Modernity makes our minds blowing with all new hi-tech inventions and diversity of data technologies. Now we can get any information about everything in just a few seconds, with no effort. Devices are improving constantly, being able to process more and more data, and at the same time, becoming cheaper.
Sorting operations are one of the most common tasks in Computer Science. To develop such a class of operations, it’s necessary to work on complex algorithms, as well as on basic principles. Every algorithm works with a certain speed, having certain structural features. The common question is what determines the speed of the algorithm? Is it determined by the computing power of a particular device or by the complexity of the algorithm itself?
To answer such a question, we analyzed speed and results of tests of most common sorting algorithms, comparing different computers with different processing capabilities.
First of all, time of execution depends on the volume of input data and on the efficiency of the algorithm as well. The more data we need to process, the more time it takes. At the same time, the more complicated the algorithm is, the slower it works. So, it’s logical to improve the construction of the algorithm to get faster work.
We also need to consider the power consumption, which is related to the speed. Different tests show that some algorithms process data faster than others (Ahmed M. Aliyu 4). Results illustrate a proportional relationship between the CPU time and power consumption. This fact pushes us to the conclusion that linear algorithms can show better results on less powerful computers, than on the most developed machines.
Such a tendency is dictated by the very nature of the linear algorithm. Linear structure makes linear calculations, using a single thread of the CPU. Every operation is executing in a certain order. Sorting operations from left to right, every one of them is executed at certain time. Thus, no extra power is needed, since the algorithm won’t use such advantages.
Modern processors include multi-core structure and they are multi-threaded. New models demonstrate low power consumption and fast clock speeds. To use all these features, we need to rebuild the algorithm, creating a parallel structure (Kevin Jouper 31-44). Thus, algorithm can use all available threads, working with increased speed (Darko Božidar 5-14). New technologies, such as CUDA processors, simplify such a task even more.
Parallel sorting algorithms running on modern CPUs no longer depend on the amount of data, working with highest speeds.
Summarizing these statements, we understand that in order to speed up the sorting algorithms, we need to rebuild them, making parallel structure. Multi-core CPUs provide enough power to accelerate any algorithm that is now able to use advantages of multi-threaded processors. Parallel algorithms provide much more operations at the same time, working much faster than the linear ones. On the contrary, using the linear algorithms, it doesn’t matter how good is processing power, since every operation will be executed only after the previous one, using one thread.
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