Swarming the SC’17 Student Cluster Competition

The Student Cluster Competition is a suite of challenges where teams of undergraduates design a computer cluster and then compete against each other through various benchmark applications. The present study will provide a select summary of the experiences of Team Swarm who represented the Georgia Institute of Technology at the SC’17 Student Cluster Competition. This report will first describe the competition and the members of Team Swarm. After this introduction, it focuses on three major aspects of the experience: the hardware and software architecture of the team’s computer cluster, the team’s system administration workflow and the team’s usage of cloud resources. Additionally, the appendix provides a brief description of the team members and their method of preparation.Undergraduat

Survey of scientific programming techniques for the management of data-intensive engineering environments

The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.The current work has been partially supported by the Research Agreement between the RTVE (the Spanish Radio and Television Corporation) and the UC3M to boost research in the field of Big Data, Linked Data, Complex Network Analysis, and Natural Language. It has also received the support of the Tecnologico Nacional de Mexico (TECNM), National Council of Science and Technology (CONACYT), and the Public Education Secretary (SEP) through PRODEP