Advantages Of Parallel Computing Over Serial Computing Computer Science

Table of Content

In this paper, we ab initio discuss the advantages of parallel calculating over consecutive computer science. Nervous Networks has many advantages and so we decide upon the type of nervous web that needs to be used for the anticipation of the host burden of a system for a grid environment. We achieve better consequences in footings of low operating expense for different types of systems. We besides observe that the standard divergence and mean for these systems is reduced by 60 % and 70 % by utilizing nervous webs. The preparation and testing clip for the system is besides really less and nervous webs can be easy applied in a existent clip environment.


Traditionally, consecutive computer science is defined where there is merely a individual processor to execute all the calculations. So therefore the instructions are broken into smaller series of instructions and so these are solved each at a clip therefore merely one direction can be executed at a time.With parallel calculating we can hold multiple treating units and each unit can run multiple instructions at the same time. Therefore different instructions will run on different processors. So by utilizing parallel calculating we can salvage clip, money, computing machine memory and supply concurrence.

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Grid computer science is a subdivision of parallel computer science and its working rule is designed in such a manner that in a web which are utilizing unfastened criterions different resources can be computed at the same clip to accomplish high criterions. To accomplish high quality public presentation these calculations need to be scheduled for supplying efficiency.

For grid calculating a node needs to be chosen so that the running clip of the undertaking can be reduced. In this paper we predict the running clip of the host and therefore it has been predicted that the host burden of the system can be used to foretell the running clip [ 7 ] .The host burden is straight relative to the running clip and the CPUaa‚¬a„?s handiness can besides be obtained.

Host burden can besides be discovered from many methods such as additive theoretical accounts [ 9,10 ] and the proposed inclination based theoretical accounts [ 11,12 ] .These methods can be inaccurate as these methods do non take into history the kineticss of grid calculating. We use the nervous web attack to gauge the host burden of the CPU. But before we perform such analysis we will foremost gauge if nervous webs can be proven better than the traditional methods and secondly the cost of the public presentation steps such as proof, proving and anticipation rate. Last, if the nervous web can be applied to the existent clip grid environment.

Experiments are performed on nervous webs where the values are collected on a period of 10 yearss and are trained and so observed if the host burden can be obtained for the following 10 yearss without the demand for retraining. It is besides required that we can bring forth low average mistakes and these consequences can be applied to a existent universe scenario.

Area of probe:

Force burden connexion in the grid environment. So we have used for different sorts of unix system APOX.APX7, SAHARA THEMES. We are utilizing host burden postulation on this four utilizing system by utilizing nervous web we besides investigate these in nervous web will work on existent clip grid environment.

Applications of the Area:

An unreal nervous web is a method of developing information design which about works as human encephalon. In this engineering usually the web will be running many figure of processors in analogue.

In the context of ANNs, one partitions the information set indiscriminately into two parts, the trainer set and the examiner set. The theoretical account and its parametric quantities are obtained utilizing trainer set. Execution of this construction is tested on examiner set. If the theoretical account is found satisfactory over the examiner set the theoretical account is considered valid plenty for farther usage otherwise a new divider may be carried out, the whole procedure is repeated to acquire a more valid theoretical account. In nervous webs the relationships between input and end product braces are construct through the acquisition procedure. The larning ab initio starts with a arbitrary weight values and larning rate, at each rhythm the weights are adjusted based on the acquisition algorithm and the acquisition will go on until the web produces the good consequences for the preparation set. The weight vector which was constructed in developing procedure will be tested on a different set to prove the public presentation of the weight vector, if it gives the good consequences on proving set we can utilize that weight vector for future usage.

We use two different nervous webs one is the feedforward web and the other is the back extension, these two webs can be used to foretell the host tonss in an environment. These webs can foretell random inputs and can bring forth accurate functions for the results.These webs provide simpleness and are easy to apply.These web can besides be applied easy in a existent clip environment.

Feed Forward Neural Networks:

In the above figures we represent the feedforward nervous web in which operations performed step the host burden in an environment.In the above figure is it observed that the web has 4 inpus where the external information can be stored and one end product bed C where the solution can be obtained.The web input and end product beds are separated by two concealed layers.Connections exist in the web which indicate the flow of the informations between nodes.

The figure of inputs in every bed are the same and the connexions as in the figure are modified by different weights and the excess input required is given a changeless value of 1.The bias value is used to modify the excess weight on the input.

Whenever the web is fed with input it performs its computations and so it transfers the consequence to the following bed.

Where — is the end product of the current node, N is the figure of nodes in the old bed, — is an input to the current node from the old bed, — is the weight modifying the corresponding connexion from — and — is the prejudice. In add-on H ( x ) is either a sigmoid activation map for concealed bed nodes, or a additive activation map for the end product bed nodes.

Requirement FOR THE Experiment:

The Input Parameters:

For a nervous web it is of import that we foremost place the input parametric quantity right for public presentation grounds and these may include the figure of concealed beds required in the web or the figure of nodes in the web. We have hence observed through trail and mistake and the old experiments that the figure of concealed beds two are sufficient and inputs can be more in figure. The end product required for this experiment is one and the nodes observed are in the ratio of 20:10:1,30:10:1,50:20:1 and 60:30:1.The nervous web besides requires a learning rate to develop the web and the learning rates required scope from 0.01,0.05,0.1,0.2 and 0.3.

The web parametric quantity are so fed to the web in the signifier of informations series for rating. We choose different burden hints observed by Dinda [ 23 ] on the Unix system such as axp0, axp7, Sahara and Themis. We define load here as the procedures which are about to be run or the 1s which are set in a waiting line by the scheduler. The burden traces ascertained represent gaining control periods and machine types.

Because the sigmoid activation can take merely bias values of either 0,1 we have to execute some preprocessing techniques to normalise the burden hint values within this scope. We so observe the standard divergence and agencies of the burden hints for the verification of the normalized values.We use the undermentioned standardization expression to x in each burden hint.

Where — and — are the maximal and minimal value of each burden hint severally and lower edge and the upper edge are represented by — – in the interval of [ 0.1,0.9 ] .The normalized burden hints are represented in the signifier of a tabular array.

The normalized burden hints are divided into three different sets: acquisition set, proving set and validating set and each is divided into different per centums such as 50 % ,30 % and 20 % .The nervous web is fed with larning rates to make up one’s mind upon the connexion weights required for the web. The normalized mean square mistake is measured utilizing the validating set and is used to make up one’s mind if the acquisition procedure needs to halt. If the mistake rate is measured above 20 % the preparation is stopped and the old weight values are used.

Consequences of the Host Load Prediction:

In the undermentioned figures we observe the consequences the y-axis represents the normalized mean square mistake and the mean and standard divergence of the different burden traces.The burden value anticipation mistake is calculated by utilizing the expression

Here N is the figure of burden values and aa‚¬ ” and aa‚¬ ” are the predicted and existent values of the ith hint severally.

The mean, SD and the NMSE keeps altering with different larning rates. As it can be seen from the figure the NMSE has a value scope of [ 3.4 % ,7.5 % ] in axp0, [ 3.8 % ,5.1 % ] in axp7, [ 3.5 % ,7.5 % ] in Sahara and [ 0.7 % ,1.2 % ] in Themis. The mean has values in the scope [ 2.6 % ,3.2 % ] in axp0, [ 1.1 % ,1.8 % ] in axp7, [ 5.5 % ,12.8 % ] in Sahara and [ 1.6 % ,3.7 % ] in Themis. The architecture of the web and the acquisition rate applied influence the preparation clip as shown in the tabular array below.

The preparation set size besides influences the preparation clip of the webs. A big preparation set may take a long clip to finish a web and a little size preparation set may be deficient to finish the web on an norm it is estimated that 100,000 preparation set is equal to 5days of host burden. The testing and validating set are independent of any parametric quantities unlike the preparation clip. The undermentioned figure shows the mean validating and testing clip for each host burden hint. If the information set is bigger the validating and testing clip besides become big. The validating and testing clip besides addition with the web architecture.

Comparison of the Consequences:

The mean and standard divergence are used to mensurate the public presentation between the soon constructed nervous web and the old networks.The webs compared are 20:10:1 and 30:10:1 both with the larning rate of 0.3.The following tabular array shows the decrease in mean by 60 % and standard divergence by 70 % .The merely decrease with old methods is upto 30 % in the instance of Sahara and the rate is much higher with axp0 and axp7.


The cost and public presentation of the nervous web is studied with the aid of burden hint analysis for host burden anticipation. The nervous web is proven much better than the old methods such as mean and standard divergence public presentation with 60 % and 70 % and the figure of preparation samples are 100,000 which can bring forth accurate anticipations.

With the cost decrease and the anticipation ability of the nervous web it is clearly observed that the web can be applied in existent clip webs in a grid environment.

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Advantages Of Parallel Computing Over Serial Computing Computer Science. (2016, Nov 29). Retrieved from

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