Numerical Algorithms for Modern Parallel Computer Architectures

1988 ◽  
Keyword(s):  
Numerical Algorithms ◽  
Computer Architectures ◽  
Parallel Computer

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

A simulation method for parallel computer architectures

1989 ◽  
Vol 25 (1-5) ◽  
pp. 209-212 ◽  
Author(s):  
E. Zehendner ◽  
Th. Ungerer
Keyword(s):  
Simulation Method ◽  
Computer Architectures ◽  
Parallel Computer

Optsim: A Tool for the Design of Optical Parallel Computer Architectures

1997 ◽  
pp. 553-561
Author(s):  
N. Langloh ◽  
V. Christopoulos ◽  
S. Van Langendonck ◽  
J. Cornelis ◽  
R. Vounckx ◽  
...  
Keyword(s):  
Computer Architectures ◽  
Parallel Computer

Parallelism in knowledge-based machines

1989 ◽  
Vol 4 (1) ◽  
pp. 53-71
Author(s):  
Apostolos N. Refenes
Keyword(s):  
Expert Systems ◽  
Programming Languages ◽  
Parallel Machine ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Rule Based ◽  
Boltzmann Machines ◽  
Knowledge Based ◽  
Parallel Tasks ◽  
Language Classes

AbstractThe application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.

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