Search

High Performance Computing (HPC) applies different techniques to complex or large-volume applications, relying on both parallel software and hardware, to reduce their execution time compared to running them on a simple computer. On the other hand, Quantum Computing (QC) emerges as a new paradigm that leverages the properties of Quantum Mechanics for computation. QC has an inherently parallel nature and it is expected to solve some problems faster than classical computing. This paper carries out a bibliographic review to examine the point of view of different authors regarding the relationship between HPC and QC. The objective is to determine the trend of this relationship: Will QC replace classical HPC computing? or Will they complement each other? Also, if they were complementary tools, the aim is to answer: How could they be integrated? How will users access these resources?

In IoT applications, data capture in a sensor network can generate a large flow of information between the nodes and the cloud, affecting response times and device complexity but, above all, increasing costs. Fog computing refers to the use of pre-processing tools to improve local data management and communication with the cloud. This work presents an analysis of the features that platforms implementing fog computing solutions should have. Additionally, an experimental work integrating two specific platforms used for controlling devices in a sensor network, processing the generated data, and communicating with the cloud is presented.

The challenge of improving the performance of current processors is achieved by increasing the integration scale. This carries a growing vulnerability to transient faults, which increase their impact on multicore clusters running large scientific parallel applications. The requirement for enhancing the reliability of these systems, coupled with the high cost of rerunning the application from the beginning, create the motivation for having specific software strategies for the target systems. This paper introduces SMCV, which is a fully distributed technique that provides fault detection for message-passing parallel applications, by validating the contents of the messages to be sent, preventing the transmission of errors to other processes and leveraging the intrinsic hardware redundancy of the multicore. SMCV achieves a wide robustness against transient faults with a reduced overhead, and accomplishes a trade-off between moderate detection latency and low additional workload.