Improving utilization of heterogeneous clusters

Datacenters often agglutinate sets of nodes with different capabilities, leading to a sub-optimal resource utilization. One of the best ways of improving utilization is to balance the load by taking into account the heterogeneity of these clusters. This article presents a novel way of expressing computational capacity, more adequate for heterogeneous clusters, and also advocates for task migration in order to further improve the utilization. The experimental evaluation shows that both proposals are advantageous and allow improving the utilization of heterogeneous clusters and reducing the makespan to 16.7% and 17.1%, respectively.This work has been supported by the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R and TIN2016-81840-REDT (CAPAP-H6 network) and the European HiPEAC Network of Excellenc

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

The work in this paper focuses on providing malleability to MPI applications by using a novel performance-aware dynamic reconfiguration technique. This paper describes the design and implementation of Flex-MPI, an MPI library extension which can automatically monitor and predict the performance of applications, balance and redistribute the workload, and reconfigure the application at runtime by changing the number of processes. Unlike existent approaches, our reconfiguring policy is guided by user-defined performance criteria. We focus on iterative SPMD programs, a class of applications with critical mass within the scientific community. Extensive experiments show that Flex-MPI can improve the performance, parallel efficiency, and cost-efficiency of MPI programs with a minimal effort from the programmer.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the project TIN2013- 41350-P, Scalable Data Management Techniques for High-End Computing Systems, and EU under the COST Program Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)Peer ReviewedPostprint (author's final draft

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

Dynamic load balancing strategies in heterogeneous distributed system

Distributed heterogeneous computing is being widely applied to a variety of large size computational problems. This computational environments are consists of multiple het- erogeneous computing modules, these modules interact with each other to solve the prob-lem. Dynamic load balancing in distributed computing system is desirable because it is an important key to establish dependability in a Heterogeneous Distributed Computing Systems (HDCS). Load balancing problem is an optimization problem with exponential solution space. The complexity of dynamic load balancing increases with the size of a HDCS and becomes difficult to solve effectively. The solution to this intractable problem is discussed under different algorithm paradigm.The load submitted to the a HDCS is assumed to be in the form of tasks. Dynamic allocation of n independent tasks to m computing nodes in heterogeneous distributed computing system can be possible through centralized or decentralized control. In central-ized approach,we have formulated load balancing problem considering task and machine heterogeneity as a linear programming problem to minimize the time by which all task completes the execution in makespan.The load balancing problem in HDCS aims to maintain a balanced allocation of tasks while using the computational resources. The system state changes with time on arrival of tasks from the users. Therefore,heterogeneous distributed system is modeled as an M/M/m queue. The task model is represented either as a consistent or an inconsistent expected time to compute (ETC) matrix. A batch mode heuristic has been used to de-sign dynamic load balancing algorithms for heterogeneous distributed computing systems with four different type of machine heterogeneity. A number of experiments have been conducted to study the performance of load balancing algorithms with three different ar-rival rate for the task. A better performance of the algorithms is observed with increasing of heterogeneity in the HDCS.A new codification scheme suitable to simulated annealing and genetic algorithm has been introduced to design dynamic load balancing algorithms for HDCS. These stochastic iterative load balancing algorithms uses sliding window techniques to select a batch of tasks, and allocate them to the computing nodes in the HDCS. The proposed dynamic genetic algorithm based load balancer has been found to be effective, especially in the case of a large number of tasks

HPCCP/CAS Workshop Proceedings 1998

This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey

A study in grid simulation and scheduling

Grid computing is emerging as an essential tool for large scale analysis and problem solving in scientific and business domains. Whilst the idea of stealing unused processor cycles is as old as the Internet, we are still far from reaching a position where many distributed resources can be seamlessly utilised on demand. One major issue preventing this vision is deciding how to effectively manage the remote resources and how to schedule the tasks amongst these resources. This thesis describes an investigation into Grid computing, specifically the problem of Grid scheduling. This complex problem has many unique features making it particularly difficult to solve and as a result many current Grid systems employ simplistic, inefficient solutions. This work describes the development of a simulation tool, G-Sim, which can be used to test the effectiveness of potential Grid scheduling algorithms under realistic operating conditions. This tool is used to analyse the effectiveness of a simple, novel scheduling technique in numerous scenarios. The results are positive and show that it could be applied to current procedures to enhance performance and decrease the negative effect of resource failure. Finally a conversion between the Grid scheduling problem and the classic computing problem SAT is provided. Such a conversion allows for the possibility of applying sophisticated SAT solving procedures to Grid scheduling providing potentially effective solutions

Software Approaches to Manage Resource Tradeoffs of Power and Energy Constrained Applications

Power and energy efficiency have become an increasingly important design metric for a wide spectrum of computing devices. Battery efficiency, which requires a mixture of energy and power efficiency, is exceedingly important especially since there have been no groundbreaking advances in battery capacity recently. The need for energy and power efficiency stretches from small embedded devices to portable computers to large scale data centers. The projected future of computing demand, referred to as exascale computing, demands that researchers find ways to perform exaFLOPs of computation at a power bound much lower than would be required by simply scaling today's standards. There is a large body of work on power and energy efficiency for a wide range of applications and at different levels of abstraction. However, there is a lack of work studying the nuances of different tradeoffs that arise when operating under a power/energy budget. Moreover, there is no work on constructing a generalized model of applications running under power/energy constraints, which allows the designer to optimize their resource consumption, be it power, energy, time, bandwidth, or space. There is need for an efficient model that can provide bounds on the optimality of an application's resource consumption, becoming a basis against which online resource management heuristics can be measured. In this thesis, we tackle the problem of managing resource tradeoffs of power/energy constrained applications. We begin by studying the nuances of power/energy tradeoffs with the response time and throughput of stream processing applications. We then study the power performance tradeoff of batch processing applications to identify a power configuration that maximizes performance under a power bound. Next, we study the tradeoff of power/energy with network bandwidth and precision. Finally, we study how to combine tradeoffs into a generalized model of applications running under resource constraints. The work in this thesis presents detailed studies of the power/energy tradeoff with response time, throughput, performance, network bandwidth, and precision of stream and batch processing applications. To that end, we present an adaptive algorithm that manages stream processing tradeoffs of response time and throughput at the CPU level. At the task-level, we present an online heuristic that adaptively distributes bounded power in a cluster to improve performance, as well as an offline approach to optimally bound performance. We demonstrate how power can be used to reduce bandwidth bottlenecks and extend our offline approach to model bandwidth tradeoffs. Moreover, we present a tool that identifies parts of a program that can be downgraded in precision with minimal impact on accuracy, and maximal impact on energy consumption. Finally, we combine all the above tradeoffs into a flexible model that is efficient to solve and allows for bounding and/or optimizing the consumption of different resources

Scheduling and Dynamic Management of Applications over Grids

The work presented in this Thesis is about scheduling applications in computational Grids. We study how to better manage jobs in a grid middleware in order to improve the performance of the platform. Our solutions are designed to work at the middleware layer, thus allowing to keep the underlying architecture unmodified. First, we propose a reallocation mechanism to dynamically tackle errors that occur during the scheduling. Indeed, it is often necessary to provide a runtime estimation when submitting on a parallel computer so that it can compute a schedule. However, estimations are inherently inaccurate and scheduling decisions are based on incorrect data, and are therefore wrong. The reallocation mechanism we propose tackles this problem by moving waiting jobs between several parallel machines in order to reduce the scheduling errors due to inaccurate runtime estimates. Our second interest in the Thesis is the study of the scheduling of a climatology application on the Grid. To provide the best possible performances, we modeled the application as a Directed Acyclic Graph (DAG) and then proposed specific scheduling heuristics. To execute the application on the Grid, the middleware uses the knowledge of the application to find thebest schedule.Les travaux présentés dans cette thèse portent sur l'ordonnancement d'applications au sein d'un environnement de grille de calcul. Nous étudions comment mieux gérer les tâches au sein des intergiciels de grille, ceci dans l'objectif d'améliorer les performances globales de la plateforme. Les solutions que nous proposons se situent dans l'intergiciel, ce qui permet de conserver les architectures sous-jacentes sans les modifier. Dans un premier temps, nous proposons un mécanisme de réallocation permettant de prendre en compte dynamiquement les erreurs d'ordonnancement commises lors de la soumission de calculs. En effet, lors de la soumission sur une machine parallèle, il est souvent nécessaire de fournir une estimation du temps d'exécution afin que celle-ci puisse effectuer un ordonnancement. Cependant, les estimations ne sont pas précises et les décisions d'ordonnancement sont sans cesse remises en question. Le mécanisme de réallocation proposé permet de prendre en compte ces changements en déplaçant des calculs d'une machine parallèle à une autre. Le second point auquel nous nous intéressons dans cette thèse est l'ordonnancement d'une application de climatologie sur la grille. Afin de fournir les meilleures performances possibles nous avons modélisé l'application puis proposé des heuristiques spécifiques. Pour exécuter l'application sur une grille de calcul, l'intergiciel utilise ces connaissances sur l'application pour fournir le meilleur ordonnancement possible

Técnicas de optimización dinámicas de aplicaciones paralelas basadas en MPI

Parallel computation on cluster architectures has become the most common solution for developing high-performance scientific applications. Message Passing Interface (MPI) [Mes94] is the message-passing library most widely used to provide communications in clusters. MPI provides a standard interface for operations such as point-to-point communication, collective communication, synchronization, and I/O operations. Along the I/O phase, the processes frequently access a common data set by issuing a large number of small non-contiguous I/O requests [NKP+96a, SR98], which might create bottlenecks in the I/O subsystem. These bottlenecks are still higher in commodity clusters, where commercial networks are usually installed. Many of those networks, such as Fast Ethernet or Gigabit, have high latency and low bandwidth which introduce performance penalties during the program execution. Scalability is also an important issue in cluster systems when many processors are used, which may cause network saturation and still higher latencies. As communication-intensive parallel applications spend a significant amount of their total execution time exchanging data between processes, the former problems may lead to poor performance not only in the I/O subsystem, but also in communication phase. Therefore, we can conclude that it is necessary to develop techniques for improving the performance of both communication and I/O subsystems. The main goal of this Ph.D. thesis is to improve the scalability and performance of MPI-based applications executed in clusters reducing the overhead of I/O and communications subsystems. In summary, this work proposes two techniques that solve these problems in an efficient way managing the high complexity of a heterogeneous environment: • Reduction in the number of communications in collective I/O operations: This thesis targets the reduction of the bottleneck in the I/O subsystem. Many applications use collective I/O operations to read/write data from/to disk. One of the most used is the Two-Phase I/O technique extended by Thakur and Choudhary in ROMIO. In this technique, many communications among the processes are performed, which could create a bottleneck. This bottleneck is still higher in commodity clusters, where commercial networks are usually installed, and in CMP clusters where the I/O bus is shared by the cores of a single node. Therefore, we propose improving locality in order to reduce the number of communications performed in Two-Phase I/O. • Reduction of transferred data volume: This thesis attemps to reduce the cost of interchanged messages, reducing the data volume by using lossless compression among processes. Furthermore, we propose turning compression on and off and selecting at run-time the most appropriate compression algorithms depending on the characteristics of each message, network performance, and compression algorithms behavior.-------------------------------------------------------------------------------------------------------------------------------------------------En la actualidad, las aplicaciones utilizadas en los entornos de computación de altas prestaciones, como por ejemplo simulaciones científicas o aplicaciones dedicadas a la extracción de datos (data-mining), necesitan además de enormes recursos de cómputo y memoria, el manejo de ingentes volúmenes de información. Las arquitecturas cluster se han convertido en la solución más común para ejecutar este tipo de aplicaciones. La librería MPI (Message Passing Interface) [Mes94] es la más utilizada en estos entornos, ya que ofrece un interfaz estándar para operaciones de comunicación punto a punto, colectivas, sincronización y de E/S. Durante la fase de E/S de las aplicaciones, los procesos acceden a un gran conjunto de datos mediante pequeñas peticiones de datos no-contiguos, por lo que pueden provocar cuellos de botella en el sistema de E/S. Estos cuellos de botella, pueden ser todavía mayor en los cluster, ya que se suelen utilizar redes comerciales como Fast Ethernet o Gigabit, las cuales tienen una gran latencia y bajo ancho de banda. Por otra parte la escalabilidad es un importante problema en los clusters, cuando se ejecutan a la vez un gran número de procesos, ya que pueden causar saturación de la red, y aumenar la latencia. Como consecuencia de una comunicación intensiva, las aplicaciones gastan mucho tiempo intercambiando información entre los procesos, provocando problemas tanto en el sistema de comunicación, como en el de E/S. Por lo tanto, podemos concluir que en un cluster los subsistemas de E/S y de comunicaciones representan uno de los principales elementos en los que conviene mejorar su rendimiento. El principal objetivo de esta Tesis Doctoral es mejorar la escalabilidad y rendimientos de las aplicaciones MPI ejecutadas en arquitecturas cluster, reduciendo la sobrecarga de los sistemas de comunicación y de E/S. Como resumen, este trabajo propone dos técnicas para resolver estos problemas de forma eficiente: • Reducción del número de comunicaciones en la operaciones colectivas de E/S: Esta tesis tiene como uno de sus objetivos reducir los cuellos de botella producidos en el sistema de E/S. Muchas aplicaciones científicas utilizan operaciones colectivas de E/S para leer/escribir datos desde/al disco. Una de las técnicas más utilizas es Two-Phase I/O ampliada por Thakur and Choudhary en ROMIO. En esta técnica se realizan muchas comunicaciones entre los procesos, por lo que pueden crear un cuello de botella. Este cuello de botella es aún mayor en los cluster que tiene instaladas redes comerciales, y en los clusters multicore donde el bus de E/S es compartido por todos los cores de un mismo nodo. Por lo tanto, nosotros proponemos aumentar la localidad y disminuir a la vez en número de comunicaciones que se producen en Two-Phase I/O para reducir los problemas de E/S en las arquitecturas cluster. • Reducción del volumen de datos en las comunicaciones: Esta tesis propone reducir el coste de las comunicaciones utilizando técnicas de compresión sin perdida. Concretamente, proponemos activar y desactivar la compresión y elegir el algoritmo de compresión en tiempo de ejecución, dependiendo de las características de cada mensaje, de la red y del comportamiento de los algoritmos de compresión