Parallel Computer Workload Modeling with Markov Chains

Job Scheduling Strategies for Parallel Processing - Lecture Notes in Computer Science ◽

10.1007/11407522_3 ◽

2005 ◽

pp. 47-62 ◽

Cited By ~ 22

Author(s):

Baiyi Song ◽

Carsten Ernemann ◽

Ramin Yahyapour

Keyword(s):

Markov Chains ◽

Parallel Computer ◽

Computer Workload

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181026 ◽

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.

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