A Dense, Massively Parallel Architecture

In this paper we discuss the use of the Cellular Automata (CA) computational model in computer vision applications on massively parallel architectures. Motivations and guidelines of this approach to low-level vision in the frame of the PROMETHEUS project are discussed. The hard real-time requirement of actual application can be only satisfied using an ad hoc VLSI massively parallel architecture (PAPRICA). The hardware solutions and the specific algorithms can be efficiently verified and tested only using, as a simulator, a general purpose machine with a parent architecture (CM-2). An example of application related to feature extraction is discussed.

Download Full-text

FPGA Blokus Duo Solver using a massively parallel architecture

2013 International Conference on Field-Programmable Technology (FPT) ◽

10.1109/fpt.2013.6718426 ◽

2013 ◽

Cited By ~ 7

Author(s):

Takashi Yoza ◽

Retsu Moriwaki ◽

Yuki Torigai ◽

Yuki Kamikubo ◽

Takayuki Kubota ◽

...

Keyword(s):

Parallel Architecture ◽

Massively Parallel

Download Full-text

A massively parallel architecture for a self-organizing neural pattern recognition machine

Computer Vision Graphics and Image Processing ◽

10.1016/0734-189x(86)90094-0 ◽

1986 ◽

Vol 36 (2-3) ◽

pp. 396 ◽

Cited By ~ 5

Author(s):

Gail A Carpenter ◽

Stephen Grossberg

Keyword(s):

Pattern Recognition ◽

Parallel Architecture ◽

Massively Parallel ◽

Self Organizing

Download Full-text

Dynamic PET Reconstruction on the GPU

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.11739 ◽

2018 ◽

Vol 62 (4) ◽

pp. 134-143 ◽

Cited By ~ 4

Author(s):

László Szirmay-Kalos ◽

Ágota Kacsó ◽

Milán Magdics ◽

Balázs Tóth

Keyword(s):

Parallel Architecture ◽

Concentration Function ◽

Space Time ◽

Massively Parallel ◽

Graphics Processors ◽

Gamma Photon ◽

Likelihood Principle ◽

Positron Emission ◽

Spatio Temporal ◽

Pet Reconstruction

Dynamic Positron Emission Tomography (PET) reconstructs the space-time concentration function of a radiotracer by observing the detector hits of gamma-photon pairs born during the radiotracer decay. The computation is based on the maximum likelihood principle, i.e. we look for the space-time function that maximizes the probability of the actual measurements. The number of finite elements representing the spatio-temporal concentration and the number of events detected by the tomograph may be higher than a billion, thus the reconstruction requires supercomputer performance. The enormous computational burden can be handled by graphics processors (GPU) if the algorithm is decomposed to parallel, independent threads, and the storage requirements are kept under control. This paper proposes a scalable dynamic reconstruction system where the algorithm is decomposed to phases where each phase is efficiently mapped onto the massively parallel architecture of the GPU.

Download Full-text

Massively parallel Monte Carlo simulations of images and analytical data for Electron Microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100172280 ◽

1994 ◽

Vol 52 ◽

pp. 910-911

Author(s):

A. D. Romig ◽

J. R. Michael ◽

S. J. Plimpton

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Analytical Data ◽

Parallel Architecture ◽

Thin Foil ◽

Massively Parallel ◽

Electron Trajectory ◽

Monte Carlo Algorithms ◽

Computational Speed ◽

Parallel Supercomputers

Monte Carlo electron trajectory simulations have been adapted to run on massively parallel supercomputers. An nCUBE2 parallel supercomputer with 1024 processors has been used in these studies. The advantage of the parallel architecture is the great increase in computational speed and the fact that few changes in the standard serial Monte Carlo algorithms are required. The temporal performance of the massively parallel Monte Carlo electron trajectory simulation run on 1024 nodes has been compared with Monte Carlo codes run on other types of supercomputers (CRAY-YMP). It was found to be as much as 100 times faster than the CRAY-YMP and over 2000 times faster than a VAX 785. This increase in computational speed allows the exploration of problems, in particular those involving small probability events, which are not normally amenable to solution by traditional serial Monte Carlo simulations due tothe time intensive nature of the calculations. For example, the calculation of 1,000,000 electrons at 100 kV through a thin foil takes about 6 seconds on the nCUBE.

Download Full-text