Very-large-scale integration device for parallel vertical group computing the sum of squared differences

Abstract

Is a paper that proposes a new method for computing sum-of-squares differences in a parallel vertical environment. The method is based on a group approach, which allows you to divide the task into several subtasks and calculate them in parallel. The article considers the problem of calculating the sum of squared differences between elements of large data arrays. Applying traditional methods of calculating such sums in parallel environments can be inefficient due to the exchange of large amounts of data between nodes. The proposed method allows to reduce the amount of transmitted data and increase the efficiency of calculations. The article proposes a new method for calculating the sum of squared differences, which allows to increase the efficiency of calculations in a parallel vertical environment. Testing of the method on different data sets shows its high efficiency compared to traditional methods of calculating sums of squared differences in parallel environments. The proposed method can be applied in various areas that require the processing of large volumes of data, and allows to increase the efficiency of calculations and reduce their execution time. The methods, algorithms and structures of devices for computing the sum of squared differences have been analyzed and their defects have been defined in the article. It has been defined that the device for computing the sum of squared differences should support the next: high device utilization; the use of capabilities and benefits of VLSI; short-term development and moderate price. The development of the device has been suggested by computing the sum of squared differences using modularity principles, coordination between data flow and computing capability of the device, pipelining and space parallelism, localization and simplification of links with elements. The proposed method can be useful for researchers in the fields of parallel computing and data processing, and can find applications in various fields such as data science, machine learning, image processing, and bioinformatics.