Parallel Processing in Network Processor Architectures (Parallelverarbeitung in Netzwerkprozessorarchitekturen)

2005 ◽  
Vol 47 (3) ◽  
Author(s):  
Rainer Hagenau ◽  
Carsten Albrecht ◽  
Erik Maehle ◽  
Andreas C. Döring
Keyword(s):  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Network Processor ◽  
Processor Architectures ◽  
Parallel Data ◽  
Data Plane ◽  
Parallel Network ◽  
Basic Approaches ◽  
Performance Computing

SummuryParallel processing is well established in high-performance computing. Currently, network processors as new emerging, special-purpose processors are targeted at the exploitation of parallelism to meet the requirements in data-plane processing with wire-speed. The achievable level of parallelism is determined by decisions in the architecture design and by the characteristics of the data-plane applications executed. We discuss two basic approaches in parallel processing, namely pipelining and concurrency, which establish basic models for parallel network processor organization. The features and constraints of these models are studied. Using this background some existing network processor architectures are reviewed and characterized regarding their potential in parallel data-plane processing.

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.

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.

High Performance Computing Benchmark Tool for Parallel Processing of Large Models

2010 ◽  
Author(s):  
Sendil Rangaswamy ◽  
Chris Ellis ◽  
Sergio Tafur ◽  
Ravi Palaniappan ◽  
Seetha Raghavan ◽  
...  
Keyword(s):  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Performance Computing

Research and Application of Parallel Computing under Linux

2013 ◽  
Vol 756-759 ◽  
pp. 2825-2828
Author(s):  
Xue Chun Wang ◽  
Quan Lu Zheng
Keyword(s):  
Parallel Computing ◽  
Parallel Processing ◽  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Performance Testing ◽  
Divide And Conquer ◽  
Parallel Computer ◽  
Super Computing ◽  
Performance Computing

Parallel computing is in parallel computer system for parallel processing of data and information, often also known as the high performance computing or super computing. The content of parallel computing were introduced, the realization of parallel computing and MPI parallel programming under Linux environment were described. The parallel algorithm based on divide and conquer method to solve rectangle placemen problem was designed and implemented with two processors. Finally, Through the performance testing and comparison, we verified the efficiency of parallel computing.

Abstractions and Middleware for Petascale Computing and Beyond

2012 ◽  
pp. 161-178
Author(s):  
Ivo F. Sbalzarini
Keyword(s):  
Numerical Simulations ◽  
High Performance Computing ◽  
Data Structures ◽  
High Performance ◽  
Source Code ◽  
Limiting Factors ◽  
Communication Overhead ◽  
Parallel Data ◽  
Petascale Computing ◽  
Performance Computing

As high-performance computing moves to the petascale and beyond, a number of algorithmic and software challenges need to be addressed. This paper reviews the main performance-limiting factors in today’s high-performance computing software and outlines a possible new programming paradigm to address them. The proposed paradigm is based on abstract parallel data structures and operations that encapsulate much of the complexity of an application, but still make communication overhead explicit. The authors argue that all numerical simulations can be formulated in terms of the presented abstractions, which thus define an abstract semantic specification language for parallel numerical simulations. Simulations defined in this language can automatically be translated to source code containing the appropriate calls to a middleware that implements the underlying abstractions. Finally, the structure and functionality of such a middleware are outlined while demonstrating its feasibility on the example of the parallel particle-mesh library (PPM).

Abstractions and Middleware for Petascale Computing and Beyond

2010 ◽  
Vol 1 (2) ◽  
pp. 40-56 ◽  
Author(s):  
Ivo F. Sbalzarini
Keyword(s):  
Numerical Simulations ◽  
High Performance Computing ◽  
High Performance ◽  
Source Code ◽  
Limiting Factors ◽  
Communication Overhead ◽  
Programming Paradigm ◽  
Parallel Data ◽  
Petascale Computing ◽  
Performance Computing

As high-performance computing moves to the petascale and beyond, a number of algorithmic and software challenges need to be addressed. This paper reviews the main performance-limiting factors in today’s high-performance computing software and outlines a possible new programming paradigm to address them. The proposed paradigm is based on abstract parallel data structures and operations that encapsulate much of the complexity of an application, but still make communication overhead explicit. The authors argue that all numerical simulations can be formulated in terms of the presented abstractions, which thus define an abstract semantic specification language for parallel numerical simulations. Simulations defined in this language can automatically be translated to source code containing the appropriate calls to a middleware that implements the underlying abstractions. Finally, the structure and functionality of such a middleware are outlined while demonstrating its feasibility on the example of the parallel particle-mesh library (PPM).

Abstractions and Middleware for Petascale Computing and Beyond

2012 ◽  
pp. 1998-2015
Author(s):  
Ivo F. Sbalzarini
Keyword(s):  
Numerical Simulations ◽  
High Performance Computing ◽  
Data Structures ◽  
High Performance ◽  
Source Code ◽  
Limiting Factors ◽  
Communication Overhead ◽  
Parallel Data ◽  
Petascale Computing ◽  
Performance Computing

As high-performance computing moves to the petascale and beyond, a number of algorithmic and software challenges need to be addressed. This paper reviews the main performance-limiting factors in today’s high-performance computing software and outlines a possible new programming paradigm to address them. The proposed paradigm is based on abstract parallel data structures and operations that encapsulate much of the complexity of an application, but still make communication overhead explicit. The authors argue that all numerical simulations can be formulated in terms of the presented abstractions, which thus define an abstract semantic specification language for parallel numerical simulations. Simulations defined in this language can automatically be translated to source code containing the appropriate calls to a middleware that implements the underlying abstractions. Finally, the structure and functionality of such a middleware are outlined while demonstrating its feasibility on the example of the parallel particle-mesh library (PPM).

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