Parallel multithreading algorithms forself-gravity computation inESyS-Particle

This thesis describes the design, implementation, and evaluation of efficient algorithms for self-gravity simulations in astronomical agglomerates. Due to the intrinsic complexity of modeling interactions between particles, agglomerate are studied using computational simulations. Self-gravity affects every particle in agglomerates, which can be composed of millions of particles. So, to perform a realistic simulation is computationally expensive. This thesis presents three parallel multithreading algorithms for self-gravity calculation, including a method that updates the occupied cells on an underlying grid and a variation of the Barnes & Hut method that partitions and arranges the simulation space in both an octal and a binary tree to speed up long range forces calculation. The goal of the algorithms is to make efficient use of the underlying grid that maps the simulated environment. The three methods were evaluated and compared over two scenarios: two agglomerates orbiting each other and a collapsing cube. The experimental evaluation comprises the performance analysis of the two scenarios using the two methods, including a comparison of the results obtained and the analysis of the numerical accuracy by the study of the conservation of the center of mass and angular momentum. Both scenarios were evaluated scaling the number of computational resources to simulate instances with different number of particles. Results show that the proposed octal tree Barnes & Hut method allows improving the performance of the self-gravity calculation up to 100 with respect to the occupied cell method. This way, efficient simulations are performed for the largest problem instance including 2,097,152 particles. The proposed algorithms are efficient and accurate methods for self-gravity simulations in astronomical agglomerates

Including accurate user estimates in HPC schedulers: ban empirical analysis

This article focuses on the problem of dealing with low accuracy of job runtime estimates provided by users of high performance computing systems. The main goal of the study is to evaluate the benefits on the system utilization of providing accurate estimations, in order to motivate users to make an effort to provide better estimates. We propose the Penalty Scheduling Policy for including information about user estimates. The experimental evaluation is performed over realistic workload and scenarios, and validated by the use of a job scheduler simulator. We simulated different static and dynamic scenarios, which emulate diverse user behavior regarding the estimation of jobs runtime. Results demonstrate that the accuracy of users runtime estimates influences the waiting time of jobs. Under our proposed policy, in a scenario where users improve their estimates, waiting time of users with high accuracy can be up to 2.43 times lower than users with the lowest accuracy.XV Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Generación automática de formularios desde ontologías

Con la finalidad de proveer una interfaz sencilla para el ingreso de datos en ontologías se desarrolló un generador semiautomático de formularios. El generador provee dos tipos de formularios, un formulario de preguntas múl-tiple opción y uno para permitir ingreso de datos. Los formularios se generan en tiempo de ejecución, de esta forma los cambios que se realicen sobre la on-tología impactarán automáticamente en los formularios. El generador desarro-llado permite disponer de formularios web para el ingreso de datos para cual-quier ontología. La aplicación es responsiva, o sea permite que pueda ser utili-zada en diferentes dispositivos (e.g., computadora portátil, tablet, smart phone). En este trabajo se presenta el diseño y el prototipo del generador semiautomáti-co de formularios desde ontologías junto con una evaluación inicial realizada sobre su efectividad con algunas ontologías de dominios diferentes.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Including accurate user estimates in HPC schedulers: ban empirical analysis

This article focuses on the problem of dealing with low accuracy of job runtime estimates provided by users of high performance computing systems. The main goal of the study is to evaluate the benefits on the system utilization of providing accurate estimations, in order to motivate users to make an effort to provide better estimates. We propose the Penalty Scheduling Policy for including information about user estimates. The experimental evaluation is performed over realistic workload and scenarios, and validated by the use of a job scheduler simulator. We simulated different static and dynamic scenarios, which emulate diverse user behavior regarding the estimation of jobs runtime. Results demonstrate that the accuracy of users runtime estimates influences the waiting time of jobs. Under our proposed policy, in a scenario where users improve their estimates, waiting time of users with high accuracy can be up to 2.43 times lower than users with the lowest accuracy.XV Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI