Artificial Intelligence and Software Engineering
Computation has offered an innumerable number of advantages to the organizations and the individuals whose works have been eased by the wake of new technology. Similarly, with the advent of modern programming languages, it’s possible to execute various tasks at a rapid rate, and still maintain the accuracy that is desired in the end products. What matters in solving particular problems is primarily controlled by the kind of input that is fed into then processors. The renowned researcher Larkin (2017) affirms that whatever you key in is what you receive on the other end of the display. The statement meant the careful deliberation of task in a machine should be donemore professionally. Variousresearchers have opined the different means through which particular problems can be solved. In the recent study conducted by Larkin (2017), he argued that the nature of the work that an organization indulges in is what determines what kind of method that can be invoked to arrive at the solution. Additionally, the type of data that is handled control the route of execution. For example, compound data requires breakdown into small subsets that canbe processed at a faster and more accurate manner. Similarly, the characteristics of the information control the methods that a programmer can use, in determining the solutions.
The previous studies indicated that algorithms serve an essential function in a particular set of design. They comprise of special formulas and mathematical equations that have specific elements to control the movements of data from one node to another. They can search for the correct solution of a particular problem or even perform the calculations, based on the information that is fed. Irrespective of the approach adopted, what matters is the final product, that is devoid of significant errors. Currently, parallelism is being embraced in various places, where colossal data is needed. According to Buricova&Reblova (2018), parallelism is the state of aligning the processors alongside each other and shares the task that is provided. Each processor interprets a unique set of data and provides the final solution. Combination of different outputs forms the final and definite answer to the sophisticated problem (Torres et al., 2017). This strategy has found applications in significant institutions and also in machines that require high levels of intelligence. It’s essential to take keen consideration of the environment in which the data opeerate.A combination of more than one method provides necessary intervention.
Possible sources of parallelism
Parallelism has achieved much interest from the researchers and scholars, who have keen interests in understanding the ways of reducing data latency. It is defined as the process of cutting the data into several subsets, which can be processed at a faster rate. Depending on the nature of the use, machines can adopt parallelism architecture to increase efficiency. Based on the research conducted by Larkin (2017), he identified that the primary cause of underperformance of computers and other programmed machines emanates from information lagging. Thisis contributed by the redundancy which occurs along the nodes. To solve such cases, dividing the data depending on their size and anticipated output could significantly mitigate the challenges. With that said, there are various sources of parallelism.
One of the sources that are discussed by Buricova & Reblova (2018) is the loop. It is defined as the flow of statements, which allows the codes to be executed in a particular form. The architecture of the processors is determined by the amount of data that is handled per a given time. As it was allluded earlier, complex information is difficult to process in one instance. It requires some series of executions, which are usually performed in a skewed manner. This is the most cause of parallelism in computers. This is because the machines are manufactured without the capability of dealing with the sophisticated data. To allow interpretation and easy processing, systems are, therefore, arranged in a way that would increase efficiency. The exchange of information along the nodes happens simultaneously, but the output of each differs. Larkin (2017) affirms thatconsiderable data intrigue the manufactures to use parallelism in designing the gadgets. However, they come with hiked prices due to the costs of the hardware and efforts employed to assemble the entire machine.
Scientific models may introduce parallelism. Data processing is done by invoking various algorithms that calculate the required task and gives the output. During the process, the machine might fail to work as anticipated, which leads to backtracking of the data to the primary node (Torres et al., 2017). Furthermore, sequencing of the nodes to the successors could fail to offer the required solution. When such a case happens, other computational methods could be investigated, and result to use of parallelism. In this scenario, the nature of the task dictates, the kind of procedure to employ. Far and above, multiple questions cannot be solvedby a single processor. More is required, to share the task, hence improve the performance. This is very essential especially in areas where prompt outputs are needed. Additionally, it’s the best option to mitigate the hanging of computer, when one processor is used to solve a huge problem (Buricova&Reblova, 2018). Various authors have affirmed that external factors such as the nature of the data, the number of tasks and efficiency required are control factors in establishing what model is fit(Torres et al.,2017). Parallelism has been tested and provides the viable solution to deal with most challenging issues in computation.
Under what conditions the sources of parallelism can be used
The uses of parallelism vary depending on the nature of the person who is need of the data. The employment of this technique has found appreciation in various sectors, where the sophisticated and seamless flow of data is required. For example, production processes requirefast interpretation of data. Again, the transmission of data from one node to another should be speedy, to prevent stoppage of individual sections of the line of production (Larkin, 2017). Parallelism is only applicable if the processor and production units are organized in a manner that facilitates secure exchange of information. This would, therefore, require the use of high memory systems that can accommodate the heaviness of the data. Furthermore, if parallelism is to be used in machines that need high recognition capabilities, more supportive hardware are required. The paramount thing to note is that what dictates the source of parallelism is the kind of data that is available for processing.
Enormous tasks are always tricky to process, and require subdivision to make the work accessible. The make of the processor determines how fast an algorithm is executed(Torres et al.,2017). Most of the machines in the market are not able to handle massive tasks. With the increased need to manage significant information, the parallel design allows the processor to split the primary question, into subproblems. The smaller tasks are processed and give immediate answers, which makes the entire process simple. This is one way that has been utilized by various people to complete tasks efficiently. Enough space is neededto have as many subproblems as possible.The parallel design, therefore, continually improves the output of a set of data. This has the overall effectsof reducing time wastages and reduced operational costs.