scholarly journals IDEA OF ARCHITECTURE OF A MULTIPROCESSOR COMPUTER M-10 AND THEIR IMPLEMENTATION IN MODERN COMPUTING PLATFORMS (IN SMALL FORMS)

2019 ◽  
pp. 28-31
Author(s):  
E. V. Glivenko ◽  
S. А. Sorokin ◽  
G. N. Petrovа
Keyword(s):  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Multiprocessor Computer ◽  
M 10 ◽  
Operator Type ◽  
Computing Platforms ◽  
Main Ideas ◽  
New Generation ◽  
Performance Computing

The article is devoted to the design of high‑performance computing devices for parallel processing of information. The problem of  increasing the productivity of computing facilities by one or several orders of magnitude is considered on the example of the high‑ performance electronic computer M‑10, which was created in the 1970s at the NIIVK. If in a conventional computer, the method  of processing numbers is given by commands, then in M‑10, the methods for processing a function were specified by operators  taken from functional analysis. At the same time, the possibility of parallel processing of an entire information line appeared. Such  systems began to be called «functional operator type machines». The main ideas presented in the article may be of interest to  developers of specialized machines of the new generation, as well as engineers involved in the creation of high‑performance  computing devices using technologies of computing platforms.

scholarly journals Editorial: Linking experimental and computational connectomics

2019 ◽  
Vol 3 (4) ◽  
pp. 902-904
Author(s):  
Alexander Peyser ◽  
Sandra Diaz Pier ◽  
Wouter Klijn ◽  
Abigail Morrison ◽  
Jochen Triesch
Keyword(s):  
High Performance Computing ◽  
In Silico ◽  
High Performance ◽  
Large Scale ◽  
Generative Models ◽  
Anatomical Structure ◽  
Global Function ◽  
Theoretical Neuroscience ◽  
New Generation ◽  
Performance Computing

Large-scale in silico experimentation depends on the generation of connectomes beyond available anatomical structure. We suggest that linking research across the fields of experimental connectomics, theoretical neuroscience, and high-performance computing can enable a new generation of models bridging the gap between biophysical detail and global function. This Focus Feature on ”Linking Experimental and Computational Connectomics” aims to bring together some examples from these domains as a step toward the development of more comprehensive generative models of multiscale connectomes.

Comparing High-Performance Computing Techniques for Modeling Structural Impact on Battery Cells

2014 ◽  
Author(s):  
Mehdi Gilaki ◽  
Ilya Avdeev
Keyword(s):  
Finite Element ◽  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Strain Curve ◽  
Thin Layers ◽  
Massively Parallel ◽  
Structural Impact ◽  
Performance Computing ◽  
Battery Cells

In this study, we have investigated feasibility of using commercial explicit finite element code LS-DYNA on massively parallel super-computing cluster for accurate modeling of structural impact on battery cells. Physical and numerical lateral impact tests have been conducted on cylindrical cells using a flat rigid drop cart in a custom-built drop test apparatus. The main component of cylindrical cell, jellyroll, is a layered spiral structure which consists of thin layers of electrodes and separator. Two numerical approaches were considered: (1) homogenized model of the cell and (2) heterogeneous (full) 3-D cell model. In the first approach, the jellyroll was considered as a homogeneous material with an effective stress-strain curve obtained through experiments. In the second model, individual layers of anode, cathode and separator were accounted for in the model, leading to extremely complex and computationally expensive finite element model. To overcome limitations of desktop computers, high-performance computing (HPC) techniques on a HPC cluster were needed in order to get the results of transient simulations in a reasonable solution time. We have compared two HPC methods used for this model is shared memory parallel processing (SMP) and massively parallel processing (MPP). Both the homogeneous and the heterogeneous models were considered for parallel simulations utilizing different number of computational nodes and cores and the performance of these models was compared. This work brings us one step closer to accurate modeling of structural impact on the entire battery pack that consists of thousands of cells.

Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms

2015 ◽  
Vol 11 ◽  
pp. 69-81 ◽  
Author(s):  
Emad Elsebakhi ◽  
Frank Lee ◽  
Eric Schendel ◽  
Anwar Haque ◽  
Nagarajan Kathireason ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Functional Networks ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals Investigating methods of supporting dynamically linked executables on high performance computing platforms.

2009 ◽  
Author(s):  
Suzanne Marie Kelly ◽  
James H., III Laros ◽  
Kevin Thomas Tauke Pedretti ◽  
Michael J. Levenhagen
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Platforms ◽  
Performance Computing
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