Green Wave : A Semi Custom Hardware Architecture for Reverse Time Migration

Over the course of the last few decades the scientific community greatly benefited from steady advances in compute performance. Until the early 2000's this performance improvement was achieved through rising clock rates. This enabled plug-n-play performance improvements for all codes. In 2005 the stagnation of CPU clock rates drove the computing hardware manufactures to attain future performance through explicit parallelism. Now the HPC community faces a new, even bigger challenge. So far performance gains were achieved through replication of general-purpose cores and nodes. Unfortunately, rising cluster sizes resulted in skyrocketing energy costs - a paradigm change in HPC architecture design is inevitable. In combination with the increasing costs of data movement, the HPC community started exploring alternatives like GPUs and large arrays of simple, low-power cores (e.g. BlueGene) to offer the better performance per Watt and greatest scalability. As in general science, the seismic community faces large-scale, complex computational challenges that can only be limited solved with available compute capabilities. Such challenges include the physically correct modeling of subsurface rock layers. This thesis analyzes the requirements and performance of isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) wave propagation kernels as they appear in the Reverse Time Migration (RTM) imaging method. It finds that even with leading-edge, commercial off-the-shelf hardware, large-scale survey sizes cannot be imaged within reasonable time and power constraints. This thesis uses a novel architecture design method leveraging a hardware/software co-design approach, adopted from the mobile- and embedded market, for HPC. The methodology tailors an architecture design to a class of applications without loss of generality like in full custom designs. This approach was first applied in the Green Flash project, which proved that the co-design approach has the potential for high energy efficiency gains. This thesis presents the novel Green Wave architecture that is derived from the Green Flash project. Rather than focusing on climate codes, like Green Flash, Green Wave chooses RTM wave propagation kernels as its target application. Thus, the goal of the application-driven, co-design Green Wave approach, is to enable full programmability while allowing greater computational efficiency than general-purpose processors or GPUs by offering custom extensions to the processor's ISA and correctly sizing software-managed memories and an efficient on-chip network interconnect. The lowest level building blocks of the Green Wave design are pre-verified IP components. This minimizes the amount of custom logic in the design, which in turn reduces verification costs and design uncertainty. In this thesis three Green Wave architecture designs derived from ISO, VTI and TTI kernel analysis are introduced. Further, a programming model is proposed capable of hiding all communication latencies. With production-strength, cycle-accurate hardware simulators Green Wave's performance is benchmarked and its performance compared to leading on-market systems from Intel, AMD and NVidia. Based on a large-scale example survey, the results show that Green Wave has the potential of an energy efficiency improvement of 5x compared to x86 and 1.4x-4x to GPU-based clusters for ISO, VTI and TTI kernels

High-performance and hardware-aware computing: proceedings of the second International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2711), San Antonio, Texas, USA, February 2011 ; (in conjunction with HPCA-17)

High-performance system architectures are increasingly exploiting heterogeneity. The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full hardware potential if all features on all levels are taken into account in a holistic approach

Exploiting Multi-Level Parallelism in Streaming Applications for Heterogeneous Platforms with GPUs

Heterogeneous computing platforms support the traditional types of parallelism, such as e.g., instruction-level, data, task, and pipeline parallelism, and provide the opportunity to exploit a combination of different types of parallelism at different platform levels. The architectural diversity of platform components makes tapping into the platform potential a challenging programming task. This thesis makes an important step in this direction by introducing a novel methodology for automatic generation of structured, multi-level parallel programs from sequential applications. We introduce a novel hierarchical intermediate program representation (HiPRDG) that captures the notions of structure and hierarchy in the polyhedral model used for compile-time program transformation and code generation. Using the HiPRDG as the starting point, we present a novel method for generation of multi-level programs (MLPs) featuring different types of parallelism, such as task, data, and pipeline parallelism. Moreover, we introduce concepts and techniques for data parallelism identification, GPU code generation, and asynchronous data-driven execution on heterogeneous platforms with efficient overlapping of host-accelerator communication and computation. By enabling the modular, hybrid parallelization of program model components via HiPRDG, this thesis opens the door for highly efficient tailor-made parallel program generation and auto-tuning for next generations of multi-level heterogeneous platforms with diverse accelerators.Computer Systems, Imagery and Medi

Software caching techniques and hardware optimizations for on-chip local memories

Despite the fact that the most viable L1 memories in processors are caches, on-chip local memories have been a great topic of consideration lately. Local memories are an interesting design option due to their many benefits: less area occupancy, reduced energy consumption and fast and constant access time. These benefits are especially interesting for the design of modern multicore processors since power and latency are important assets in computer architecture today. Also, local memories do not generate coherency traffic which is important for the scalability of the multicore systems. Unfortunately, local memories have not been well accepted in modern processors yet, mainly due to their poor programmability. Systems with on-chip local memories do not have hardware support for transparent data transfers between local and global memories, and thus ease of programming is one of the main impediments for the broad acceptance of those systems. This thesis addresses software and hardware optimizations regarding the programmability, and the usage of the on-chip local memories in the context of both single-core and multicore systems. Software optimizations are related to the software caching techniques. Software cache is a robust approach to provide the user with a transparent view of the memory architecture; but this software approach can suffer from poor performance. In this thesis, we start optimizing traditional software cache by proposing a hierarchical, hybrid software-cache architecture. Afterwards, we develop few optimizations in order to speedup our hybrid software cache as much as possible. As the result of the software optimizations we obtain that our hybrid software cache performs from 4 to 10 times faster than traditional software cache on a set of NAS parallel benchmarks. We do not stop with software caching. We cover some other aspects of the architectures with on-chip local memories, such as the quality of the generated code and its correspondence with the quality of the buffer management in local memories, in order to improve performance of these architectures. Therefore, we run our research till we reach the limit in software and start proposing optimizations on the hardware level. Two hardware proposals are presented in this thesis. One is about relaxing alignment constraints imposed in the architectures with on-chip local memories and the other proposal is about accelerating the management of local memories by providing hardware support for the majority of actions performed in our software cache.Malgrat les memòries cau encara son el component basic pel disseny del subsistema de memòria, les memòries locals han esdevingut una alternativa degut a les seves característiques pel que fa a l’ocupació d’àrea, el seu consum energètic i el seu rendiment amb un temps d’accés ràpid i constant. Aquestes característiques son d’especial interès quan les properes arquitectures multi-nucli estan limitades pel consum de potencia i la latència del subsistema de memòria.Les memòries locals pateixen de limitacions respecte la complexitat en la seva programació, fet que dificulta la seva introducció en arquitectures multi-nucli, tot i els avantatges esmentats anteriorment. Aquesta tesi presenta un seguit de solucions basades en programari i maquinari específicament dissenyat per resoldre aquestes limitacions.Les optimitzacions del programari estan basades amb tècniques d'emmagatzematge de memòria cau suportades per llibreries especifiques. La memòria cau per programari és un sòlid mètode per proporcionar a l'usuari una visió transparent de l'arquitectura, però aquest enfocament pot patir d'un rendiment deficient. En aquesta tesi, es proposa una estructura jeràrquica i híbrida. Posteriorment, desenvolupem optimitzacions per tal d'accelerar l’execució del programari que suporta el disseny de la memòria cau. Com a resultat de les optimitzacions realitzades, obtenim que el nostre disseny híbrid es comporta de 4 a 10 vegades més ràpid que una implementació tradicional de memòria cau sobre un conjunt d’aplicacions de referencia, com son els “NAS parallel benchmarks”.El treball de tesi inclou altres aspectes de les arquitectures amb memòries locals, com ara la qualitat del codi generat i la seva correspondència amb la qualitat de la gestió de memòria intermèdia en les memòries locals, per tal de millorar el rendiment d'aquestes arquitectures. La tesi desenvolupa propostes basades estrictament en el disseny de nou maquinari per tal de millorar el rendiment de les memòries locals quan ja no es possible realitzar mes optimitzacions en el programari. En particular, la tesi presenta dues propostes de maquinari: una relaxa les restriccions imposades per les memòries locals respecte l’alineament de dades, l’altra introdueix maquinari específic per accelerar les operacions mes usuals sobre les memòries locals

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

This work presents a method for a computer vision-based detection of biological viruses in PAMONO sensor images and, related to this, methods to explore cyber-physical systems such as those consisting of the PAMONO sensor, the detection software, and processing hardware. The focus is especially on an exploration of Graphics Processing Units (GPU) hardware for “General-Purpose computing on Graphics Processing Units” (GPGPU) software and the targeted systems are high performance servers, desktop systems, mobile systems, and hand-held systems. The first problem that is addressed and solved in this work is to automatically detect biological viruses in PAMONO sensor images. PAMONO is short for “Plasmon Assisted Microscopy Of Nano-sized Objects”. The images from the PAMONO sensor are very challenging to process. The signal magnitude and spatial extension from attaching viruses is small, and it is not visible to the human eye on raw sensor images. Compared to the signal, the noise magnitude in the images is large, resulting in a small Signal-to-Noise Ratio (SNR). With the VirusDetectionCL method for a computer vision-based detection of viruses, presented in this work, an automatic detection and counting of individual viruses in PAMONO sensor images has been made possible. A data set of 4000 images can be evaluated in less than three minutes, whereas a manual evaluation by an expert can take up to two days. As the most important result, sensor signals with a median SNR of two can be handled. This enables the detection of particles down to 100 nm. The VirusDetectionCL method has been realized as a GPGPU software. The PAMONO sensor, the detection software, and the processing hardware form a so called cyber-physical system. For different PAMONO scenarios, e.g., using the PAMONO sensor in laboratories, hospitals, airports, and in mobile scenarios, one or more cyber-physical systems need to be explored. Depending on the particular use case, the demands toward the cyber-physical system differ. This leads to the second problem for which a solution is presented in this work: how can existing software with several degrees of freedom be automatically mapped to a selection of hardware architectures with several hardware configurations to fulfill the demands to the system? Answering this question is a difficult task. Especially, when several possibly conflicting objectives, e.g., quality of the results, energy consumption, and execution time have to be optimized. An extensive exploration of different software and hardware configurations is expensive and time-consuming. Sometimes it is not even possible, e.g., if the desired architecture is not yet available on the market or the design space is too big to be explored manually in reasonable time. A Pareto optimal selection of software parameters, hardware architectures, and hardware configurations has to be found. To achieve this, three parameter and design space exploration methods have been developed. These are named SOG-PSE, SOG-DSE, and MOGEA-DSE. MOGEA-DSE is the most advanced method of these three. It enables a multi-objective, energy-aware, measurement-based or simulation-based exploration of cyber-physical systems. This can be done in a hardware/software codesign manner. In addition, offloading of tasks to a server and approximate computing can be taken into account. With the simulation-based exploration, systems that do not exist can be explored. This is useful if a system should be equipped, e.g., with the next generation of GPUs. Such an exploration can reveal bottlenecks of the existing software before new GPUs are bought. With MOGEA-DSE the overall goal—to develop a method to automatically explore suitable cyber-physical systems for different PAMONO scenarios—could be achieved. As a result, a rapid, reliable detection and counting of viruses in PAMONO sensor data using high-performance, desktop, laptop, down to hand-held systems has been made possible. The fact that this could be achieved even for a small, hand-held device is the most important result of MOGEA-DSE. With the automatic parameter and design space exploration 84% energy could be saved on the hand-held device compared to a baseline measurement. At the same time, a speedup of four and an F-1 quality score of 0.995 could be obtained. The speedup enables live processing of the sensor data on the embedded system with a very high detection quality. With this result, viruses can be detected and counted on a mobile, hand-held device in less than three minutes and with real-time visualization of results. This opens up completely new possibilities for biological virus detection that were not possible before

Improving Energy and Area Scalability of the Cache Hierarchy in CMPs

As the core counts increase in each chip multiprocessor generation, CMPs should improve scalability in performance, area, and energy consumption to meet the demands of larger core counts. Directory-based protocols constitute the most scalable alternative. A conventional directory, however, suffers from an inefficient use of storage and energy. First, the large, non-scalable, sharer vectors consume unnecessary area and leakage, especially considering that most of the blocks tracked in a directory are cached by a single core. Second, although increasing directory size and associativity could boost system performance by reducing the coverage misses, it would come at the expense of area and energy consumption. This thesis focuses and exploits the important differences of behavior between private and shared blocks from the directory point of view. These differences claim for a separate management of both types of blocks at the directory. First, we propose the PS-Directory, a two-level directory cache that keeps the reduced number of frequently accessed shared entries in a small and fast first-level cache, namely Shared Directory Cache, and uses a larger and slower second-level Private Directory Cache to track the large amount of private blocks. Experimental results show that, compared to a conventional directory, the PS-Directory improves performance while also reducing silicon area and energy consumption. In this thesis we also show that the shared/private ratio of entries in the directory varies across applications and across different execution phases within the applications, which encourages us to propose Dynamic Way Partitioning (DWP) Directory. DWP-Directory reduces the number of ways with storage for shared blocks and it allows this storage to be powered off or on at run-time according to the dynamic requirements of the applications following a repartitioning algorithm. Results show similar performance as a traditional directory with high associativity, and similar area requirements as recent state-of-the-art schemes. In addition, DWP-Directory achieves notable static and dynamic power consumption savings. This dissertation also deals with the scalability issues in terms of power found in processor caches. A significant fraction of the total power budget is consumed by on-chip caches which are usually deployed with a high associativity degree (even L1 caches are being implemented with eight ways) to enhance the system performance. On a cache access, each way in the corresponding set is accessed in parallel, which is costly in terms of energy. This thesis presents the PS-Cache architecture, an energy-efficient cache design that reduces the number of accessed ways without hurting the performance. The PS-Cache takes advantage of the private-shared knowledge of the referenced block to reduce energy by accessing only those ways holding the kind of block looked up. Results show significant dynamic power consumption savings. Finally, we propose an energy-efficient architectural design that can be effectively applied to any kind of set-associative cache memory, not only to processor caches. The proposed approach, called the Tag Filter (TF) Architecture, filters the ways accessed in the target cache set, and just a few ways are searched in the tag and data arrays. This allows the approach to reduce the dynamic energy consumption of caches without hurting their access time. For this purpose, the proposed architecture holds the X least significant bits of each tag in a small auxiliary X-bit-wide array. These bits are used to filter the ways where the least significant bits of the tag do not match with the bits in the X-bit array. Experimental results show that this filtering mechanism achieves energy consumption in set-associative caches similar to direct mapped ones. Experimental results show that the proposals presented in this thesis offer a good tradeoff among these three major design axes.Conforme se incrementa el número de núcleos en las nuevas generaciones de multiprocesadores en chip, los CMPs deben de escalar en prestaciones, área y consumo energético para cumplir con las demandas de un número núcleos mayor. Los protocolos basados en directorio constituyen la alternativa más escalable. Un directorio convencional, no obstante, sufre de una utilización ineficiente de almacenamiento y energía. En primer lugar, los grandes y poco escalables vectores de compartidores consumen una cantidad de energía de fuga y de área innecesaria, especialmente si se tiene en consideración que la mayoría de los bloques en un directorio solo se encuentran en la cache de un único núcleo. En segundo lugar, aunque incrementar el tamaño y la asociatividad del directorio aumentaría las prestaciones del sistema, esto supondría un incremento notable en el consumo energético. Esta tesis estudia las diferencias significativas entre el comportamiento de bloques privados y compartidos en el directorio, lo que nos lleva hacia una gestión separada para cada uno de los tipos de bloque. Proponemos el PS-Directory, una cache de directorio de dos niveles que mantiene el reducido número de las entradas compartidas, que son los que se acceden con más frecuencia, en una estructura pequeña de primer nivel (concretamente, la Shared Directory Cache) y que utiliza una estructura más grande y lenta en el segundo nivel (Private Directory Cache) para poder mantener la información de los bloques privados. Los resultados experimentales muestran que, comparado con un directorio convencional, el PS-Directory consigue mejorar las prestaciones a la vez que reduce el área de silicio y el consumo energético. Ya que el ratio compartido/privado de las entradas en el directorio varia entre aplicaciones y entre las diferentes fases de ejecución dentro de las aplicaciones, proponemos el Dynamic Way Partitioning (DWP) Directory. El DWP-Directory reduce el número de vías que almacenan entradas compartidas y permite que éstas se enciendan o apaguen en tiempo de ejecución según los requisitos dinámicos de las aplicaciones según un algoritmo de reparticionado. Los resultados muestran unas prestaciones similares a un directorio tradicional de alta asociatividad y un área similar a otros esquemas recientes del estado del arte. Adicionalmente, el DWP-Directory obtiene importantes reducciones de consumo estático y dinámico. Esta disertación también se enfrenta a los problemas de escalabilidad que se pueden encontrar en las memorias cache. En un acceso a la cache, se accede a cada vía del conjunto en paralelo, siendo así un acción costosa en energía. Esta tesis presenta la arquitectura PS-Cache, un diseño energéticamente eficiente que reduce el número de vías accedidas sin perjudicar las prestaciones. La PS-Cache utiliza la información del estado privado-compartido del bloque referenciado para reducir la energía, ya que tan solo accedemos a un subconjunto de las vías que mantienen los bloques del tipo solicitado. Los resultados muestran unos importantes ahorros de energía dinámica. Finalmente, proponemos otro diseño de arquitectura energéticamente eficiente que se puede aplicar a cualquier tipo de memoria cache asociativa por conjuntos. La propuesta, la Tag Filter (TF) Architecture, filtra las vías accedidas en el conjunto de la cache, de manera que solo se mira un número reducido de vías tanto en el array de etiquetas como en el de datos. Esto permite que nuestra propuesta reduzca el consumo de energía dinámico de las caches sin perjudicar su tiempo de acceso. Los resultados experimentales muestran que este mecanismo de filtrado es capaz de obtener un consumo energético en caches asociativas por conjunto similar de las caches de mapeado directo. Los resultados experimentales muestran que las propuestas presentadas en esta tesis consiguen un buen compromiso entre estos tres importantes pilares de diseño.Conforme s'incrementen el nombre de nuclis en les noves generacions de multiprocessadors en xip, els CMPs han d'escalar en prestacions, àrea i consum energètic per complir en les demandes d'un nombre de nuclis major. El protocols basats en directori són l'alternativa més escalable. Un directori convencional, no obstant, pateix una utilització ineficient d'emmagatzematge i energia. En primer lloc, els grans i poc escalables vectors de compartidors consumeixen una quantitat d'energia estàtica i d'àrea innecessària, especialment si es considera que la majoria dels blocs en un directori només es troben en la cache d'un sol nucli. En segon lloc, tot i que incrementar la grandària i l'associativitat del directori augmentaria les prestacions del sistema, això suposaria un increment notable en el consum d'energia. Aquesta tesis estudia les diferències significatives entre el comportament de blocs privats i compartits dins del directori, la qual cosa ens guia cap a una gestió separada per a cada un dels tipus de bloc. Proposem el PS-Directory, una cache de directori de dos nivells que manté el reduït nombre de les entrades de blocs compartits, que són els que s'accedeixen amb més freqüència, en una estructura menuda de primer nivell (concretament, la Shared Directory Cache) i que empra una estructura més gran i lenta en el segon nivell (Private Directory Cache) per poder mantenir la informació dels blocs privats. Els resultats experimentals mostren que, comparat amb un directori convencional, el PS-Directory aconsegueix millorar les prestacions a la vegada que redueix l'àrea de silici i el consum energètic. Ja que la ràtio compartit/privat de les entrades en el directori varia entre aplicacions i entre les diferents fases d'execució dins de les aplicacions, proposem el Dynamic Way Partitioning (DWP) Directory. DWP-Directory redueix el nombre de vies que emmagatzemen entrades compartides i permeten que aquest s'encengui o apagui en temps d'execució segons els requeriments dinàmics de les aplicacions seguint un algoritme de reparticionat. Els resultats mostren unes prestacions similars a un directori tradicional d'alta associativitat i una àrea similar a altres esquemes recents de l'estat de l'art. Adicionalment, el DWP-Directory obté importants reduccions de consum estàtic i dinàmic. Aquesta dissertació també s'enfronta als problemes d'escalabilitat que es poden tro- bar en les memòries cache. Les caches on-chip consumeixen una part significativa del consum total del sistema. Aquestes caches implementen un alt nivell d'associativitat. En un accés a la cache, s'accedeix a cada via del conjunt en paral·lel, essent així una acció costosa en energia. Aquesta tesis presenta l'arquitectura PS-Cache, un disseny energèticament eficient que redueix el nombre de vies accedides sense perjudicar les prestacions. La PS-Cache utilitza la informació de l'estat privat-compartit del bloc referenciat per a reduir energia, ja que només accedim al subconjunt de vies que mantenen blocs del tipus sol·licitat. Els resultats mostren uns importants estalvis d'energia dinàmica. Finalment, proposem un altre disseny d'arquitectura energèticament eficient que es pot aplicar a qualsevol tipus de memòria cache associativa per conjunts. La proposta, la Tag Filter (TF) Architecture, filtra les vies accedides en el conjunt de la cache, de manera que només un reduït nombre de vies es miren tant en el array d'etiquetes com en el de dades. Això permet que la nostra proposta redueixi el consum dinàmic energètic de les caches sense perjudicar el seu temps d'accés. Els resultats experimentals mostren que aquest mecanisme de filtre és capaç d'obtenir un consum energètic en caches associatives per conjunt similar al de les caches de mapejada directa. Els resultats experimentals mostren que les propostes presentades en aquesta tesis conseguixen un bon compromís entre aquestros tres importants pilars de diseny.Valls Mompó, JJ. (2017). Improving Energy and Area Scalability of the Cache Hierarchy in CMPs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79551TESI

Supporting general data structures and execution models in runtime environments

Para aprovechar las plataformas paralelas, se necesitan herramientas de programación para poder representar apropiadamente los algoritmos paralelos. Además, los entornos paralelos requieren sistemas en tiempo de ejecución que ofrezcan diferentes paradigmas de computación. Existen diferentes áreas a estudiar con el fin de construir un sistema en tiempo de ejecución completo para un entorno paralelo. Esta Tesis aborda dos problemas comunes: el soporte unificado de datos densos y dispersos, y la integración de paralelismo orientado a mapeo de datos y paralelismo orientado a flujo de datos. Esta Tesis propone una solución que desacopla la representación, partición y reparto de datos, del algoritmo y de la estrategia de diseño paralelo para integrar manejo para datos densos y dispersos. Además, se presenta un nuevo modelo de programación basado en el paradigma de flujo de datos, donde diferentes actividades pueden ser arbitrariamente enlazadas para formar redes genéricas pero estructuradas que representan el cómputo globalDepartamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos

Hardware design of task superscalar architecture

Exploiting concurrency to achieve greater performance is a difficult and important challenge for current high performance systems. Although the theory is plain, the complexity of traditional parallel programming models in most cases impedes the programmer to harvest performance. Several partitioning granularities have been proposed to better exploit concurrency at task granularity. In this sense, different dynamic software task management systems, such as task-based dataflow programming models, benefit dataflow principles to improve task-level parallelism and overcome the limitations of static task management systems. These models implicitly schedule computation and data and use tasks instead of instructions as a basic work unit, thereby relieving the programmer of explicitly managing parallelism. While these programming models share conceptual similarities with the well-known Out-of-Order superscalar pipelines (e.g., dynamic data dependency analysis and dataflow scheduling), they rely on software-based dependency analysis, which is inherently slow, and limits their scalability when there is fine-grained task granularity and a large amount of tasks. The aforementioned problem increases with the number of available cores. In order to keep all the cores busy and accelerate the overall application performance, it becomes necessary to partition it into more and smaller tasks. The task scheduling (i.e., creation and management of the execution of tasks) in software introduces overheads, and so becomes increasingly inefficient with the number of cores. In contrast, a hardware scheduling solution can achieve greater speed-ups as a hardware task scheduler requires fewer cycles than the software version to dispatch a task. The Task Superscalar is a hybrid dataflow/von-Neumann architecture that exploits the task level parallelism of the program. The Task Superscalar combines the effectiveness of Out-of-Order processors together with the task abstraction, and thereby provides an unified management layer for CMPs which effectively employs processors as functional units. The Task Superscalar has been implemented in software with limited parallelism and high memory consumption due to the nature of the software implementation. In this thesis, a Hardware Task Superscalar architecture is designed to be integrated in a future High Performance Computer with the ability to exploit fine-grained task parallelism. The main contributions of this thesis are: (1) a design of the operational flow of Task Superscalar architecture adapted and improved for hardware implementation, (2) a HDL prototype for latency exploration, (3) a full cycle-accurate simulator of the Hardware Task Superscalar (based on the previously obtained latencies), (4) full design space exploration of the Task Superscalar component configuration (number and size) for systems with different number of processing elements (cores), (5) comparison with a software implementation of a real task-based programming model runtime using real benchmarks, and (6) hardware resource usage exploration of the selected configurations.Explotar la concurrencia para conseguir un mejor rendimiento es un reto importante y difícil para los sistemas de alto rendimiento. Aunque la teoría es sencilla, en muchos casos la complejidad de los modelos de programación paralela tradicionales impide al programador obtener un buen rendimiento. Se han propuesto diferentes granularidades de particionamiento de tareas para explotar mejor la concurrencia implícita en las aplicaciones. En este sentido, diferentes sistemas software de manejo dinámico de tareas utilizan los principios de ejecución "dataflow" para mejorar el paralelismo a nivel de tarea y superar el rendimiento de los sistemas de planificación estáticos. Estos modelos planfican la ejecución dinámicamente y utilizan tareas, en lugar de instrucciones, como unidad básica de trabajo. De esta forma descargan al programador de tener que realizar la sincronización de las tareas explícitamente en su programa. Aunque estos modelos de programación comparten muchas similitudes con los bien conocidos procesadores fuera de orden (como el análisis dinámico de dependencias y la ejecución en "dataflow"), dependen de un análisis dinámico software de las dependencias. Dicho análisis es inherentemente lento y limita la escalabilidad cuando hay un gran número de tareas pequeñas. Los problemas antes mencionados se incrementan exponencialmente con el número de núcleos disponibles. Para conseguir mantener todos los núcleos ocupados y conseguir acelerar el rendimiento global de la aplicación se hace necesario particionarla en muchas tareas pequeñas. La gestión de dichas tareas (es decir, su creación y distribución entre los núcleos) en software introduce sobrecostes, y por tanto resulta ineficiente conforme aumenta el número de núcleos. En contraposición, un sistema hardware de planificación de tareas puede conseguir mejores rendimientos ya que requiere una menor latencia en la gestión de las tareas. El Task Superscalar (TSS) es una arquitectura híbrida dataflow/von-Neumann que explota el paralelismo a nivel de tareas de los programas. El TSS combina la efectividad de los procesadores fuera de orden con la abstracción de tarea, y por tanto provee una capa unificada de gestión para los CMPs que gestiona los núcleos como unidades funcionales. Previo al trabajo de esta tesis el Task Superscalar se había implementado en software con un paralelismo limitado y mucho consumo de memoria debido a las limitaciones inherentes de una implementación software. En esta tesis se diseñado una implementación hardware de la arquitectura Task Superscalar con capacidad para manejar muchas tareas de pequeño tamaño que es integrable en un futuro computador de altas prestaciones. Así pues, las contribuciones principales de esta tesis son: (1) el diseño de un flujo operacional de la arquitectura Task Superscalar adaptado y mejorado para su implementación hardware; (2) un prototipo HDL de dicho flujo para la exploración de las latencias asociadas a la implementación hardware; (3) un simulador ciclo a ciclo del diseño hardware basado en los resultados obtenidos en la implementación hardware; (4) una exploración completa del espacio de diseño de los componentes hardware (número y cantidad de módulos, tamaños de las memorias, etc.) para diferentes tamaños de computadores (es decir, para diferentes cantidades de nucleos); (5) una comparación con la implementación software actual del mismo modelo de programación utilizando aplicaciones reales y; (6) una exploración de la utilización de recursos hardware de las diferentes configuraciones seleccionadas