Esteban E. Mocskos

Grid Matrix: a grid simulation tool to focus on the propagation of resource and monitoring information

Grid Computing proposes unlimited access to different computational resources in a transparent way. High performance execution in grid environments is virtually impossible without timely access to accurate and up-to-date information related to distributed resources and services. Due to inherent difficulty of testing the different information propagation policies in real grid infrastructures, several simulation frameworks arose to help in this issue. In this work, we present Grid Matrix, an extension to one of the most used grid simulation tools (SimGrid2) to focus on the propagation of monitoring and resource information allowing the creation of virtual grid infrastructures. This extension enables GUI editing of network topology and provides the feature of scripting to define simulation details based on the newly developed C++ and Python bindings of SimGrid2 API. As a case study, Grid Matrix was used to test four different policies: hierarchical, super-peer, best-neighbor and random. The simulated scenario consisted of 96 master nodes based on the real Teragrid infrastructure as was publicly available at the time of writing this paper. We introduce three metrics that capture and summarize the information propagation behavior: LIR, GIR and GIV. LIR captures the local behavior quantifying the amount of up-to-date information in each node. GIR evaluates the global information state in the whole network averaging the LIR values, while GIV measures the variability of LIR. In the presented case, the best results in terms of the proposed metrics were attained by the hierarchical policy, followed by super-peer which outperformed random and best-neighbor. The modern and modular design of the scripting features included in Grid Matrix, in close conjunction with the user friendly GUI happened to be a very powerful tool for the evaluation of new propagation policies of resource information.

Improvements to Super-Peer Policy Communication Mechanisms

The use of large distributed computing infrastructures has become a fundamental component in most of scientific and technological projects. Due to its highly distributed nature, one of the key topics to be addressed in large distributed systems (like Grids and Federation of Clouds) is the determination of the availability and state of resources. Having up-to-date information about resources in the system is extremely important as this is consumed by the scheduler for selecting the appropriate target in each job to be served.

Efficient Fluorescence Microscopy Analysis over a Volunteer Grid/Cloud Infrastructure

This work presents a distributed computing algorithm over volunteer grid/cloud computing systems for Fluorescence Correlation Spectroscopy, a computational biology technique for obtaining quantitative information about the motion of molecules in living cells. High performance computing is needed to cope with large computing times when performing complex simulations, and volunteer grid/cloud computing emerges as a powerful paradigm to solve this kind of problems by coordinately using many computing resources distributed around the world. The proposed algorithm applies a domain decomposition technique for performing many simulations using different cell models at the same time. The experimental evaluation performed on a volunteer distributing computing infrastructure demonstrates that efficient execution times are obtained when using OurGrid middleware.

Hands-On Experience in HPC With Secondary School Students

It is a global concern that the number of students in computing-related fields has been decreasing in the last few years. As a way to improve this situation, several institutions have been implementing programs to attract and keep students in science and technology majors. This work describes a high-performance computing (HPC) course intended for secondary school students, which requires only the most basic infrastructure. In this hands-on workshop, the students learn how to assemble, install, and test a cluster. In parallel, students are exposed to a complete new area of knowledge, obtaining a more accurate view of the discipline of computer science and increasing their confidence in their ability to follow computer science. This course also provides opportunities for students to meet scientists and lose any misconceptions and negative stereotypes about science in general and computer science in particular. The aim of this paper is to detail the key concepts conveyed to students, share the course organization, materials, and examples used, and describe the overall experience.

SherlockFog: Finding Opportunities for MPI Applications in Fog and Edge Computing

The Fog and Edge Computing paradigms have emerged as a solution to limitations of the Cloud Computing model to serve a huge amount of connected devices efficiently. These devices have unused computing power that could be exploited to execute parallel applications.

Initial Experiences from TUPAC Supercomputer

High Performance Computing centers boost the development of a wide range of disciplines in Science and Engineering. The installation of public shared facilities in a country is an effort that should be carefully used and must be as open as possible to strengthen the impact of these resources.

Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems

The computational infrastructures are becoming larger and more complex. Their organization and interconnection are acquiring new dimensions with the increasing adoption of Cloud Technology and the establishment of Federations of cloud providers.

Evaluation of a Master-Slave Parallel Evolutionary Algorithm Applied to Artificial Intelligence for Games in the Xeon-Phi Many-Core Platform

Evolutionary algorithms are non-deterministic metaheuristic methods that emulate the evolution of species in nature to solve optimization, search, and learning problems. This article presents a parallel implementation of evolutionary algorithms on Xeon Phi for developing an artificial intelligence to play the NES Pinball game. The proposed parallel implementation offloads the execution of the fitness function evaluation to Xeon Phi. Multiple evolution schemes are studied to get the most efficient resource utilization. A micro-benchmarking of the Xeon Phi coprocessor is performed to verify the existing technical documentation and obtain detail knowledge of its behavior. Finally, a performance analysis of the proposed parallel evolutionary algorithm is presented, focusing on the characteristics of the evaluated platform.

Cloud Computing for Fluorescence Correlation Spectroscopy Simulations

Fluorescence microscopy techniques and protein labeling set an inflection point in the way cells are studied. The fluorescence correlation spectroscopy is extremely useful for quantitatively measuring the movement of molecules in living cells. This article presents the design and implementation of a system for fluorescence analysis through stochastic simulations using distributed computing techniques over a cloud infrastructure. A highly scalable architecture, accessible to many users, is proposed for studying complex cellular biological processes. A MapReduce algorithm that allows the parallel execution of multiple simulations is developed over a distributed Hadoop cluster using the Microsoft Azure cloud platform. The experimental analysis shows the correctness of the implementation developed and its utility as a tool for scientific computing in the cloud.