiplication is the most common operation in ECC and, consequently, any significant improvement in perfor- mance will likely require accelerating point multiplication. In ECC, the Montgomery algorithm is widely used for point multiplication. The primary purpose of this project is to implement and analyze a parallel implementation of the Montgomery algorithm as it is used in ECC. Specifically, the performance of CPU-based Montgomery multiplication and a GPU-based implementation in CUDA are compared
Real time video pipeline for computer vision using embedded GPUs, A
2016 Fall.Includes bibliographical references.This thesis presents case study confirming the feasibility of real time Computer Vision applications on embedded GPUs. Applications that depend on video processing, such as security surveillance, can benefit from applying optimizations common in scientific computing. This thesis demonstrates the benefit of applying such optimizations to real time Computer Vision applications on embedded GPUs. The primary contribution of this thesis is an optimized implementation of ViBe targeting NVIDIA's Jetson TK1. ViBe is a commonly used background subtraction algorithm. Optimizing a background subtraction algorithm accelerates the task of reducing the field of view to only interesting patches of the frames of the video. Placing portable hardware close to capturing devices in the surveillance system reduces bandwidth requirements and cost. The goals of the optimizations proposed for this algorithm are to 1) reduce memory traffic 2) overlap CPU and GPU usage 3) reduce kernel overhead. The optimized implementation of ViBe achieves a frame rate of almost 55 FPS beating the real time goal standard of 30 FPS for real time video. This is a small portion of the real-time window leaving processing time for additional algorithms like object recognition
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