In the Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades, the permission to use the embedded systems laboratory was obtained. INTI-Lab researchers will use this laboratory to do different research related to the processing of large scale videos, climate predictions, climate change research, physical simulations, among others. This type of projects, demand a high complexity in their processes, carried out in ordinary computers that result in an unfavorable time for the researcher. For this reason, one opted for the implementation of a high-performance cluster architecture that is a set of computers interconnected to a local network. This set of computers tries to give a unique behavior to solve complex problems using parallel computing techniques. The intention is to reduce the time directly proportional to the number of machines, giving a similarity of having a low-cost supercomputer. Different performance tests were performed scaling from 1 to 28 computers to measure time reduction. The results will show if it is feasible to use the architecture in future projects that demand processes of high scientific complexity.

The Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades has several research projects related to computer science needing high computational resources. Some of these projects are associated with climate prediction, molecule modeling, physical simulations, and others these applications generate a significant amount of data, regarding the big data issue, despite having excellent hardware features, the final result is obtained after hours or days of calculation depending on the algorithm complexity. For this reason, it is not possible to present optimal solutions at an ideal time. .In this work, we propose the virtualization and configuration of a high-performance cluster (HPC) known commercially as a "supercomputer" that is composed of several computers connected to a high-speed network to behave like a single computer. The virtualization is used to run a scientific algorithm that will apply performance tests using four virtual computers to demonstrate that the reduction of time is achieved by using more machines and thus be able to be implemented in the laboratories of the institution.

Now-a-days, photorealistic images are demanded for the realization of scientific models, so we use rendering tools that convert three-dimensional models into highly realistic images. The problem of generating photorealistic images occurs when the three-dimensional model becomes larger and more complex, so the time to generate an image is much greater due to the limitations of hardware resources, about this problem is implemented the render farm, which consists in a set of computers interconnected by a high-speed network that provides a strip of the global image distributed in each participating computers with the intention of reducing the processing time of highly complex computational images. The research was implemented in a high-performance Beowulf group of the Universidad de Ciencias y Humanidades using a total of 18 computers. To demonstrate the efficiency of a rendering farm implementation, scalability tests were performed using a 360° equirrectangular model with a total of 67 million pixels, the work is carried out to achieve highly complex renderings in less time to benefit the direction of the research.