Configurable data center switch architectures

In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces

AN ARCHITECTURE EVALUATION AND IMPLEMENTATION OF A SOFT GPGPU FOR FPGAs

Embedded and mobile systems must be able to execute a variety of different types of code, often with minimal available hardware. Many embedded systems now come with a simple processor and an FPGA, but not more energy-hungry components, such as a GPGPU. In this dissertation we present FlexGrip, a soft architecture which allows for the execution of GPGPU code on an FPGA without the need to recompile the design. The architecture is optimized for FPGA implementation to effectively support the conditional and thread-based execution characteristics of GPGPU execution without FPGA design recompilation. This architecture supports direct CUDA compilation to a binary which is executable on the FPGA-based GPGPU. Our architecture is customizable, thus providing the FPGA designer with a selection of GPGPU cores which display performance versus area tradeoffs. This dissertation describes the FlexGrip architecture in detail and showcases the benefits by evaluating the design for a collection of five standard CUDA benchmarks which are compiled using standard GPGPU compilation tools. Speedups of 23x, on average, versus a MicroBlaze microprocessor are achieved for designs which take advantage of the conditional execution capabilities offered by FlexGrip. We also show FlexGrip can achieve an 80% average reduction of dynamic energy versus the MicroBlaze microprocessor. The dissertation furthers discussion by exploring application-customized versions of the soft GPGPU, thus exploiting the overlay architecture. We expand the architecture to multiple processors per GPGPU and optimizing away features which are not needed by certain classes of applications. These optimizations, which include the effective use of block RAMs and DSP blocks, are critical to the performance of FlexGrip. By implementing a 2 GPGPU design, we show speedups of 44x on average versus a MicroBlaze microprocessor. Application-customized versions of the soft GPGPU can be used to further reduce dynamic energy consumption by an average of 14%. To complete this thesis, we augmented a GPGPU cycle accurate simulator to emulate FlexGrip and evaluate different levels of cache design spaces. We show performance increases for select benchmarks, however, we also show that 64% and 45% of benchmarks exhibited performance decreases when L1D cache was enabled for the 1 SMP and 2 SMP configurations, and only one benchmark showed performance improvement when the L2 cache was enabled

A Reconfigurable Processor for Heterogeneous Multi-Core Architectures

A reconfigurable processor is a general-purpose processor coupled with an FPGA-like reconfigurable fabric. By deploying application-specific accelerators, performance for a wide range of applications can be improved with such a system. In this work concepts are designed for the use of reconfigurable processors in multi-tasking scenarios and as part of multi-core systems

Conflict-Free Networks on Chip for Real Time Systems

[ES] La constante necesidad de un mayor rendimiento para cumplir con la gran demanda de potencia de cómputo de las nuevas aplicaciones, (ej. sistemas de conducción autónoma), obliga a la industria a apostar por la tecnología basada en Sistemas en Chip con Procesadores Multinúcleo (MPSoCs) en sus sistemas embebidos de seguridad-crítica. Los sistemas MPSoCs generalmente incluyen una red en el chip (NoC) para interconectar los núcleos de procesamiento entre ellos, con la memoria y con el resto de recursos compartidos. Desafortunadamente, el uso de las NoCs dificulta alcanzar la predecibilidad en el tiempo, ya que pueden aparecer conflictos en muchos puntos y de forma distribuida a nivel de red. Para afrontar este problema, en esta tesis se propone un nuevo paradigma de diseño para NoCs de tiempo real donde los conflictos en la red son eliminados por diseño. Este nuevo paradigma parte del Grafo de Dependencia de Canales (CDG) para evitar los conflictos de red de forma determinista. Nuestra solución es capaz de inyectar mensajes de forma natural usando un periodo TDM igual al límite teórico óptimo sin la necesidad de usar un proceso offline exigente computacionalmente. La red se ha integrado en un sistema multinúcleo basado en tiles y adaptado a su jerarquía de memoria. Como segunda contribución principal, proponemos un nuevo planificador dinámico y distribuido capaz de alcanzar un rendimiento pico muy cercanos a las NoC basadas en un diseño wormhole sin comprometer sus garantías de tiempo real. El planificador se basa en nuestro diseño de red para explotar sus propiedades clave. Los resultados de nuestra NoC muestran que nuestro diseño garantiza la predecibilidad en el tiempo evitando interferencias en la red entre múltiples aplicaciones ejecutándose concurrentemente. La red siempre garantiza el rendimiento y también mejora el rendimiento respecto al de las redes wormhole en una red 4 x 4 en un factor de 3,7x cuando se inyecta trafico para generar interferencias. En una red 8 x 8 las diferencias son incluso mayores. Además, la red obtiene un ahorro de área total del 10,79% frente a una implementación básica de una red wormhole. El planificador propuesto alcanza una mejora de rendimiento de 6,9x y 14,4x frente la versión básica de la red DCFNoC para redes en forma de malla de 16 y 64 nodos, respectivamente. Cuando lo comparamos frente a un conmutador estándar wormhole se preserva un rendimiento de red del 95% al mismo tiempo que preserva la estricta predecibilidad en el tiempo. Este logro abre la puerta a nuevos diseños de NoCs de alto rendimiento con predecibilidad en el tiempo. Como contribución final, construimos una taxonomía de NoCs basadas en TDM con propiedades de tiempo real. Con esta taxonomía realizamos un análisis exhaustivo para estudiar y comparar desde tiempos de respuesta, a implementaciones con bajo coste, pasando por soluciones de compromiso para diseños de NoCs de tiempo real. Como resultado, obtenemos nuevos diseños de NoCs basadas en TDM.[CA] La constant necessitat d'un major rendiment per a complir amb la gran demanda de potència de còmput de les noves aplicacions, (ex. sistemes de conducció autònoma), obliga la indústria a apostar per la tecnologia basada en Sistemes en Xip amb Processadors Multinucli (MPSoCs) en els seus sistemes embeguts de seguretat-crítica. Els sistemes MPSoCs generalment inclouen una xarxa en el xip (NoC) per a interconnectar els nuclis de processament entre ells, amb la memòria i amb la resta de recursos compartits. Desafortunadament, l'ús de les NoCs dificulta aconseguir la predictibilitat en el temps, ja que poden aparéixer conflictes en molts punts i de forma distribuïda a nivell de xarxa. Per a afrontar aquest problema, en aquesta tesi es proposa un nou paradigma de disseny per a NoCs de temps real on els conflictes en la xarxa són eliminats per disseny. Aquest nou paradigma parteix del Graf de Dependència de Canals (CDG) per a evitar els conflictes de xarxa de manera determinista. La nostra solució és capaç d'injectar missatges de mra natural fent ús d'un període TDM igual al límit teòric òptim sense la necessitat de fer ús d'un procés offline exigent computacionalment. La xarxa s'ha integrat en un sistema multinucli basat en tiles i adaptat a la seua jerarquia de memòria. Com a segona contribució principal, proposem un nou planificador dinàmic i distribuït capaç d'aconseguir un rendiment pic molt pròxims a les NoC basades en un disseny wormhole sense comprometre les seues garanties de temps real. El planificador es basa en el nostre disseny de xarxa per a explotar les seues propietats clau. Els resultats de la nostra NoC mostren que el nostre disseny garanteix la predictibilitat en el temps evitant interferències en la xarxa entre múltiples aplicacions executant-se concurrentment. La xarxa sempre garanteix el rendiment i també millora el rendiment respecte al de les xarxes wormhole en una xarxa 4 x 4 en un factor de 3,7x quan s'injecta trafic per a generar interferències. En una xarxa 8 x 8 les diferències són fins i tot majors. A més, la xarxa obté un estalvi d'àrea total del 10,79% front una implementació bàsica d'una xarxa wormhole. El planificador proposat aconsegueix una millora de rendiment de 6,9x i 14,4x front la versió bàsica de la xarxa DCFNoC per a xarxes en forma de malla de 16 i 64 nodes, respectivament. Quan ho comparem amb un commutador estàndard wormhole es preserva un rendiment de xarxa del 95% al mateix temps que preserva la estricta predictibilitat en el temps. Aquest assoliment obri la porta a nous dissenys de NoCs d'alt rendiment amb predictibilitat en el temps. Com a contribució final, construïm una taxonomia de NoCs basades en TDM amb propietats de temps real. Amb aquesta taxonomia realitzem una anàlisi exhaustiu per a estudiar i comparar des de temps de resposta, a implementacions amb baix cost, passant per solucions de compromís per a dissenys de NoCs de temps real. Com a resultat, obtenim nous dissenys de NoCs basades en TDM.[EN] The ever need for higher performance to cope with the high computational power demands of new applications (e.g autonomous driving systems), forces industry to support technology based on multi-processors system on chip (MPSoCs) in their safety-critical embedded systems. MPSoCs usually include a network-on-chip (NoC) to interconnect the cores between them and, with memory and the rest of shared resources. Unfortunately, the inclusion of NoCs difficults achieving time predictability as network-level conflicts may occur in many points in a distributed manner. To overcome this problem, this thesis proposes a new time-predictable NoC design paradigm where conflicts within the network are eliminated by design. This new paradigm builds on top of the Channel Dependency Graph (CDG) in order to deterministically avoid network conflicts. Our solution is able to naturally inject messages using a TDM period equal to the optimal theoretical bound without the need of using a computationally demanding offline process. The network is integrated in a tile-based manycore system and adapted to its memory hierarchy. As a second main contribution, we propose a novel distributed dynamic scheduler that is able to achieve peak performance close to a wormhole-based NoC design without compromising its real-time guarantees. The scheduler builds on top of our NoC design to exploit its key properties. The results of our NoC show that our design guarantees time predictability avoiding network interference among multiple running applications. The network always guarantees performance and also improves wormhole performance in a 4 x 4 setting by a factor of 3.7x when interference traffic is injected. For a 8 x 8 network differences are even larger. In addition, the network obtains a total area saving of 10.79% over a standard wormhole implementation. The proposed scheduler achieves an overall throughput improvement of 6.9x and 14.4x over a baseline conflict-free NoC for 16 and 64-node meshes, respectively. When compared against a standard wormhole router 95% of its network throughput is preserved while strict timing predictability is kept. This achievement opens the door to new high performance time predictable NoC designs. As a final contribution, we build a taxonomy of TDM-based NoCs with real-time properties. With this taxonomy we perform a comprehensive analysis to study and compare from response time specific, to low resource implementation cost, through trade-off solutions for real-time NoCs designs. As a result, we derive new TDM-based NoC designs.Picornell Sanjuan, T. (2021). Conflict-Free Networks on Chip for Real Time Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/177347TESI

Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code

Compiler and Architecture Design for Coarse-Grained Programmable Accelerators

abstract: The holy grail of computer hardware across all market segments has been to sustain performance improvement at the same pace as silicon technology scales. As the technology scales and the size of transistors shrinks, the power consumption and energy usage per transistor decrease. On the other hand, the transistor density increases significantly by technology scaling. Due to technology factors, the reduction in power consumption per transistor is not sufficient to offset the increase in power consumption per unit area. Therefore, to improve performance, increasing energy-efficiency must be addressed at all design levels from circuit level to application and algorithm levels. At architectural level, one promising approach is to populate the system with hardware accelerators each optimized for a specific task. One drawback of hardware accelerators is that they are not programmable. Therefore, their utilization can be low as they perform one specific function. Using software programmable accelerators is an alternative approach to achieve high energy-efficiency and programmability. Due to intrinsic characteristics of software accelerators, they can exploit both instruction level parallelism and data level parallelism. Coarse-Grained Reconfigurable Architecture (CGRA) is a software programmable accelerator consists of a number of word-level functional units. Motivated by promising characteristics of software programmable accelerators, the potentials of CGRAs in future computing platforms is studied and an end-to-end CGRA research framework is developed. This framework consists of three different aspects: CGRA architectural design, integration in a computing system, and CGRA compiler. First, the design and implementation of a CGRA and its instruction set is presented. This design is then modeled in a cycle accurate system simulator. The simulation platform enables us to investigate several problems associated with a CGRA when it is deployed as an accelerator in a computing system. Next, the problem of mapping a compute intensive region of a program to CGRAs is formulated. From this formulation, several efficient algorithms are developed which effectively utilize CGRA scarce resources very well to minimize the running time of input applications. Finally, these mapping algorithms are integrated in a compiler framework to construct a compiler for CGRADissertation/ThesisDoctoral Dissertation Computer Science 201