Strategies of Domain Decomposition to Partition Mesh-Based Applications onto Computational Grids

In this paper, we evaluate strategies of domain decomposition in Grid environment to solve mesh-basedapplications. We compare the balanced distribution strategy with unbalanced distribution strategies. While the former is acommon strategy in homogenous computing environment (e.g. parallel computers), it presents some problems due tocommunication latency in Grid environments. Unbalanced decomposition strategies consist of assigning less workload toprocessors responsible for sending updates outside the host. The results obtained in Grid environments show that unbalanceddistributions strategies improve the expected execution time of mesh-based applications by up to 53%. However, this is not truewhen the number of processors devoted to communication exceeds the number of processors devoted to calculation in thehost. To solve this problem we propose a new unbalanced distribution strategy that improves the expected execution time up to43%. We analyze the influence of the communication patterns on execution times using the Dimemas simulator.Peer ReviewedPostprint (published version

Parallel Out-of-Core Sorting: The Third Way

Sorting very large datasets is a key subroutine in almost any application that is built on top of a large database. Two ways to sort out-of-core data dominate the literature: merging-based algorithms and partitioning-based algorithms. Within these two paradigms, all the programs that sort out-of-core data on a cluster rely on assumptions about the input distribution. We propose a third way of out-of-core sorting: oblivious algorithms. In all, we have developed six programs that sort out-of-core data on a cluster. The first three programs, based completely on Leighton\u27s columnsort algorithm, have a restriction on the maximum problem size that they can sort. The other three programs relax this restriction; two are based on our original algorithmic extensions to columnsort. We present experimental results to show that our algorithms perform well. To the best of our knowledge, the programs presented in this thesis are the first to sort out-of-core data on a cluster without making any simplifying assumptions about the distribution of the data to be sorted

Equilibrado de carga dirigido por modelos de Kernels de datos paralelos en plataformas heterogéneas de alto rendimiento

Las aplicaciones de datos paralelos se componen de varios procesos que aplican el mismo cómputo (kernel) a diferentes conjuntos de datos. Además, durante su ejecución, estas aplicaciones necesitan comunicar resultados parciales. Las plataformas heterogéneas son aquellas donde cada recurso de cómputo del sistema es probablemente diferente a los otros, y están compuestas por aceleradores. La conexión entre los elementos se realiza mediante redes de diferente rendimiento y características. Estos tienen que trabajar juntos para ejecutar una aplicación o resolver un problema, lo cual es lo complicado de este escenario. Por ello, el problema del equilibrado de carga de las aplicaciones paralelas de datos en plataformas heterogéneas se está investigando y resolviendo mediante distribuciones no uniformes de la carga de trabajo entre todos los recursos disponibles. Este problema se ha demostrado NP-Completo. La literatura ha desarrollado varias heurísticas para encontrar soluciones óptimas en las que diferentes modelos de rendimiento de computación y comunicación se utilizan como métrica en los algoritmos de partición. Los modelos nos permiten describir el funcionamiento del sistema, mientras que las heurísticas son el enfoque que se utiliza para encontrar una solución satisfactoria. Discutimos el papel de estos modelos y, finalmente para mejorar estos enfoques heurísticos, sustituimos métricas basadas en volumen de comunicaciones por una métrica basada en los tiempos de comunicaciones. Estos tiempos son obtenidos mediante un modelo analítico a través de una herramienta simbólica que manipula, evalúa y representa el coste de la comunicación de una partición con una expresión analítica utilizando el modelo de rendimiento de comunicación τ–Lop.Data-Parallel applications are composed of several processes that apply the same computation (kernel) to different amounts of data. While its execution, these applications need to communicate partial results. The heterogeneous platforms are those where each computation resource of the system is probably different from the others, and are composed of accelerators. The connection between the elements is made through networks of different performance and characteristics. These have to work together to execute an application or solve a problem, which is the complicated part of this scenario. Therefore, the load balancing problem of Data-Parallel applications in heterogeneous platforms is being investigated and solved by non-uniform distributions of the workload among all available resources. The objective of this solution is to find a partition that minimizes the cost of computation and communication, which is not trivial. This problem is demonstrated as NP-Complete. The literature has developed several heuristics to find optimal solutions where computation and communication performance models are used as metrics in the partitioning algorithms. The models allow us to describe the functioning of the system, while heuristics are the approach used to find a satisfactory solution. We discuss the role of these models and finally, to improve these heuristic approaches, we replace metrics based on communications volume with a metric based on communication times. These times are obtained through a symbolic tool that manipulates, evaluates and represents the cost of communication of a partition with an analytic expression using the communication performance model τ –Lop.Máster Universitario en Ingeniería Informática. Universidad de Extremadur

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Numerical solution of 3-D electromagnetic problems in exploration geophysics and its implementation on massively parallel computers

The growing significance, technical development and employment of electromagnetic (EM) methods in exploration geophysics have led to the increasing need for reliable and fast techniques of interpretation of 3-D EM data sets acquired in complex geological environments. The first and most important step to creating an inversion method is the development of a solver for the forward problem. In order to create an efficient, reliable and practical 3-D EM inversion, it is necessary to have a 3-D EM modelling code that is highly accurate, robust and very fast. This thesis focuses precisely on this crucial and very demanding step to building a 3-D EM interpretation method. The thesis presents as its main contribution a highly accurate, robust, very fast and extremely scalable numerical method for 3-D EM modelling in geophysics that is based on finite elements (FE) and designed to run on massively parallel computing platforms. Thanks to the fact that the FE approach supports completely unstructured tetrahedral meshes as well as local mesh refinements, the presented solver is able to represent complex geometries of subsurface structures very precisely and thus improve the solution accuracy and avoid misleading artefacts in images. Consequently, it can be successfully used in geological environments of arbitrary geometrical complexities. The parallel implementation of the method, which is based on the domain decomposition and a hybrid MPI-OpenMP scheme, has proved to be highly scalable - the achieved speed-up is close to the linear for more than a thousand processors. Thanks to this, the code is able to deal with extremely large problems, which may have hundreds of millions of degrees of freedom, in a very efficient way. The importance of having this forward-problem solver lies in the fact that it is now possible to create a 3-D EM inversion that can deal with data obtained in extremely complex geological environments in a way that is realistic for practical use in industry. So far, such imaging tool has not been proposed due to a lack of efficient, parallel FE solutions as well as the limitations of efficient solvers based on finite differences. In addition, the thesis discusses physical, mathematical and numerical aspects and challenges of 3-D EM modelling, which have been studied during my research in order to properly design the presented software for EM field simulations on 3-D areas of the Earth. Through this work, a physical problem formulation based on the secondary Coulomb-gauged EM potentials has been validated, proving that it can be successfully used with the standard nodal FE method to give highly accurate numerical solutions. Also, this work has shown that Krylov subspace iterative methods are the best solution for solving linear systems that arise after FE discretisation of the problem under consideration. More precisely, it has been discovered empirically that the best iterative method for this kind of problems is biconjugate gradient stabilised with an elaborate preconditioner. Since most commonly used preconditioners proved to be either unable to improve the convergence of the implemented solvers to the desired extent, or impractical in the parallel context, I have proposed a preconditioning technique for Krylov methods that is based on algebraic multigrid. Tests for various problems with different conductivity structures and characteristics have shown that the new preconditioner greatly improves the convergence of different Krylov subspace methods, which significantly reduces the total execution time of the program and improves the solution quality. Furthermore, the preconditioner is very practical for parallel implementation. Finally, it has been concluded that there are not any restrictions in employing classical parallel programming models, MPI and OpenMP, for parallelisation of the presented FE solver. Moreover, they have proved to be enough to provide an excellent scalability for it