Iterative Compilation and Performance Prediction for Numerical Applications

Institute for Computing Systems ArchitectureAs the current rate of improvement in processor performance far exceeds the rate of memory performance, memory latency is the dominant overhead in many performance critical applications. In many cases, automatic compiler-based approaches to improving memory performance are limited and programmers frequently resort to manual optimisation techniques. However, this process is tedious and time-consuming. Furthermore, a diverse range of a rapidly evolving hardware makes the optimisation process even more complex. It is often hard to predict the potential benefits from different optimisations and there are no simple criteria to stop optimisations i.e. when optimal memory performance has been achieved or sufficiently approached. This thesis presents a platform independent optimisation approach for numerical applications based on iterative feedback-directed program restructuring using a new reasonably fast and accurate performance prediction technique for guiding optimisations. New strategies for searching the optimisation space, by means of profiling to find the best possible program variant, have been developed. These strategies have been evaluated using a range of kernels and programs on different platforms and operating systems. A significant performance improvement has been achieved using new approaches when compared to the state-of-the-art native static and platform-specific feedback directed compilers

Miss Rate Prediction across All Program Inputs

Improving cache performance requires understanding cache behavior. However, measuring cache performance for one or two data input sets provides little insight into how cache behavior varies across all data input sets. This paper uses our recently published locality analysis to generate a parameterized model of program cache behavior. Given a cache size and associativity, this model predicts the miss rate for arbitrary data input set sizes. This model also identifies critical data input sizes where cache behavior exhibits marked changes. Experiments show this technique is within 2% of the hit rate for set associative caches on a set of integer and floating-point programs

Pinpointing Software Inefficiencies With Profiling

Complex codebases with several layers of abstractions have abundant inefficiencies that affect the performance. These inefficiencies arise due to various causes such as developers\u27 inattention to performance, inappropriate choice of algorithms and inefficient code generation among others. To eliminate the redundancies, lots of work has been done during the compiling phase. However, not all redundancies can be easily detected or eliminated with compiler optimization passes due to aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. There are also profiling tools which can reveal how resources are used. However, they can hard to distinguish whether the resource is worth fully used. More profiling tools are in needed to diagnose resource wastage and pinpoint inefficiencies. We have developed three tools to pinpoint different types of inefficiencies in different granularity. We build Runtime Value Numbering (RVN), a dynamic fine-grained profiler to pinpoint and quantify redundant computations in an execution. It is based on the classical value numbering technique but works at runtime instead of compile-time. We developed RedSpy, a fine-grained profiler to pinpoint and quantify value redundancies in program executions. Value redundancy may happen overtime at the same locations or in adjacent locations, and thus it has temporal and spatial locality. RedSpy identifies both temporal and spatial value locality. Furthermore, RedSpy is capable of identifying values that are approximately the same, enabling optimization opportunities in HPC codes that often use floating-point computations. RVN and RedSpy are both instrumentation based tools. They provide comprehensive result while introducing high space and time overhead. Our lightweight framework, Witch, samples consecutive accesses to the same memory location by exploiting two ubiquitous hardware features: the performance monitoring units (PMU) and debug registers. Witch performs no instrumentation. Hence, witchcraft - tools built atop Witch - can detect a variety of software inefficiencies while introducing negligible slowdown and insignificant memory consumption and yet maintaining accuracy comparable to exhaustive instrumentation tools. Witch allowed us to scale our analysis to a large number of codebases. All the tools work on fully optimized binary executable and provide insightful optimization guidance by apportioning redundancies to their provenance - source lines and full calling contexts. We apply RVN, RedSpy, and Witch on programs that were optimization targets for decades and guided by the tools, we were able to eliminate redundancies that resulted in significant speedups

Smart memory management through locality analysis

Las memorias caché fueron incorporadas en los microprocesadores ya desde los primeros tiempos, y representan la solución más común para tratar la diferencia de velocidad entre el procesador y la memoria. Sin embargo, muchos estudios señalan que la capacidad de almacenamiento de la caché es malgastada muchas veces, lo cual tiene un impacto directo en el rendimiento del procesador. Aunque una caché está diseñada para explotar diferentes tipos de localidad, todas la referencias a memoria son tratadas de la misma forma, ignorando comportamientos particulares de localidad. El uso restringido de la información de localidad para cada acceso a memoria puede limitar la eficiencia de la cache. En esta tesis se demuestra como un análisis de localidad de datos puede ayudar al investigador a entender dónde y porqué ocurren los fallos de caché, y proponer entonces diferentes técnicas que hacen uso de esta información con el objetivo de mejorar el rendimiento de la memoria caché. Proponemos técnicas en las cuales la información de localidad obtenida por el analizador de localidad es pasada desde el compilador al hardware a través del ISA para guiar el manejo de los accesos a memoria.Hemos desarrollado un análisis estático de localidad de datos. Este análisis está basado en los vectores de reuso y contiene los tres típicos pasos: reuso, volumen y análisis de interferencias. Comparado con trabajos previos, tanto el análisis de volúmenes como el de interferencias ha sido mejorado utilizando información de profiling así como un análisis de interferencias más preciso. El analizador de localidad de datos propuesto ha sido incluido como un paso más en un compilador de investigación. Los resultados demuestran que, para aplicaciones numéricas, el análisis es muy preciso y el overhead de cálculo es bajo. Este análisis es la base para todas las otras partes de la tesis. Además, para algunas propuestas en la última parte de la tesis, hemos usado un análisis de localidad de datos basado en las ecuaciones de fallos de cache. Este análisis, aunque requiere más tiempo de cálculo, es más preciso y más apropiado para cachés asociativas por conjuntos. El uso de dos análisis de localidad diferentes también demuestra que las propuestas arquitectónicas de esta tesis son independientes del análisis de localidad particular utilizado.Después de mostrar la precisión del análisis, lo hemos utilizado para estudiar el comportamiento de localidad exhibido por los programas SPECfp95. Este tipo de análisis es necesario antes de proponer alguna nueva técnica ya que ayuda al investigador a entender porqué ocurren los fallos de caché. Se muestra que con el análisis propuesto se puede estudiar de forma muy precisa la localidad de un programa y detectar donde estan los "puntos negros" así como la razón de estos fallos en cache. Este estudio del comportamiento de localidad de diferentes programas es la base y motivación para las diferentes técnicas propuestas en esta tesis para mejorar el rendimiento de la memoria.Así, usando el análisis de localidad de datos y basándonos en los resultados obtenidos después de analizar el comportamiento de localidad de un conjunto de programas, proponemos utilizar este análisis con el objetivo de guiar tres técnicas diferentes: (i) manejo de caches multimódulo, (ii) prebúsqueda software para bucles con planificación módulo, y (iii) planificación de instrucciones de arquitecturas VLIW clusterizadas.El primer uso del análisis de localidad propuesto es el manejo de una novedosa organización de caché. Esta caché soporta bypass y/o está compuesta por diferentes módulos, cada uno orientado a explotar un tipo particular de localidad. La mayor diferencia de esta caché con respecto propuestas previas es que la decisión de "cachear" o no, o en qué módulo un nuevo bloque es almacenado, está controlado por algunos bits en las instrucciones de memoria ("pistas" de localidad). Estas "pistas" (hints) son fijadas en tiempo de compilación utilizando el análisis de localidad propuesto. Así, la complejidad del manejo de esta caché se mantiene bajo ya que no requiere ningún hardware adicional. Los resultados demuestran que cachés más pequeñas con un manejo más inteligente pueden funcionar tan bien (o mejor) que cachés convencionales más grandes.Hemos utilizado también el análisis de localidad para estudiar la interacción entre la segmentación software y la prebúsqueda software. La segmentación software es una técnica muy efectiva para la planificación de código en bucles (principalmente en aplicaciones numéricas en procesadores VLIW). El esquema más popular de prebúsqueda software se llama planificación módulo. Muchos trabajos sobre planificación módulo se pueden encontrar en la literatura, pero casi todos ellos consideran una suposición crítica: consideran un comportamiento optimista de la cache (en otras palabras, usan siempre la latencia de acierto cuando planifican instrucciones de memoria). Así, los resultados que presentan ignoran los efectos del bloqueo debido a dependencias con instrucciones de memoria. En esta parte de la tesis mostramos que esta suposición puede llevar a planificaciones cuyo rendimiento es bastante más bajo cuando se considera una memoria real. Nosotros proponemos un algoritmo para planificar instrucciones de memoria en bucles con planificación módulo. Hemos estudiado diferentes estrategias de prebúsqueda software y finalmente hemos propuesto un algoritmo que realiza prebúsqueda basándose en el análisis de localidad y en la forma del grafo de dependencias del bucle. Los resultados obtenidos demuestran que el esquema propuesto mejora el rendimiento de las otras heurísticas ya que obtiene un mejor compromiso entre tiempo de cálculo y de bloqueo.Finalmente, el último uso del análisis de localidad estudiado en esta tesis es para guiar un planificador de instrucciones para arquitecturas VLIW clusterizadas. Las arquitecturas clusterizadas están siendo una tendencia común en el diseño de procesadores empotrados/DSP. Típicamente, el núcleo de estos procesadores está basado en un diseño VLIW el cual particiona tanto el banco de registros como las unidades funcionales. En este trabajo vamos un paso más allá y también hacemos la partición de la memoria caché. En este caso, tanto las comunicaciones entre registros como entre memorias han de ser consideradas. Nosotros proponemos un algoritmo que realiza la partición del grafo así como la planificación de instrucciones en un único paso en lugar de hacerlo secuencialmente, lo cual se demuestra que es más efectivo. Este algoritmo es mejorado añadiendo una análisis basado en las ecuaciones de fallos de cache con el objetivo de guiar en la planificación de las instrucciones de memoria para reducir no solo comunicaciones entre registros, sino también fallos de cache.Cache memories were incorporated in microprocessors in the early times and represent the most common solution to deal with the gap between processor and memory speeds. However, many studies point out that the cache storage capacity is wasted many times, which means a direct impact in processor performance. Although a cache is designed to exploit different types of locality, all memory references are handled in the same way, ignoring particular locality behaviors. The restricted use of the locality information for each memory access can limit the effectivity of the cache. In this thesis we show how a data locality analysis can help the researcher to understand where and why cache misses occur, and then to propose different techniques that make use of this information in order to improve the performance of cache memory. We propose techniques in which locality information obtained by the locality analyzer is passed from the compiler to the hardware through the ISA to guide the management of memory accesses.We have developed a static data locality analysis. This analysis is based on reuse vectors and performs the three typical steps: reuse, volume and interfere analysis. Compared with previous works, both volume and interference analysis have been improved by using profile information as well as a more precise inter-ference analysis. The proposed data locality analyzer has been inserted as another pass in a research compiler. Results show that for numerical applications the analysis is very accurate and the computing overhead is low. This analysis is the base for all other parts of the thesis. In addition, for some proposals in the last part of the thesis we have used a data locality analysis based on cache miss equations. This analysis, although more time consuming, is more accurate and more appropriate for set-associative caches. The usage of two different locality analyzers also shows that the architectural proposals of this thesis are independent from the particular locality analysis.After showing the accuracy of the analysis, we have used it to study the locality behavior exhibited by the SPECfp95 programs. This kind of analysis is necessary before proposing any new technique since can help the researcher to understand why cache misses occur. We show that with the proposed analysis we can study very accurately the locality of a program and detect where the hot spots are as well as the reason for these misses. This study of the locality behavior of different programs is the base and motivation for the different techniques proposed in this thesis to improve the memory performance.Thus, using the data locality analysis and based on the results obtained after analyzing the locality behavior of a set of programs, we propose to use this analysis in order to guide three different techniques: (i) management of multi-module caches, (ii) software prefetching for modulo scheduled loops, and (iii) instruction scheduling for clustered VLIW architectures.The first use of the proposed data locality analysis is to manage a novel cache organization. This cache supports bypassing and/or is composed of different modules, each one oriented to exploit a particular type of locality. The main difference of this cache with respect to previous proposals is that the decision of caching or not, or in which module a new fetched block is allocated is managed by some bits in memory instructions (locality hints). These hints are set at compile time using the proposed locality analysis. Thus, the management complexity of this cache is kept low since no additional hardware is required. Results show that smaller caches with a smart management can perform as well as (or better than) bigger conventional caches.We have also used the locality analysis to study the interaction between software pipelining and software prefetching. Software pipelining has been shown to be a very effective scheduling technique for loops (mainly in numerical applications for VLIW processors). The most popular scheme for software pipelining is called modulo scheduling. Many works on modulo scheduling can be found in the literature, but almost all of them make a critical assumption: they consider an optimistic behavior of the cache (in other words, they use the hit latency when a memory instruction is scheduled). Thus, the results they present ignore the effect of stalls due to dependences with memory instructions. In this part of the thesis we show that this assumption can lead to schedules whose performance is rather low when a real memory is considered. Thus, we propose an algorithm to schedule memory instructions in modulo scheduled loops. We have studied different software prefetching strategies and finally proposed an algorithm that performs prefetching based on the locality analysis and the shape of the loop dependence graph. Results obtained shows that the proposed scheme outperforms other heuristic approaches since it achieves a better trade-off between compute and stall time than the others. Finally, the last use of the locality analysis studied in this thesis is to guide an instruction scheduler for a clustered VLIW architecture. Clustered architectures are becoming a common trend in the design of embedded/DSP processors. Typically, the core of these processors is based on a VLIW design which partitionates both register file and functional units. In this work we go a step beyond and also make a partition of the cache memory. Then, both inter-register and inter-memory communications have to be taken into account. We propose an algorithm that performs both graph partition and instruction scheduling in a single step instead of doing it sequentially, which is shown to be more effective. This algorithm is improved by adding an analysis based on the cache miss equations in order to guide the scheduling of memory instructions in clusters with the aim of reducing not only inter-register communications, but also cache misses

ILP-based energy minimization techniques for banked memories

Main memories can consume a significant portion of overall energy in many data-intensive embedded applications. One way of reducing this energy consumption is banking, that is, dividing available memory space into multiple banks and placing unused (idle) memory banks into low-power operating modes. Prior work investigated code-restructuring- and data-layout-reorganization-based approaches for increasing the energy benefits that could be obtained from a banked memory architecture. This article explores different techniques that can potentially coexist within the same optimization framework for maximizing benefits of low-power operating modes. These techniques include employing nonuniform bank sizes, data migration, data compression, and data replication. By using these techniques, we try to increase the chances for utilizing low-power operating modes in a more effective manner, and achieve further energy savings over what could be achieved by exploiting low-power modes alone. Specifically, nonuniform banking tries to match bank sizes with application-data access patterns. The goal of data migration is to cluster data with similar access patterns in the same set of banks. Data compression reduces the size of the data used by an application, and thus helps reduce the number of memory banks occupied by data. Finally, data replication increases bank idleness by duplicating select read-only data blocks across banks. We formulate each of these techniques as an ILP (integer linear programming) problem, and solve them using a commercial solver. Our experimental analysis using several benchmarks indicates that all the techniques presented in this framework are successful in reducing memory energy consumption. Based on our experience with these techniques, we recommend to compiler writers for banked memories to consider data compression, replication, and migration. © 2008 ACM

Modeling Data Center Co-Tenancy Performance Interference

A multi-core machine allows executing several applications simultaneously. Those jobs are scheduled on different cores and compete for shared resources such as the last level cache and memory bandwidth. Such competitions might cause performance degradation. Data centers often utilize virtualization to provide a certain level of performance isolation. However, some of the shared resources cannot be divided, even in a virtualized system, to ensure complete isolation. If the performance degradation of co-tenancy is not known to the cloud administrator, a data center often has to dedicate a whole machine for a latency-sensitive application to guarantee its quality of service. Co-run scheduling attempts to make good utilization of resources by scheduling compatible jobs into one machine while maintaining their service level agreements. An ideal co-run scheduling scheme requires accurate contention modeling. Recent studies for co-run modeling and scheduling have made steady progress to predict performance for two co-run applications sharing a specific system. This thesis advances co-tenancy modeling in three aspects. First, with an accurate co-run modeling for one system at hand, we propose a regression model to transfer the knowledge and create a model for a new system with different hardware configuration. Second, by examining those programs that yield high prediction errors, we further leverage clustering techniques to create a model for each group of applications that show similar behavior. Clustering helps improve the prediction accuracy of those pathological cases. Third, existing research is typically focused on modeling two application co-run cases. We extend a two-core model to a three- and four-core model by introducing a light-weight micro-kernel that emulates a complicated benchmark through program instrumentation. Our experimental evaluation shows that our cross-architecture model achieves an average prediction error less than 2% for pairwise co-runs across the SPECCPU2006 benchmark suite. For more than two application co-tenancy modeling, we show that our model is more scalable and can achieve an average prediction error of 2-3%

A language and a system for program optimization

Hardware complexity has increased over time, and as architectures evolve and new ones are adopted, programs must often be altered by numerous optimizations to attain maximum computing power on each target environment. As a result, the code becomes unrecognizable over time, hard to maintain, and challenging to modify. Furthermore, as the code evolves, it is hard to keep the optimizations up to date. The need to develop and maintain separate versions of the application for each target platform is an immense undertaking, especially for the large and long-lived applications commonly found in the high-performance computing (HPC) community. This dissertation presents Locus, a new system, and a language for optimizing complex, long-lived applications for different platforms. We describe the requirements that we believe are necessary for making automatic performance tuning widely adopted. We present the design and implementation of a system that fulfills these requirements. It includes a domain-specific language that can represent complex collections of transformations, an interface to integrate external modules, and a database to manage platform-specific efficient code. The database allows the system’s users to access optimized code without having to install the code generation toolset. The Locus language allows the definition of a search space combined with the programming of optimization sequences separated from the application’s reference code. After all, we present an approach for performance portability. Our thesis is that we can ameliorate the difficulty of optimizing applications using a methodology based on optimization programming and automated empirical search. Our system automatically selects, generates, and executes candidate implementations to find the one with the best performance. We present examples to illustrate the power and simplicity of the language. The experimental evaluation shows that exploring the space of candidate implementations typically leads to better performing codes than those produced by conventional compiler optimizations that are based solely on heuristics. Locus was able to generate a matrix-matrix multiplication code that outperformed the IBM XLC internal hand-optimized version by 2× on the Power 9 processors. On Intel E5, Locus generates code with performance comparable to Intel MKL’s. We also improve performance relative to the reference implementation of up to 4× on stencil computations. Locus ability to integrate complex search spaces with optimization sequences can result in very complicated optimization programs. Locus compiler applies optimizations to remove from the optimization sequences unnecessary search statements making the exploration for faster implementations more accessible. We optimize matrix transpose, matrix-matrix multiplication, fast Fourier transform, symmetric eigenproblem, and sparse matrix-vector multiplication through divide and conquer. We implement three strategies using the Locus language to create search spaces to find the best shapes of the base case and the best ways of subdividing the problem. The search space representation for the divide-and-conquer strategy uses a combination of recursion and OR blocks. The Locus compiler automatically expands the recursion and ensures that the search space is correctly represented. The results showed that the empirical search was important to improve performance by generating faster base cases and finding the best splitting. We also use Locus to optimize large, complex applications. We match the performance of hand-optimized kernels of the Kripke transport code for different input data layouts. The Plascom2 multi-physics application is optimized to find the best way to use a multi-core CPU and GPU. The use of Tangram, Hydra, and OpenMP provided an interesting search space that improved performance by approximately 4.3× on ZAXPY and ZXDOTY kernels. Lastly, in a similar fashion to how a compiler works, we applied a search space representing a collection of optimization sequences to 856 loops extracted from 16 benchmarks that resulted in good performance improvements

Structured parallelism discovery with hybrid static-dynamic analysis and evaluation technique

Parallel computer architectures have dominated the computing landscape for the past two decades; a trend that is only expected to continue and intensify, with increasing specialization and heterogeneity. This creates huge pressure across the software stack to produce programming languages, libraries, frameworks and tools which will efficiently exploit the capabilities of parallel computers, not only for new software, but also revitalizing existing sequential code. Automatic parallelization, despite decades of research, has had limited success in transforming sequential software to take advantage of efficient parallel execution. This thesis investigates three approaches that use commutativity analysis as the enabler for parallelization. This has the potential to overcome limitations of traditional techniques. We introduce the concept of liveness-based commutativity for sequential loops. We examine the use of a practical analysis utilizing liveness-based commutativity in a symbolic execution framework. Symbolic execution represents input values as groups of constraints, consequently deriving the output as a function of the input and enabling the identification of further program properties. We employ this feature to develop an analysis and discern commutativity properties between loop iterations. We study the application of this approach on loops taken from real-world programs in the OLDEN and NAS Parallel Benchmark (NPB) suites, and identify its limitations and related overheads. Informed by these findings, we develop Dynamic Commutativity Analysis (DCA), a new technique that leverages profiling information from program execution with specific input sets. Using profiling information, we track liveness information and detect loop commutativity by examining the code’s live-out values. We evaluate DCA against almost 1400 loops of the NPB suite, discovering 86% of them as parallelizable. Comparing our results against dependence-based methods, we match the detection efficacy of two dynamic and outperform three static approaches, respectively. Additionally, DCA is able to automatically detect parallelism in loops which iterate over Pointer-Linked Data Structures (PLDSs), taken from wide range of benchmarks used in the literature, where all other techniques we considered failed. Parallelizing the discovered loops, our methodology achieves an average speedup of 3.6× across NPB (and up to 55×) and up to 36.9× for the PLDS-based loops on a 72-core host. We also demonstrate that our methodology, despite relying on specific input values for profiling each program, is able to correctly identify parallelism that is valid for all potential input sets. Lastly, we develop a methodology to utilize liveness-based commutativity, as implemented in DCA, to detect latent loop parallelism in the shape of patterns. Our approach applies a series of transformations which subsequently enable multiple applications of DCA over the generated multi-loop code section and match its loop commutativity outcomes against the expected criteria for each pattern. Applying our methodology on sets of sequential loops, we are able to identify well-known parallel patterns (i.e., maps, reduction and scans). This extends the scope of parallelism detection to loops, such as those performing scan operations, which cannot be determined as parallelizable by simply evaluating liveness-based commutativity conditions on their original form

Methodology for malleable applications on distributed memory systems

A la portada logo BSC(English) The dominant programming approach for scientific and industrial computing on clusters is MPI+X. While there are a variety of approaches within the node, denoted by the ``X'', Message Passing interface (MPI) is the standard for programming multiple nodes with distributed memory. This thesis argues that the OmpSs-2 tasking model can be extended beyond the node to naturally support distributed memory, with three benefits: First, at small to medium scale the tasking model is a simpler and more productive alternative to MPI. It eliminates the need to distribute the data explicitly and convert all dependencies into explicit message passing. It also avoids the complexity of hybrid programming using MPI+X. Second, the ability to offload parts of the computation among the nodes enables the runtime to automatically balance the loads in a full-scale MPI+X program. This approach does not require a cost model, and it is able to transparently balance the computational loads across the whole program, on all its nodes. Third, because the runtime handles all low-level aspects of data distribution and communication, it can change the resource allocation dynamically, in a way that is transparent to the application. This thesis describes the design, development and evaluation of OmpSs-2@Cluster, a programming model and runtime system that extends the OmpSs-2 model to allow a virtually unmodified OmpSs-2 program to run across multiple distributed memory nodes. For well-balanced applications it provides similar performance to MPI+OpenMP on up to 16 nodes, and it improves performance by up to 2x for irregular and unbalanced applications like Cholesky factorization. This work also extended OmpSs-2@Cluster for interoperability with MPI and Barcelona Supercomputing Center (BSC)'s state-of-the-art Dynamic Load Balance (DLB) library in order to dynamically balance MPI+OmpSs-2 applications by transparently offloading tasks among nodes. This approach reduces the execution time of a microscale solid mechanics application by 46% on 64 nodes and on a synthetic benchmark, it is within 10% of perfect load balancing on up to 8 nodes. Finally, the runtime was extended to transparently support malleability for pure OmpSs-2@Cluster programs and interoperate with the Resources Management System (RMS). The only change to the application is to explicitly call an API function to control the addition or removal of nodes. In this regard we additionally provide the runtime with the ability to semi-transparently save and recover part of the application status to perform checkpoint and restart. Such a feature hides the complexity of data redistribution and parallel IO from the user while allowing the program to recover and continue previous executions. Our work is a starting point for future research on fault tolerance. In summary, OmpSs-2@Cluster expands the OmpSs-2 programming model to encompass distributed memory clusters. It allows an existing OmpSs-2 program, with few if any changes, to run across multiple nodes. OmpSs-2@Cluster supports transparent multi-node dynamic load balancing for MPI+OmpSs-2 programs, and enables semi-transparent malleability for OmpSs-2@Cluster programs. The runtime system has a high level of stability and performance, and it opens several avenues for future work.(Español) El modelo de programación dominante para clusters tanto en ciencia como industria es actualmente MPI+X. A pesar de que hay alguna variedad de alternativas para programar dentro de un nodo (indicado por la "X"), el estandar para programar múltiples nodos con memoria distribuida sigue siendo Message Passing Interface (MPI). Esta tesis propone la extensión del modelo de programación basado en tareas OmpSs-2 para su funcionamiento en sistemas de memoria distribuida, destacando 3 beneficios principales: En primer lugar; a pequeña y mediana escala, un modelo basado en tareas es más simple y productivo que MPI y elimina la necesidad de distribuir los datos explícitamente y convertir todas las dependencias en mensajes. Además, evita la complejidad de la programacion híbrida MPI+X. En segundo lugar; la capacidad de enviar partes del cálculo entre los nodos permite a la librería balancear la carga de trabajo en programas MPI+X a gran escala. Este enfoque no necesita un modelo de coste y permite equilibrar cargas transversalmente en todo el programa y todos los nodos. En tercer lugar; teniendo en cuenta que es la librería quien maneja todos los aspectos relacionados con distribución y transferencia de datos, es posible la modificación dinámica y transparente de los recursos que utiliza la aplicación. Esta tesis describe el diseño, desarrollo y evaluación de OmpSs-2@Cluster; un modelo de programación y librería que extiende OmpSs-2 permitiendo la ejecución de programas OmpSs-2 existentes en múltiples nodos sin prácticamente necesidad de modificarlos. Para aplicaciones balanceadas, este modelo proporciona un rendimiento similar a MPI+OpenMP hasta 16 nodos y duplica el rendimiento en aplicaciones irregulares o desbalanceadas como la factorización de Cholesky. Este trabajo incluye la extensión de OmpSs-2@Cluster para interactuar con MPI y la librería de balanceo de carga Dynamic Load Balancing (DLB) desarrollada en el Barcelona Supercomputing Center (BSC). De este modo es posible equilibrar aplicaciones MPI+OmpSs-2 mediante la transferencia transparente de tareas entre nodos. Este enfoque reduce el tiempo de ejecución de una aplicación de mecánica de sólidos a micro-escala en un 46% en 64 nodos; en algunos experimentos hasta 8 nodos se pudo equilibrar perfectamente la carga con una diferencia inferior al 10% del equilibrio perfecto. Finalmente, se implementó otra extensión de la librería para realizar operaciones de maleabilidad en programas OmpSs-2@Cluster e interactuar con el Sistema de Manejo de Recursos (RMS). El único cambio requerido en la aplicación es la llamada explicita a una función de la interfaz que controla la adición o eliminación de nodos. Además, se agregó la funcionalidad de guardar y recuperar parte del estado de la aplicación de forma semitransparente con el objetivo de realizar operaciones de salva-reinicio. Dicha funcionalidad oculta al usuario la complejidad de la redistribución de datos y las operaciones de lectura-escritura en paralelo, mientras permite al programa recuperar y continuar ejecuciones previas. Este es un punto de partida para futuras investigaciones en tolerancia a fallos. En resumen, OmpSs-2@Cluster amplía el modelo de programación de OmpSs-2 para abarcar sistemas de memoria distribuida. El modelo permite la ejecución de programas OmpSs-2 en múltiples nodos prácticamente sin necesidad de modificarlos. OmpSs-2@Cluster permite además el balanceo dinámico de carga en aplicaciones híbridas MPI+OmpSs-2 ejecutadas en varios nodos y es capaz de realizar maleabilidad semi-transparente en programas OmpSs-2@Cluster puros. La librería tiene un niveles de rendimiento y estabilidad altos y abre varios caminos para trabajos futuro.Arquitectura de computador