Designing a scalable dynamic load -balancing algorithm for pipelined single program multiple data applications on a non-dedicated heterogeneous network of workstations

Dynamic load balancing strategies have been shown to be the most critical part of an efficient implementation of various applications on large distributed computing systems. The need for dynamic load balancing strategies increases when the underlying hardware is a non-dedicated heterogeneous network of workstations (HNOW). This research focuses on the single program multiple data (SPMD) programming model as it has been extensively used in parallel programming for its simplicity and scalability in terms of computational power and memory size.;This dissertation formally defines and addresses the problem of designing a scalable dynamic load-balancing algorithm for pipelined SPMD applications on non-dedicated HNOW. During this process, the HNOW parameters, SPMD application characteristics, and load-balancing performance parameters are identified.;The dissertation presents a taxonomy that categorizes general load balancing algorithms and a methodology that facilitates creating new algorithms that can harness the HNOW computing power and still preserve the scalability of the SPMD application.;The dissertation devises a new algorithm, DLAH (Dynamic Load-balancing Algorithm for HNOW). DLAH is based on a modified diffusion technique, which incorporates the HNOW parameters. Analytical performance bound for the worst-case scenario of the diffusion technique has been derived.;The dissertation develops and utilizes an HNOW simulation model to conduct extensive simulations. These simulations were used to validate DLAH and compare its performance to related dynamic algorithms. The simulations results show that DLAH algorithm is scalable and performs well for both homogeneous and heterogeneous networks. Detailed sensitivity analysis was conducted to study the effects of key parameters on performance

Dynamic load balancing of parallel road traffic simulation

The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied

Toward Optimizing Distributed Programs Directed by Configurations

Networks of workstations are now viable environments for running distributed and parallel applications. Recent advances in software interconnection technology enables programmers to prepare applications to run in dynamically changing environments because module interconnection activity is regarded as an essentially distinct and different intellectual activity so as isolated from that of implementing individual modules. But there remains the question of how to optimize the performance of those applications for a given execution environment: how can developers realize performance gains without paying a high programming cost to specialize their application for the target environment? Interconnection technology has allowed programmers to tailor and tune their applications on distributed environments, but the traditional approach to this process has ignored the performance issue over gracefully seemless integration of various software components

Run-time Support for Parallelization of Data-Parallel Applications on Adaptive and Nonuniform Computational Environments

In this paper we discuss the runtime support required for the parallelization of unstructured data parallel applications on nonuniform and adaptive environments. The approach presented is reasonably general and is applicable to a wide variety of regular as well as irregular applications. We present performance results for the solution of an unstructured mesh on a cluster of heterogeneous workstations

Dynamic load balancing of parallel road traffic simulation

The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimization techniques for adaptability in MPI application

The first version of MPI (Message Passing Interface) was released in 1994. At that time, scientific applications for HPC (High Performance Computing) were characterized by a static execution environment. These applications usually had regular computation and communication patterns, operated on dense data structures accessed with good data locality, and ran on homogeneous computing platforms. For these reasons, MPI has become the de facto standard for developing scientific parallel applications for HPC during the last decades. In recent years scientific applications have evolved in order to cope with several challenges posed by different fields of engineering, economics and medicine among others. These challenges include large amounts of data stored in irregular and sparse data structures with poor data locality to be processed in parallel (big data), algorithms with irregular computation and communication patterns, and heterogeneous computing platforms (grid, cloud and heterogeneous cluster). On the other hand, over the last years MPI has introduced relevant improvements and new features in order to meet the requirements of dynamic execution environments. Some of them include asynchronous non-blocking communications, collective I/O routines and the dynamic process management interface introduced in MPI 2.0. The dynamic process management interface allows the application to spawn new processes at runtime and enable communication with them. However, this feature has some technical limitations that make the implementation of malleable MPI applications still a challenge. This thesis proposes FLEX-MPI, a runtime system that extends the functionalities of the MPI standard library and features optimization techniques for adaptability of MPI applications to dynamic execution environments. These techniques can significantly improve the performance and scalability of scientific applications and the overall efficiency of the HPC system on which they run. Specifically, FLEX-MPI focuses on dynamic load balancing and performance-aware malleability for parallel applications. The main goal of the design and implementation of the adaptability techniques is to efficiently execute MPI applications on a wide range of HPC platforms ranging from small to large-scale systems. Dynamic load balancing allows FLEX-MPI to adapt the workload assignments at runtime to the performance of the computing elements that execute the parallel application. On the other hand, performance-aware malleability leverages the dynamic process management interface of MPI to change the number of processes of the application at runtime. This feature allows to improve the performance of applications that exhibit irregular computation patterns and execute in computing systems with dynamic availability of resources. One of the main features of these techniques is that they do not require user intervention nor prior knowledge of the underlying hardware. We have validated and evaluated the performance of the adaptability techniques with three parallel MPI benchmarks and different execution environments with homogeneous and heterogeneous cluster configurations. The results show that FLEXMPI significantly improves the performance of applications when running with the support of dynamic load balancing and malleability, along with a substantial enhancement of their scalability and an improvement of the overall system efficiency.La primera versión de MPI (Message Passing Interface) fue publicada en 1994, cuando la base común de las aplicaciones científicas para HPC (High Performance Computing) se caracterizaba por un entorno de ejecución estático. Dichas aplicaciones presentaban generalmente patrones regulares de cómputo y comunicaciones, accesos a estructuras de datos densas con alta localidad, y ejecución sobre plataformas de computación homogéneas. Esto ha hecho que MPI haya sido la alternativa más adecuada para la implementación de aplicaciones científicas para HPC durante más de 20 años. Sin embargo, en los últimos años las aplicaciones científicas han evolucionado para adaptarse a diferentes retos propuestos por diferentes campos de la ingeniería, la economía o la medicina entre otros. Estos nuevos retos destacan por características como grandes cantidades de datos almacenados en estructuras de datos irregulares con baja localidad para el análisis en paralelo (big data), algoritmos con patrones irregulares de cómputo y comunicaciones, e infraestructuras de computación heterogéneas (cluster heterogéneos, grid y cloud). Por otra parte, MPI ha evolucionado significativamente en cada una de sus sucesivas versiones, siendo algunas de las mejoras más destacables presentadas hasta la reciente versión 3.0 las operaciones de comunicación asíncronas no bloqueantes, rutinas de E/S colectiva, y la interfaz de procesos dinámicos presentada en MPI 2.0. Esta última proporciona un procedimiento para la creación de procesos en tiempo de ejecución de la aplicación. Sin embargo, la implementación de la interfaz de procesos dinámicos por parte de las diferentes distribuciones de MPI aún presenta numerosas limitaciones que condicionan el desarrollo de aplicaciones maleables en MPI. Esta tesis propone FLEX-MPI, un sistema que extiende las funcionalidades de la librería MPI y proporciona técnicas de optimización para la adaptación de aplicaciones MPI a entornos de ejecución dinámicos. Las técnicas integradas en FLEX-MPI permiten mejorar el rendimiento y escalabilidad de las aplicaciones científicas y la eficiencia de las plataformas sobre las que se ejecutan. Entre estas técnicas destacan el balanceo de carga dinámico y maleabilidad para aplicaciones MPI. El diseño e implementación de estas técnicas está dirigido a plataformas de cómputo HPC de pequeña a gran escala. El balanceo de carga dinámico permite a las aplicaciones adaptar de forma eficiente su carga de trabajo a las características y rendimiento de los elementos de procesamiento sobre los que se ejecutan. Por otro lado, la técnica de maleabilidad aprovecha la interfaz de procesos dinámicos de MPI para modificar el número de procesos de la aplicación en tiempo de ejecución, una funcionalidad que permite mejorar el rendimiento de aplicaciones con patrones irregulares o que se ejecutan sobre plataformas de cómputo con disponibilidad dinámica de recursos. Una de las principales características de estas técnicas es que no requieren intervención del usuario ni conocimiento previo de la arquitectura sobre la que se ejecuta la aplicación. Hemos llevado a cabo un proceso de validación y evaluación de rendimiento de las técnicas de adaptabilidad con tres diferentes aplicaciones basadas en MPI, bajo diferentes escenarios de computación homogéneos y heterogéneos. Los resultados demuestran que FLEX-MPI permite obtener un significativo incremento del rendimiento de las aplicaciones, unido a una mejora sustancial de la escalabilidad y un aumento de la eficiencia global del sistema.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Francisco Fernández Rivera.- Secretario: Florín Daniel Isaila.- Vocal: María Santos Pérez Hernánde