Parallel Aerodynamic Simulation on Open Workstation Clusters. Department of Aerospace Engineering Report no. 9830

The parallel execution of an aerodynamic simulation code on a non-dedicated, heterogeneous cluster of workstations is examined. This type of facility is commonly available to CFD developers and users in academia, industry and government laboratories and is attractive in terms of cost for CFD simulations. However, practical considerations appear at present to be discouraging widespread adoption of this technology. The main obstacles to achieving an efficient, robust parallel CFD capability in a demanding multi-user environment are investigated. A static load-balancing method, which takes account of varying processor speeds, is described. A dynamic re-allocation method to account for varying processor loads has been developed. Use of proprietary management software has facilitated the implementation of the method

Parallel optimization algorithms for high performance computing : application to thermal systems

The need of optimization is present in every field of engineering. Moreover, applications requiring a multidisciplinary approach in order to make a step forward are increasing. This leads to the need of solving complex optimization problems that exceed the capacity of human brain or intuition. A standard way of proceeding is to use evolutionary algorithms, among which genetic algorithms hold a prominent place. These are characterized by their robustness and versatility, as well as their high computational cost and low convergence speed. Many optimization packages are available under free software licenses and are representative of the current state of the art in optimization technology. However, the ability of optimization algorithms to adapt to massively parallel computers reaching satisfactory efficiency levels is still an open issue. Even packages suited for multilevel parallelism encounter difficulties when dealing with objective functions involving long and variable simulation times. This variability is common in Computational Fluid Dynamics and Heat Transfer (CFD & HT), nonlinear mechanics, etc. and is nowadays a dominant concern for large scale applications. Current research in improving the performance of evolutionary algorithms is mainly focused on developing new search algorithms. Nevertheless, there is a vast knowledge of sequential well-performing algorithmic suitable for being implemented in parallel computers. The gap to be covered is efficient parallelization. Moreover, advances in the research of both new search algorithms and efficient parallelization are additive, so that the enhancement of current state of the art optimization software can be accelerated if both fronts are tackled simultaneously. The motivation of this Doctoral Thesis is to make a step forward towards the successful integration of Optimization and High Performance Computing capabilities, which has the potential to boost technological development by providing better designs, shortening product development times and minimizing the required resources. After conducting a thorough state of the art study of the mathematical optimization techniques available to date, a generic mathematical optimization tool has been developed putting a special focus on the application of the library to the field of Computational Fluid Dynamics and Heat Transfer (CFD & HT). Then the main shortcomings of the standard parallelization strategies available for genetic algorithms and similar population-based optimization methods have been analyzed. Computational load imbalance has been identified to be the key point causing the degradation of the optimization algorithm¿s scalability (i.e. parallel efficiency) in case the average makespan of the batch of individuals is greater than the average time required by the optimizer for performing inter-processor communications. It occurs because processors are often unable to finish the evaluation of their queue of individuals simultaneously and need to be synchronized before the next batch of individuals is created. Consequently, the computational load imbalance is translated into idle time in some processors. Several load balancing algorithms have been proposed and exhaustively tested, being extendable to any other population-based optimization method that needs to synchronize all processors after the evaluation of each batch of individuals. Finally, a real-world engineering application that consists on optimizing the refrigeration system of a power electronic device has been presented as an illustrative example in which the use of the proposed load balancing algorithms is able to reduce the simulation time required by the optimization tool.El aumento de las aplicaciones que requieren de una aproximación multidisciplinar para poder avanzar se constata en todos los campos de la ingeniería, lo cual conlleva la necesidad de resolver problemas de optimización complejos que exceden la capacidad del cerebro humano o de la intuición. En estos casos es habitual el uso de algoritmos evolutivos, principalmente de los algoritmos genéticos, caracterizados por su robustez y versatilidad, así como por su gran coste computacional y baja velocidad de convergencia. La multitud de paquetes de optimización disponibles con licencias de software libre representan el estado del arte actual en tecnología de optimización. Sin embargo, la capacidad de adaptación de los algoritmos de optimización a ordenadores masivamente paralelos alcanzando niveles de eficiencia satisfactorios es todavía una tarea pendiente. Incluso los paquetes adaptados al paralelismo multinivel tienen dificultades para gestionar funciones objetivo que requieren de tiempos de simulación largos y variables. Esta variabilidad es común en la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT), mecánica no lineal, etc. y es una de las principales preocupaciones en aplicaciones a gran escala a día de hoy. La investigación actual que tiene por objetivo la mejora del rendimiento de los algoritmos evolutivos está enfocada principalmente al desarrollo de nuevos algoritmos de búsqueda. Sin embargo, ya se conoce una gran variedad de algoritmos secuenciales apropiados para su implementación en ordenadores paralelos. La tarea pendiente es conseguir una paralelización eficiente. Además, los avances en la investigación de nuevos algoritmos de búsqueda y la paralelización son aditivos, por lo que el proceso de mejora del software de optimización actual se verá incrementada si se atacan ambos frentes simultáneamente. La motivación de esta Tesis Doctoral es avanzar hacia una integración completa de las capacidades de Optimización y Computación de Alto Rendimiento para así impulsar el desarrollo tecnológico proporcionando mejores diseños, acortando los tiempos de desarrollo del producto y minimizando los recursos necesarios. Tras un exhaustivo estudio del estado del arte de las técnicas de optimización matemática disponibles a día de hoy, se ha diseñado una librería de optimización orientada al campo de la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT). A continuación se han analizado las principales limitaciones de las estrategias de paralelización disponibles para algoritmos genéticos y otros métodos de optimización basados en poblaciones. En el caso en que el tiempo de evaluación medio de la tanda de individuos sea mayor que el tiempo medio que necesita el optimizador para llevar a cabo comunicaciones entre procesadores, se ha detectado que la causa principal de la degradación de la escalabilidad o eficiencia paralela del algoritmo de optimización es el desequilibrio de la carga computacional. El motivo es que a menudo los procesadores no terminan de evaluar su cola de individuos simultáneamente y deben sincronizarse antes de que se cree la siguiente tanda de individuos. Por consiguiente, el desequilibrio de la carga computacional se convierte en tiempo de inactividad en algunos procesadores. Se han propuesto y testado exhaustivamente varios algoritmos de equilibrado de carga aplicables a cualquier método de optimización basado en una población que necesite sincronizar los procesadores tras cada tanda de evaluaciones. Finalmente, se ha presentado como ejemplo ilustrativo un caso real de ingeniería que consiste en optimizar el sistema de refrigeración de un dispositivo de electrónica de potencia. En él queda demostrado que el uso de los algoritmos de equilibrado de carga computacional propuestos es capaz de reducir el tiempo de simulación que necesita la herramienta de optimización

Scheduling strategies for parallel patterns on heterogeneous architectures

To help shrink the programmability-performance efficiency gap, we discuss that adaptive runtime systems can be used to facilitate the management of heterogeneous architectures. A runtime system can provide a significant performance boost while reducing the energy consumption, because it is aware of processors’ architectures and application’s requirements. We analyse how applications map onto hardware by inspecting built-in processor counters, and therefore build models to describe the observed behaviour. In this thesis, we discuss how parallel patterns, such as parallel for loops and pipelines, can be decomposed and efficiently executed on heterogeneous plat- forms. We propose several scheduling strategies aiming at reducing execution time and energy consumption. We demonstrate how applications can be run faster by mapping the application level parallelism onto the hardware process- ing units that best fit the application requirements, and by selecting the right task size. First, we devise a load balancing technique, that targets heterogeneous CPU and multi-GPU architectures. It monitors the relative speed of each processing unit, and distributes the remaining workload based on these relative speeds. By making all processing units to finish at same time, we avoid unnecessary waits between processors. Along with this load balancing technique, we propose a performance-sensitive partitioner that adapts the amount of computation offloaded to the accelerator for better performance and utilisation. We also present an accurate performance model for streaming applications, such as face recognition or object tracking. This model targets pipelined applications, as a series of stages, and performs a scalability analysis of each stage by using coarse and medium grain parallelism. Additionally, it also considers executing the stage on the GPU or not. By applying the model, we always find the best pipeline configuration among all possible, and get substantial performance and energy savings. All experiments in this thesis have been performed by using state-of-the-art hardware accelerators and benchmarks of the field of HPC. Specifically, we use the Rodinia and SHOC benchmark suites, for the evaluation of the parallel for partitioner. Moreover, we use the the ViVid application, along with tracking and SRAD applications from Rodinia Benchmark Suite, all of them are good candidates of vision applications. Finally, we rely on Intel Threading Building Blocks, the core engine of our schedulers; the Intel OpenCL SDK and CUDA SDK to offload computations to the GPU accelerators and Intel PCM library to monitor energy consumption and cache memory metrics.During the last decade, power consumption and energy efficiency have become key aspects in processor design. Nowadays, the power consumption is the principal limitation for further scaling of chip multiprocessors design (CMPs). In general, the research community agrees that current chip multiprocessor technology trends will not scale performance without an increase of power budget. Hardware design innovations as the recent Heterogeneous Architectures and Near Threshold Computing are needed to cope with the performance-power barrier. As a result of this, there has been a shift away from chip multiprocessors to heterogeneous processor architectures. Recently, we have witnessed an explosion in the availability of this kind of architectures. Many hardware vendors have released a number of heterogeneous processors to overcome the aforementioned limitations. However, software also requires changes to allow further performance scaling on these architectures. With the advent of heterogeneous architectures, hardware manufactures have impose the burden of explicit accelerator management on software developers. In general, programmers are used to sequential programming, but writing high-performance programs for heterogeneous architectures is a complex task. Programming for this kind of platforms requires the understanding of new hardware concepts, orchestration of different parallelism levels, the explicit management of different memory spaces and synchronisations between processing units, and finally the usage of low-level programming models such as OpenCL or CUDA. Moreover, heterogeneous architectures suffer from performance portability, as one program can exhibit unequal performance on different devices

Parallel Computation of Unsteady Flows on a Network of Workstations

Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced

A hierarchical parallel implementation model for algebra-based CFD simulations on hybrid supercomputers

(English) Continuous enhancement in hardware technologies enables scientific computing to advance incessantly and reach further aims. Since the start of the global race for exascale high-performance computing (HPC), massively-parallel devices of various architectures have been incorporated into the newest supercomputers, leading to an increasing hybridization of HPC systems. In this context of accelerated innovation, software portability and efficiency become crucial. Traditionally, scientific computing software development is based on calculations in iterative stencil loops (ISL) over a discretized geometry—the mesh. Despite being intuitive and versatile, the interdependency between algorithms and their computational implementations in stencil applications usually results in a large number of subroutines and introduces an inevitable complexity when it comes to portability and sustainability. An alternative is to break the interdependency between algorithm and implementation to cast the calculations into a minimalist set of kernels. The portable implementation model that is the object of this thesis is not restricted to a particular numerical method or problem. However, owing to the CTTC's long tradition in computational fluid dynamics (CFD) and without loss of generality, this work is targeted to solve transient CFD simulations. By casting discrete operators and mesh functions into (sparse) matrices and vectors, it is shown that all the calculations in a typical CFD algorithm boil down to the following basic linear algebra subroutines: the sparse matrix-vector product, the linear combination of vectors, and the dot product. The proposed formulation eases the deployment of scientific computing software in massively parallel hybrid computing systems and is demonstrated in the large-scale, direct numerical simulation of transient turbulent flows.(Català) La millora contínua en tecnologies de la informàtica possibilita a la comunitat de computació científica avançar incessantment i assolir ulteriors objectius. Des de l'inici de la cursa global per a la computació d'alt rendiment (HPC) d'exa-escala, s'han incorporat dispositius massivament paral·lels de diverses arquitectures als supercomputadors més nous, donant lloc a una creixent hibridació dels sistemes HPC. En aquest context d'innovació accelerada, la portabilitat i l'eficiència del programari esdevenen crucials. Tradicionalment, el desenvolupament de programari informàtic científic es basa en càlculs en bucles de patrons iteratius (ISL) sobre una geometria discretitzada: la malla. Tot i ser intuïtiva i versàtil, la interdependència entre algorismes i les seves implementacions computacionals en aplicacions de patrons sol donar lloc a un gran nombre de subrutines i introdueix una complexitat inevitable quan es tracta de portabilitat i sostenibilitat. Una alternativa és trencar la interdependència entre l'algorisme i la implementació per reduir els càlculs a un conjunt minimalista de subrutines. El model d'implementació portable objecte d'aquesta tesi no es limita a un mètode o problema numèric concret. No obstant això, i a causa de la llarga tradició del CTTC en dinàmica de fluids computacional (CFD) i sense pèrdua de generalitat, aquest treball està dirigit a resoldre simulacions CFD transitòries. Mitjançant la conversió d'operadors discrets i funcions de malla en matrius (disperses) i vectors, es demostra que tots els càlculs d'un algorisme CFD típic es redueixen a les següents subrutines bàsiques d'àlgebra lineal: el producte dispers matriu-vector, la combinació lineal de vectors, i el producte escalar. La formulació proposada facilita el desplegament de programari de computació científica en sistemes informàtics híbrids massivament paral·lels i es demostra el seu rendiment en la simulació numèrica directa de gran escala de fluxos turbulents transitoris.Enginyeria tèrmic

A hierarchical parallel implementation model for algebra-based CFD simulations on hybrid supercomputers

(English) Continuous enhancement in hardware technologies enables scientific computing to advance incessantly and reach further aims. Since the start of the global race for exascale high-performance computing (HPC), massively-parallel devices of various architectures have been incorporated into the newest supercomputers, leading to an increasing hybridization of HPC systems. In this context of accelerated innovation, software portability and efficiency become crucial. Traditionally, scientific computing software development is based on calculations in iterative stencil loops (ISL) over a discretized geometry—the mesh. Despite being intuitive and versatile, the interdependency between algorithms and their computational implementations in stencil applications usually results in a large number of subroutines and introduces an inevitable complexity when it comes to portability and sustainability. An alternative is to break the interdependency between algorithm and implementation to cast the calculations into a minimalist set of kernels. The portable implementation model that is the object of this thesis is not restricted to a particular numerical method or problem. However, owing to the CTTC's long tradition in computational fluid dynamics (CFD) and without loss of generality, this work is targeted to solve transient CFD simulations. By casting discrete operators and mesh functions into (sparse) matrices and vectors, it is shown that all the calculations in a typical CFD algorithm boil down to the following basic linear algebra subroutines: the sparse matrix-vector product, the linear combination of vectors, and the dot product. The proposed formulation eases the deployment of scientific computing software in massively parallel hybrid computing systems and is demonstrated in the large-scale, direct numerical simulation of transient turbulent flows.(Català) La millora contínua en tecnologies de la informàtica possibilita a la comunitat de computació científica avançar incessantment i assolir ulteriors objectius. Des de l'inici de la cursa global per a la computació d'alt rendiment (HPC) d'exa-escala, s'han incorporat dispositius massivament paral·lels de diverses arquitectures als supercomputadors més nous, donant lloc a una creixent hibridació dels sistemes HPC. En aquest context d'innovació accelerada, la portabilitat i l'eficiència del programari esdevenen crucials. Tradicionalment, el desenvolupament de programari informàtic científic es basa en càlculs en bucles de patrons iteratius (ISL) sobre una geometria discretitzada: la malla. Tot i ser intuïtiva i versàtil, la interdependència entre algorismes i les seves implementacions computacionals en aplicacions de patrons sol donar lloc a un gran nombre de subrutines i introdueix una complexitat inevitable quan es tracta de portabilitat i sostenibilitat. Una alternativa és trencar la interdependència entre l'algorisme i la implementació per reduir els càlculs a un conjunt minimalista de subrutines. El model d'implementació portable objecte d'aquesta tesi no es limita a un mètode o problema numèric concret. No obstant això, i a causa de la llarga tradició del CTTC en dinàmica de fluids computacional (CFD) i sense pèrdua de generalitat, aquest treball està dirigit a resoldre simulacions CFD transitòries. Mitjançant la conversió d'operadors discrets i funcions de malla en matrius (disperses) i vectors, es demostra que tots els càlculs d'un algorisme CFD típic es redueixen a les següents subrutines bàsiques d'àlgebra lineal: el producte dispers matriu-vector, la combinació lineal de vectors, i el producte escalar. La formulació proposada facilita el desplegament de programari de computació científica en sistemes informàtics híbrids massivament paral·lels i es demostra el seu rendiment en la simulació numèrica directa de gran escala de fluxos turbulents transitoris.Postprint (published version

Parallel source code transformation techniques using design patterns

Mención Internacional en el título de doctorIn recent years, the traditional approaches for improving performance, such as increasing the clock frequency, has come to a dead-end. To tackle this issue, parallel architectures, such as multi-/many-core processors, have been envisioned to increase the performance by providing greater processing capabilities. However, programming efficiently for this architectures demands big efforts in order to transform sequential applications into parallel and to optimize such applications. Compared to sequential programming, designing and implementing parallel applications for operating on modern hardware poses a number of new challenges to developers such as data races, deadlocks, load imbalance, etc. To pave the way, parallel design patterns provide a way to encapsulate algorithmic aspects, allowing users to implement robust, readable and portable solutions with such high-level abstractions. Basically, these patterns instantiate parallelism while hiding away the complexity of concurrency mechanisms, such as thread management, synchronizations or data sharing. Nonetheless, frameworks following this philosophy does not share the same interface and users require understanding different libraries, and their capabilities, not only to decide which fits best for their purposes but also to properly leverage them. Furthermore, in order to parallelize these applications, it is necessary to analyze the sequential code in order to detect the regions of code that can be parallelized that is a time consuming and complex task. Additionally, different libraries targeted to specific devices provide some algorithms implementations that are already parallel and highly-tuned. In these situations, it is also necessary to analyze and determine which routine implementation is the most suitable for a given problem. To tackle these issues, this thesis aims at simplifying and minimizing the necessary efforts to transform sequential applications into parallel. This way, resulting codes will improve their performance by fully exploiting the available resources while the development efforts will be considerably reduced. Basically, in this thesis, we contribute with the following. First, we propose a technique to detect potential parallel patterns in sequential code. Second, we provide a novel generic C++ interface for parallel patterns which acts as a switch among existing frameworks. Third, we implement a framework that is able to transform sequential code into parallel using the proposed pattern discovery technique and pattern interface. Finally, we propose mechanisms that are able to select the most suitable device and routine implementation to solve a given problem based on previous performance information. The evaluation demonstrates that using the proposed techniques can minimize the refactoring and optimization time while improving the performance of the resulting applications with respect to the original code.En los últimos años, las técnicas tradicionales para mejorar el rendimiento, como es el caso del incremento de la frecuencia de reloj, han llegado a sus límites. Con el fin de seguir mejorando el rendimiento, se han desarrollado las arquitecturas paralelas, las cuales proporcionan un incremento del rendimiento al estar provistas de mayores capacidades de procesamiento. Sin embargo, programar de forma eficiente para estas arquitecturas requieren de grandes esfuerzos por parte de los desarrolladores. Comparado con la programación secuencial, diseñar e implementar aplicaciones paralelas enfocadas a trabajar en estas arquitecturas presentan una gran cantidad de dificultades como son las condiciones de carrera, los deadlocks o el incorrecto balanceo de la carga. En este sentido, los patrones paralelos son una forma de encapsular aspectos algorítmicos de las aplicaciones permitiendo el desarrollo de soluciones robustas, portables y legibles gracias a las abstracciones de alto nivel. En general, estos patrones son capaces de proporcionar el paralelismo a la vez que ocultan las complejidades derivadas de los mecanismos de control de concurrencia necesarios como el manejo de los hilos, las sincronizaciones o la compartición de datos. No obstante, los diferentes frameworks que siguen esta filosofía no comparten una única interfaz lo que conlleva que los usuarios deban conocer múltiples bibliotecas y sus capacidades, con el fin de decidir cuál de ellos es mejor para una situación concreta y como usarlos de forma eficiente. Además, con el fin de paralelizar aplicaciones existentes, es necesario analizar e identificar las regiones del código que pueden ser paralelizadas, lo cual es una tarea ardua y compleja. Además, algunos algoritmos ya se encuentran implementados en paralelo y optimizados para arquitecturas concretas en diversas bibliotecas. Esto da lugar a que sea necesario analizar y determinar que implementación concreta es la más adecuada para solucionar un problema dado. Para paliar estas situaciones, está tesis busca simplificar y minimizar el esfuerzo necesario para transformar aplicaciones secuenciales en paralelas. De esta forma, los códigos resultantes serán capaces de explotar los recursos disponibles a la vez que se reduce considerablemente el esfuerzo de desarrollo necesario. En general, esta tesis contribuye con lo siguiente. En primer lugar, se propone una técnica de detección de patrones paralelos en códigos secuenciales. En segundo lugar, se presenta una interfaz genérica de patrones paralelos para C++ que permite seleccionar la implementación de dichos patrones proporcionada por frameworks ya existentes. En tercer lugar, se introduce un framework de transformación de código secuencial a paralelo que hace uso de las técnicas de detección de patrones y la interfaz presentadas. Finalmente, se proponen mecanismos capaces de seleccionar la implementación más adecuada para solucionar un problema concreto basándose en el rendimiento obtenido en ejecuciones previas. Gracias a la evaluación realizada se ha podido demostrar que uso de las técnicas presentadas pueden minimizar el tiempo necesario para transformar y optimizar el código a la vez que mejora el rendimiento de las aplicaciones transformadas.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: David Expósito Singh.- Secretario: Rafael Asenjo Plaza.- Vocal: Marco Aldinucc

A Policy-Based Resource Brokering Environment for Computational Grids

With the advances in networking infrastructure in general, and the Internet in particular, we can build grid environments that allow users to utilize a diverse set of distributed and heterogeneous resources. Since the focus of such environments is the efficient usage of the underlying resources, a critical component is the resource brokering environment that mediates the discovery, access and usage of these resources. With the consumer\u27s constraints, provider\u27s rules, distributed heterogeneous resources and the large number of scheduling choices, the resource brokering environment needs to decide where to place the user\u27s jobs and when to start their execution in a way that yields the best performance for the user and the best utilization for the resource provider. As brokering and scheduling are very complicated tasks, most current resource brokering environments are either specific to a particular grid environment or have limited features. This makes them unsuitable for large applications with heterogeneous requirements. In addition, most of these resource brokering environments lack flexibility. Policies at the resource-, application-, and system-levels cannot be specified and enforced to provide commitment to the guaranteed level of allocation that can help in attracting grid users and contribute to establishing credibility for existing grid environments. In this thesis, we propose and prototype a flexible and extensible Policy-based Resource Brokering Environment (PROBE) that can be utilized by various grid systems. In designing PROBE, we follow a policy-based approach that provides PROBE with the intelligence to not only match the user\u27s request with the right set of resources, but also to assure the guaranteed level of the allocation. PROBE looks at the task allocation as a Service Level Agreement (SLA) that needs to be enforced between the resource provider and the resource consumer. The policy-based framework is useful in a typical grid environment where resources, most of the time, are not dedicated. In implementing PROBE, we have utilized a layered architecture and façade design patterns. These along with the well-defined API, make the framework independent of any architecture and allow for the incorporation of different types of scheduling algorithms, applications and platform adaptors as the underlying environment requires. We have utilized XML as a base for all the specification needs. This provides a flexible mechanism to specify the heterogeneous resources and user\u27s requests along with their allocation constraints. We have developed XML-based specifications by which high-level internal structures of resources, jobs and policies can be specified. This provides interoperability in which a grid system can utilize PROBE to discover and use resources controlled by other grid systems. We have implemented a prototype of PROBE to demonstrate its feasibility. We also describe a test bed environment and the evaluation experiments that we have conducted to demonstrate the usefulness and effectiveness of our approach

"Virtual malleability" applied to MPI jobs to improve their execution in a multiprogrammed environment"

This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). The objective was to obtain the best performance in response time in multiprogrammed multiprocessors systems using batch systems, assuming all the jobs have the same priority. To achieve that purpose, the benefits of supporting malleability on MPI jobs to reduce fragmentation and consequently improve the performance of the system were studied. The contributions made in this work can be summarized as follows:· Virtual malleability: A mechanism where a job is assigned a dynamic processor partition, where the number of processes is greater than the number of processors. The partition size is modified at runtime, according to external requirements such as the load of the system, by varying the multiprogramming level, making the job contend for resources with itself. In addition to this, a mechanism which decides at runtime if applying local or global process queues to an application depending on the load balancing between processes of it. · A job scheduling policy, that takes decisions such as how many processes to start with and the maximum multiprogramming degree based on the type and number of applications running and queued. Moreover, as soon as a job finishes execution and where there are queued jobs, this algorithm analyzes whether it is better to start execution of another job immediately or just wait until there are more resources available. · A new alternative to backfilling strategies for the problema of window execution time expiring. Virtual malleability is applied to the backfilled job, reducing its partition size but without aborting or suspending it as in traditional backfilling. The evaluation of this thesis has been done using a practical approach. All the proposals were implemented, modifying the three scheduling levels: queuing system, processor scheduler and runtime library. The impact of the contributions were studied under several types of workloads, varying machine utilization, communication and, balance degree of the applications, multiprogramming level, and job size. Results showed that it is possible to offer malleability over MPI jobs. An application obtained better performance when contending for the resources with itself than with other applications, especially in workloads with high machine utilization. Load imbalance was taken into account obtaining better performance if applying the right queue type to each application independently.The job scheduling policy proposed exploited virtual malleability by choosing at the beginning of execution some parameters like the number of processes and maximum multiprogramming level. It performed well under bursty workloads with low to medium machine utilizations. However as the load increases, virtual malleability was not enough. That is because, when the machine is heavily loaded, the jobs, once shrunk are not able to expand, so they must be executed all the time with a partition smaller than the job size, thus degrading performance. Thus, at this point the job scheduling policy concentrated just in moldability.Fragmentation was alleviated also by applying backfilling techniques to the job scheduling algorithm. Virtual malleability showed to be an interesting improvement in the window expiring problem. Backfilled jobs even on a smaller partition, can continue execution reducing memory swapping generated by aborts/suspensions In this way the queueing system is prevented from reinserting the backfilled job in the queue and re-executing it in the future.Postprint (published version