Modeling of Parallel Computers Based on Network of Computing

Hanuliak, Michal

doi:10.11648/j.ajnc.s.2014030501.14

Cited by 3 publications

(3 citation statements)

References 32 publications

(37 reference statements)

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“…To solve such coupled network of computing nodes appoints to couple network of queuing theory systems. We refer to it in another paper of this Special Issue [11]. The achieved results we can apply to performance modeling of multiprocessors or multicores) using as input parameter ρ = λ .E(t s ) / m (for m=1 we can model computing node with one processor) as follows…”

Section: Discussionmentioning

confidence: 99%

Modeling of Single Computing Nodes of Parallel Computers

Hanuliak¹,

Hanuliak²

2014

AJNC

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The paper describes analytical modeling for single computing nodes of parallel computers. At first the paper describes very shortly the developing steps of parallel computer architectures and then he summarized the basic concepts for performance evaluation. To illustrate theoretical evaluation concepts the paper considers in its experimental part the achieved results on concrete analyzed examples and their comparison. The suggested analytical models consider for single computing node based on processor or core and SMP modeling of own computer node´s activities and node´s communication channels of performed data communications within computing node queuing theory systems M/D/m or M/D/. In case of using SMP parallel system as node computer the suggested models consider for own node's activities M/M/m or M/D/m queuing theory systems. Although we are able to use other more complicated queuing theory systems we prefer modeling with mentioned models because achieved results for these models we can use in decomposed modeling of coupled computing nodes as network of workstations (NOW) or network of massive NOW modules (Grid). The achieved results of the developed analytical models we have compared with the results of tested computing nodes with other alternative evaluation method based on suitable benchmarks to verify developed analytical models. The developed analytical models could be used under various ranges of input analytical parameters, which influence the architecture of analyzed computing nodes which are interested for the praxis.

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Section: Discussionmentioning

confidence: 99%

Modeling of Single Computing Nodes of Parallel Computers

Hanuliak¹,

Hanuliak²

2014

AJNC

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“…10. These communication parameters t s , t w are constants for defined parallel computer [11]. Then the communication latency T(s, p) comm using communication complexity Z(s, p) comm and the defined communication parameters is given as follows These two defined functions limit performance of used parallel computer on defined NOW module of parallel computer.…”

Section: Communication Latenciesmentioning

confidence: 99%

Modeling of Communication Complexity in Parallel Computing

Hanuliak¹

2014

AJNC

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Parallel principles are the most effective way how to increase parallel computer performance and parallel algorithms (PA) too. Parallel using of more computing nodes (processors, cores), which have to cooperate each other in solving complex problems in a parallel way, opened imperative problem of modeling communication complexity so in symmetrical multiprocessors (SMP) based on motherboard as in other asynchronous parallel computers (computer networks, cluster etc.). In actually dominant parallel computers based on NOW and Grid (network of NOW networks) [31] there is necessary to model communication latency because it could be dominant at using massive (number of processors more than 100) parallel computers [17]. In this sense the paper is devoted to modeling of communication complexity in parallel computing (parallel computers and algorithms). At first the paper describes very shortly various used communication topologies and networks and then it summarized basic concepts for modeling of communication complexity and latency too. To illustrate the analyzed modeling concepts the paper considers in its experimental part the results for real analyzed examples of abstract square matrix and its possible decomposition models. These illustration examples we have chosen first due to wide matrix application in scientific and engineering fields and second from its typical exemplary representation for any other PA.

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“…Current trends also in high performance computing (HPC) are to use networks of workstations (NOW, SMP) as a cheaper alternative to traditionally used massively parallel multiprocessors or supercomputers and to profit from unifying of both mentioned disciplines [19]. The individual powerful computing nodes (workstations) could be so single personal computer (PC) as parallel computers based on modern SMP parallel computers implemented within computing node of parallel computer [13,15]. Based on such modular NOW modules there were are realized high integrated massive parallel computers named as Grid…”

mentioning

confidence: 99%

Complex Modeling of Matrix Parallel Algorithms

Hanuliak¹

2014

AJNC

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Abstract:Parallel principles are the most effective way how to increase performance in parallel computing (parallel computers and algorithms too). In this sense the paper is devoted to a complex performance evaluation of matrix parallel algorithms (MPA). At first the paper describes the typical matrix parallel algorithms and then it summarizes common properties of them to complex performance modeling of MPA. To complex performance analysis we are able to take into account all overheads influence performance of parallel algorithms (parallel computer architecture, parallel computation, communication etc.). To be le to analyze MPA in their abstract form we have defined needed decomposition models of MPA. For these decomposition strategies we derived analytical relation for defined complex performance criterions including isoefficiency functions, which allow us to predict performance although for hypothetical parallel computer. In its experimental part the paper considers the achieved results using defined complex performance criterions including issoefficiency function for performance prediction also for hypothetical future parallel computers. Such idea of common abstract analysis could be very useful in deriving complex performance criterions for groups of other similar parallel algorithms (PA) as for example numerical integration PA, optimization PA etc. Keywords: Trends in Parallel ComputingBasic common properties in parallel computing (parallel computers, parallel algorithms) computing, which are reaching continuous demands to performance acceleration, are as follows embedded parallel principles on various levels of technical (hardware) and program support means (software) [8] using of homogenous shared resources so in computing nodes of parallel computers (processors, cores, computers) as in parallel algorithms too [24] using of high speed communication networks reducing communication latency [39] increased client/server computing on symmetrical multiple processors or cores (SMP) trends to unified modeling of parallel computers (shared memory, distributed memory) and in parallel algorithms (shared memory, distributed memory, hybrid) continuous demands to increase mobility and data migration [23] the development of hardware neutral parallel programming language, such as Java, provides a virtual computational environment in which computing nodes of parallel computer appear to be homogenous continuous improvements in network technology and communication middleware in order to use shared parallel resources in unified manner (cloud computing, Internet computing). Current trends also in high performance computing (HPC) are to use networks of workstations (NOW, SMP) as a cheaper alternative to traditionally used massively parallel multiprocessors or supercomputers and to profit from unifying of both mentioned disciplines [19]. The individual powerful computing nodes (workstations) could be so single personal computer (PC) as parallel computers based on modern SMP parallel computers implemented within computing no...

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Modeling of Parallel Computers Based on Network of Computing

Cited by 3 publications

References 32 publications

Modeling of Single Computing Nodes of Parallel Computers

Modeling of Single Computing Nodes of Parallel Computers

Modeling of Communication Complexity in Parallel Computing

Complex Modeling of Matrix Parallel Algorithms

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