PRISM: an intelligent adaptation of prefetch and SMT levels

Current microprocessors include hardware to optimize some specifics workloads. In general, these hardware knobs are set on a default configuration on the booting process of the machine. This default behavior cannot be beneficial for all types of workloads and they are not controlled by anyone but the end user, who needs to know what configuration is the best one for the workload running. Some of these knobs are: (1) the Simultaneous MultiThreading level, which specifies the number of threads that can run simultaneously on a physical CPU, and (2) the data prefetch engine, that manages the prefetches on memory. Parallel programming models are here to stay, and one programming model that succeed in allowing programmers to easily parallelize applications is Open Multi Processing (OMP). Also, the architecture of microprocessors is getting more complex that end users cannot afford to optimize their workloads for all the architectural details. These architectural knobs can help to increase performance but it is needed an automatic and adaptive system managing them. In this work we propose an independent library for OpenMP runtimes to increase performance up to 220% (14.7% on average) while reducing dynamic power consumption up to 13% (2% on average) on a real POWER8 processor

"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

An Infrastructure for the Analysis of Communication Patterns in Virtual Topologies

The virtual topology of a parallel application is the neighborhood relationship between communicating processes developed due to specific communication patterns resulting from domain decomposition. We present an infrastructure that allows the usage of topological information for the performance analysis of a parallel application. For this purpose we have implemented an easy to use extension of the KOJAK performance analysis toolkit. The KOJAK toolkit defines communication patterns for parallel applications which describe inefficient behavior. The performance analysis is carried out by calculating the effect of these inefficiency patterns on the application\u27s performance. The distribution of these inefficiency patterns is studied across a three-dimensional performance space. The knowledge of virtual topology can be exploited to explain the occurrence of these inefficiency patterns in terms of higher-level events related to the parallel algorithm implemented in the application. Also, it can be used to visualize the relationships between pattern occurrences and the topological characteristics of the affected processes, To prove these principles, we have used our extensions to KOJAK to analyze two realistic MPI applications

Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs

Hybrid parallel programming models that combine message passing (MP) and shared- memory multithreading (MT) are becoming more popular, especially with applications requiring higher degrees of parallelism and scalability. Consequently, coupled parallel programs, those built via the integration of independently developed and optimized software libraries linked into a single application, increasingly comprise message-passing libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult, and the challenge is exacerbated because contemporary middleware services provide only static scheduling policies over entire program executions, necessitating suboptimal, over-subscribed or under-subscribed, configurations. In coupled applications, a poorly configured component can lead to overall poor application performance, suboptimal resource utilization, and increased time-to-solution. So it is critical that each library executes in a manner consistent with its design and tuning for a particular system architecture and workload. Therefore, there is a need for techniques that address dynamic, conflicting configurations in coupled multithreaded message-passing (MT-MP) programs. Our thesis is that we can achieve significant performance improvements over static under-subscribed approaches through reconfigurable execution environments that consider compute phase parallelization strategies along with both hardware and software characteristics. In this work, we present new ways to structure, execute, and analyze coupled MT- MP programs. Our study begins with an examination of contemporary approaches used to accommodate thread-level heterogeneity in coupled MT-MP programs. Here we identify potential inefficiencies in how these programs are structured and executed in the high-performance computing domain. We then present and evaluate a novel approach for accommodating thread-level heterogeneity. Our approach enables full utilization of all available compute resources throughout an application’s execution by providing programmable facilities with modest overheads to dynamically reconfigure runtime environments for compute phases with differing threading factors and affinities. Our performance results show that for a majority of the tested scientific workloads our approach and corresponding open-source reference implementation render speedups greater than 50 % over the static under-subscribed baseline. Motivated by our examination of reconfigurable execution environments and their memory overhead, we also study the memory attribution problem: the inability to predict or evaluate during runtime where the available memory is used across the software stack comprising the application, reusable software libraries, and supporting runtime infrastructure. Specifically, dynamic adaptation requires runtime intervention, which by its nature introduces additional runtime and memory overhead. To better understand the latter, we propose and evaluate a new way to quantify component-level memory usage from unmodified binaries dynamically linked to a message-passing communication library. Our experimental results show that our approach and corresponding implementation accurately measure memory resource usage as a function of time, scale, communication workload, and software or hardware system architecture, clearly distinguishing between application and communication library usage at a per-process level

"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

Task Packing: Efficient task scheduling in unbalanced parallel programs to maximize CPU utilization

Load imbalance in parallel systems can be generated by external factors to the currently running applications like operating system noise or the underlying hardware like a heterogeneous cluster. HPC applications working on irregular data structures can also have difficulties to balance their computations across the parallel tasks. In this article we extend, improve and evaluate more deeply the Task Packing mechanism proposed in a previous work. The main idea of the mechanism is to concentrate the idle cycles of unbalanced applications in such a way that one or more CPUs are freed from execution. To achieve this, CPUs are stressed with just useful work of the parallel application tasks, provided performance is not degraded. The packing is solved by an algorithm based on the Knapsack problem, in a minimum number of CPUs and using oversubscription. We design and implement a more efficient version of such mechanism. To that end, we perform the Task Packing “in place”, taking advantage of idle cycles generated at synchronization points of unbalanced applications. Evaluations are carried out on a heterogeneous platform using FT and miniFE benchmarks. Results showed that our proposal generates low overhead. In addition the amount of freed CPUs are related to a load imbalance metric which can be used as a prediction for it.Peer ReviewedPostprint (author's final draft

Simulating whole supercomputer applications

Architecture simulation tools are extremely useful not only to predict the performance of future system designs, but also to analyze and improve the performance of software running on well know architectures. However, since power and complexity issues stopped the progress of single-thread performance, simulation speed no longer scales with technology: systems get larger and faster, but simulators do not get any faster. Detailed simulation of full-scale applications running on large clusters with hundreds or thousands of processors is not feasible. In this paper we present a methodology that allows detailed simulation of large-scale MPI applications running on systems with thousands of processors with low resource cost. Our methodology allows detailed processor simulation, from the memory and cache hierarchy down to the functional units and the pipeline structure. This feature enables software performance analysis beyond what performance counters would allow. In addition, it enables performance prediction targeting non-existent architectures and systems, that is, systems for which no performance data can be used as a reference. For example, detailed analysis of the weather forecasting application WRF reveals that it is highly optimized for cache locality, and is strongly compute bound, with faster functional units having the greatest impact on its performance. Also, analysis of next-generation CMP clusters show that performance may start to decline beyond 8 processors per chip due to shared resource contention, regardless of the benefits of through-memory communication.Postprint (published version

Performance Improvement of Multithreaded Java Applications Execution on Multiprocessor Systems

El disseny del llenguatge Java, que inclou aspectes importants com són la seva portabilitat i neutralitat envers l'arquitectura, les seves capacitats multithreading, la seva familiaritat (degut a la seva semblança amb C/C++), la seva robustesa, les seves capacitats en seguretat i la seva naturalesa distribuïda, fan que sigui un llenguatge potencialment interessant per ser utilitzat en entorns paral·lels com són els entorns de computació d'altes prestacions (HPC), on les aplicacions poden treure profit del suport que ofereix Java a l'execució multithreaded per realitzar càlculs en paral·lel, o en entorns e-business, on els servidors Java multithreaded (que segueixen l'especificació J2EE) poden treure profit de les capacitats multithreading de Java per atendre de manera concurrent un gran nombre de peticions.No obstant, l'ús de Java per la programació paral·lela ha d'enfrontar-se a una sèrie de problemes que fàcilment poden neutralitzar el guany obtingut amb l'execució en paral·lel. El primer problema és el gran overhead provocat pel suport de threads de la JVM quan s'utilitzen threads per executar feina de gra fi, quan es crea un gran nombre de threads per suportar l'execució d'una aplicació o quan els threads interaccionen estretament mitjançant mecanismes de sincronització. El segon problema és la degradació en el rendiment produïda quan aquestes aplicacions multithreaded s'executen en sistemes paral·lels multiprogramats. La principal causa d'aquest problemes és la manca de comunicació entre l'entorn d'execució i les aplicacions, la qual pot induir a les aplicacions a fer un ús descoordinat dels recursos disponibles.Aquesta tesi contribueix amb la definició d'un entorn per analitzar i comprendre el comportament de les aplicacions Java multithreaded. La contribució principal d'aquest entorn és que la informació de tots els nivells involucrats en l'execució (aplicació, servidor d'aplicacions, JVM i sistema operatiu) està correlada. Aquest fet és molt important per entendre com aquest tipus d'aplicacions es comporten quan s'executen en entorns que inclouen servidors i màquines virtuals, donat que l'origen dels problemes de rendiment es pot trobar en qualsevol d'aquests nivells o en la seva interacció.Addicionalment, i basat en el coneixement adquirit mitjançant l'entorn d'anàlisis proposat, aquesta tesi contribueix amb mecanismes i polítiques de planificació orientats cap a l'execució eficient d'aplicacions Java multithreaded en sistemes multiprocessador considerant les interaccions i la coordinació dels mecanismes i les polítiques de planificació en els diferents nivells involucrats en l'execució. La idea bàsica consisteix en permetre la cooperació entre les aplicacions i l'entorn d'execució en la gestió de recursos establint una comunicació bi-direccional entre les aplicacions i el sistema. Per una banda, les aplicacions demanen a l'entorn d'execució la quantitat de recursos que necessiten. Per altra banda, l'entorn d'execució pot ser inquirit en qualsevol moment per les aplicacions ser informades sobre la seva assignació de recursos. Aquesta tesi proposa que les aplicacions utilitzin la informació proporcionada per l'entorn d'execució per adaptar el seu comportament a la quantitat de recursos que tenen assignats (aplicacions auto-adaptables). Aquesta adaptació s'assoleix en aquesta tesi per entorns HPC per mitjà de la mal·leabilitat de les aplicacions, i per entorns e-business amb una proposta de control de congestió que fa control d'admissió basat en la diferenciació de connexions SSL per prevenir la degradació del rendiment i mantenir la Qualitat de Servei (QoS).Els resultats de l'avaluació demostren que subministrar recursos de manera dinàmica a les aplicacions auto-adaptables en funció de la seva demanda millora el rendiment de les aplicacions Java multithreaded tant en entorns HPC com en entorns e-business. Mentre disposar d'aplicacions auto-adaptables evita la degradació del rendiment, el subministrament dinàmic de recursos permet satisfer els requeriments de les aplicacions en funció de la seva demanda i adaptar-se a la variabilitat de les seves necessitats de recursos. D'aquesta manera s'aconsegueix una millor utilització dels recursos donat que els recursos que no utilitza una aplicació determinada poden ser distribuïts entre les altres aplicacions.The design of the Java language, which includes important aspects such as its portability and architecture neutrality, its multithreading facilities, its familiarity (due to its resemblance with C/C++), its robustness, its security capabilities and its distributed nature, makes it a potentially interesting language to be used in parallel environments such as high performance computing (HPC) environments, where applications can benefit from the Java multithreading support for performing parallel calculations, or e-business environments, where multithreaded Java application servers (i.e. following the J2EE specification) can take profit of Java multithreading facilities to handle concurrently a large number of requests.However, the use of Java for parallel programming has to face a number of problems that can easily offset the gain due to parallel execution. The first problem is the large overhead incurred by the threading support available in the JVM when threads are used to execute fine-grained work, when a large number of threads are created to support the execution of the application or when threads closely interact through synchronization mechanisms. The second problem is the performance degradation occurred when these multithreaded applications are executed in multiprogrammed parallel systems. The main issue that causes these problems is the lack of communication between the execution environment and the applications, which can cause these applications to make an uncoordinated use of the available resources.This thesis contributes with the definition of an environment to analyze and understand the behavior of multithreaded Java applications. The main contribution of this environment is that all levels in the execution (application, application server, JVM and operating system) are correlated. This is very important to understand how this kind of applications behaves when executed on environments that include servers and virtual machines, because the origin of performance problems can reside in any of these levels or in their interaction.In addition, and based on the understanding gathered using the proposed analysis environment, this thesis contributes with scheduling mechanisms and policies oriented towards the efficient execution of multithreaded Java applications on multiprocessor systems considering the interactions and coordination between scheduling mechanisms and policies at the different levels involved in the execution. The basis idea consists of allowing the cooperation between the applications and the execution environment in the resource management by establishing a bi-directional communication path between the applications and the underlying system. On one side, the applications request to the execution environment the amount of resources they need. On the other side, the execution environment can be requested at any time by the applications to inform them about their resource assignments. This thesis proposes that applications use the information provided by the execution environment to adapt their behavior to the amount of resources allocated to them (self-adaptive applications). This adaptation is accomplished in this thesis for HPC environments through the malleability of the applications, and for e-business environments with an overload control approach that performs admission control based on SSL connections differentiation for preventing throughput degradation and maintaining Quality of Service (QoS).The evaluation results demonstrate that providing resources dynamically to self-adaptive applications on demand improves the performance of multithreaded Java applications as in HPC environments as in e-business environments. While having self-adaptive applications avoids performance degradation, dynamic provision of resources allows meeting the requirements of the applications on demand and adapting to their changing resource needs. In this way, better resource utilization is achieved because the resources not used by some application may be distributed among other applications

Doctor of Philosophy in Computing

dissertationThe aim of direct volume rendering is to facilitate exploration and understanding of three-dimensional scalar fields referred to as volume datasets. Improving understanding is done by improving depth perception, whereas facilitating exploration is done by speeding up volume rendering. In this dissertation, improving both depth perception and rendering speed is considered. The impact of depth of field (DoF) on depth perception in direct volume rendering is evaluated by conducting a user study in which the test subjects had to choose which of two features, located at different depths, appeared to be in front in a volume-rendered image. Whereas DoF was expected to improve perception in all cases, the user study revealed that if used on the back feature, DoF reduced depth perception, whereas it produced a marked improvement when used on the front feature. We then worked on improving the speed of volume rendering on distributed memory machines. Distributed volume rendering has three stages: loading, rendering, and compositing. In this dissertation, the focus is on image compositing, more specifically, trying to optimize communication in image compositing algorithms. For that, we have developed the Task Overlapped Direct Send Tree image compositing algorithm, which works on both CPU- and GPU-accelerated supercomputers, which focuses on communication avoidance and overlapping communication with computation; the Dynamically Scheduled Region-Based image compositing algorithm that uses spatial and temporal awareness to efficiently schedule communication among compositing nodes, and a rendering and compositing pipeline that allows both image compositing and rendering to be done on GPUs of GPU-accelerated supercomputers. We tested these on CPU- and GPU-accelerated supercomputers and explain how these improvements allow us to obtain better performance than image compositing algorithms that focus on load-balancing and algorithms that have no spatial and temporal awareness of the rendering and compositing stages

Real-time high-performance computing for embedded control systems

The real-time control systems industry is moving towards the consolidation of multiple computing systems into fewer and more powerful ones, aiming for a reduction in size, weight, and power. The increasing demand for higher performance in other critical domains like autonomous driving has led the industry to recently include embedded GPUs for the implementation of advanced functionalities. The highly parallel architecture of GPUs could also be leveraged in the control systems industry to develop more advanced, energy-efficient, and scalable control systems. However, the closed-source and non-deterministic nature of GPUs complicates the resource provisioning analysis required for the implementation of critical real-time systems. On the other hand, there is no indication of the integration of GPUs in the traditional development cycle of control systems, which is oriented to the use of a model-based design approach. Recently, some model-based design tools vendors have extended their development frameworks with GPU code generation capabilities targeting hybrid computing platforms, so that the model-based design environment now enables the concurrent analysis of more complex and diverse functions by simulation and automating the deployment to the final target. However, there is no indication whether these tools are well-suited for the design and development of time-sensitive systems. Motivated by these challenges, in this thesis, we contribute to the state of the art of real-time control systems towards the adoption of embedded GPUs by providing tools to facilitate the resource provisioning analysis and the integration in the model-based design development cycle. First, we present a methodology and an automated tool to extract the properties of GPU memory allocators. This tool allows the computation of the real amount of memory used by GPU applications, facilitating a correct resource provisioning analysis. Then, we present a library which allows the characterization of the use of dynamic memory in GPU applications. We use this library to characterize GPU benchmarks and we identify memory allocation patterns that could be modified to improve performance and memory consumption when targeting embedded GPUs. Based on these results, we present a tool to optimize the use of dynamic memory in legacy GPU applications executed on embedded platforms. This tool allows us to minimize the memory consumption and memory management overhead of GPU applications without rewriting them. Afterwards, we analyze the timing of control algorithms executed in embedded GPUs and we identify techniques to achieve an acceptable real-time behavior. Finally, we evaluate model-based design tools in terms of integration with GPU hardware and GPU code generation, and we propose improvements for the model-based generated GPU code. Then, we present a source-to-source transformation tool to automatically apply the proposed improvements.La industria de los sistemas de control en tiempo real avanza hacia la consolidación de múltiples sistemas informáticos en menos y más potentes sistemas, con el objetivo de reducir el tamaño, el peso y el consumo. La creciente demanda de un mayor rendimiento en otros dominios críticos, como la conducción autónoma, ha llevado a la industria a incluir recientemente GPU embebidas para la implementación de funcionalidades avanzadas. La arquitectura altamente paralela de las GPU también podría aprovecharse en la industria de los sistemas de control para desarrollar sistemas de control más avanzados, eficientes energéticamente y escalables. Sin embargo, la naturaleza privativa y no determinista de las GPUs complica el análisis de aprovisionamiento de recursos requerido para la implementación de sistemas críticos en tiempo real. Por otro lado, no hay indicios de la integración de las GPU en el ciclo de desarrollo tradicional de los sistemas de control, que está orientado al uso de un enfoque de diseño basado en modelos. Recientemente, algunos proveedores de herramientas de diseño basado en modelos han ampliado sus entornos de desarrollo con capacidades de generación de código de GPU dirigidas a plataformas informáticas híbridas, de modo que el entorno de diseño basado en modelos ahora permite el análisis simultáneo de funciones más complejas y diversas mediante la simulación y la automatización de la implementación para el objetivo final. Sin embargo, no hay indicación de si estas herramientas son adecuadas para el diseño y desarrollo de sistemas sensibles al tiempo. Motivados por estos desafíos, en esta tesis contribuimos al estado del arte de los sistemas de control en tiempo real hacia la adopción de GPUs integradas al proporcionar herramientas para facilitar el análisis de aprovisionamiento de recursos y la integración en el ciclo de desarrollo de diseño basado en modelos. Primero, presentamos una metodología y una herramienta automatizada para extraer las propiedades de los asignadores de memoria en GPUs. Esta herramienta permite el cómputo de la cantidad real de memoria utilizada por las aplicaciones GPU, facilitando un correcto análisis del aprovisionamiento de recursos. Luego, presentamos una librería que permite la caracterización del uso de memoria dinámica en aplicaciones de GPU. Usamos esta librería para caracterizar una serie de benchmarks GPU e identificamos patrones de asignación de memoria que podrían modificarse para mejorar el rendimiento y el consumo de memoria al utilizar GPUs embebidas. Con base en estos resultados, presentamos también una herramienta para optimizar el uso de la memoria dinámica en aplicaciones de GPU heredadas al ser ejecutadas en plataformas embebidas. Esta herramienta nos permite minimizar el consumo de memoria y la sobrecarga de administración de memoria de las aplicaciones GPU sin necesidad de reescribirlas. Posteriormente, analizamos el tiempo de los algoritmos de control ejecutados en GPUs embebidas e identificamos técnicas para lograr un comportamiento de tiempo real aceptable. Finalmente, evaluamos las herramientas de diseño basadas en modelos en términos de integración con hardware GPU y generación de código GPU, y proponemos mejoras para el código GPU generado por las herramientas basadas en modelos. Luego, presentamos una herramienta de transformación de código fuente para aplicar automáticamente al código generado las mejoras propuestas.Postprint (published version