EXTRA: Towards the exploitation of eXascale technology for reconfigurable architectures

© 2016 IEEE. To handle the stringent performance requirements of future exascale-class applications, High Performance Computing (HPC) systems need ultra-efficient heterogeneous compute nodes. To reduce power and increase performance, such compute nodes will require hardware accelerators with a high degree of specialization. Ideally, dynamic reconfiguration will be an intrinsic feature, so that specific HPC application features can be optimally accelerated, even if they regularly change over time. In the EXTRA project, we create a new and flexible exploration platform for developing reconfigurable architectures, design tools and HPC applications with run-time reconfiguration built-in as a core fundamental feature instead of an add-on. EXTRA covers the entire stack from architecture up to the application, focusing on the fundamental building blocks for run-time reconfigurable exascale HPC systems: new chip architectures with very low reconfiguration overhead, new tools that truly take reconfiguration as a central design concept, and applications that are tuned to maximally benefit from the proposed run-time reconfiguration techniques. Ultimately, this open platform will improve Europe's competitive advantage and leadership in the field

Large-scale Data Analysis and Deep Learning Using Distributed Cyberinfrastructures and High Performance Computing

Data in many research fields continues to grow in both size and complexity. For instance, recent technological advances have caused an increased throughput in data in various biological-related endeavors, such as DNA sequencing, molecular simulations, and medical imaging. In addition, the variance in the types of data (textual, signal, image, etc.) adds an additional complexity in analyzing the data. As such, there is a need for uniquely developed applications that cater towards the type of data. Several considerations must be made when attempting to create a tool for a particular dataset. First, we must consider the type of algorithm required for analyzing the data. Next, since the size and complexity of the data imposes high computation and memory requirements, it is important to select a proper hardware environment on which to build the application. By carefully both developing the algorithm and selecting the hardware, we can provide an effective environment in which to analyze huge amounts of highly complex data in a large-scale manner. In this dissertation, I go into detail regarding my applications using big data and deep learning techniques to analyze complex and large data. I investigate how big data frameworks, such as Hadoop, can be applied to problems such as large-scale molecular dynamics simulations. Following this, many popular deep learning frameworks are evaluated and compared to find those that suit certain hardware setups and deep learning models. Then, we explore an application of deep learning to a biomedical problem, namely ADHD diagnosis from fMRI data. Lastly, I demonstrate a framework for real-time and fine-grained vehicle detection and classification. With each of these works in this dissertation, a unique large-scale analysis algorithm or deep learning model is implemented that caters towards the problem and leverages specialized computing resources

Especulação de threads usando arquiteturas de memória transacional em hardware

Orientadores: Guido Costa Souza de Araújo, José Nelson AmaralTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Especulação no nível de threads (TLS) é uma técnica em hardware/software que possibilita a execução paralela de múltiplas iterações de um laço, inclusive na presença de algumas dependências loop-carried. TLS exige mecanismos em hardware para auxiliar a detecção de conflitos, o armazenamento especulativo, os commits das transações em ordem, e o roll-back das transações. Trabalhos anteriores exploraram enfoques para implementar TLS, tanto em hardware dedicado como puramente em software, e tentaram predizer o desempenho de futuras implementações de TLS em hardware. Contudo, não existe nenhum processador comercial que forneça suporte direto para TLS. Entretanto, execução especulativa é suportada na forma de Memória Transacional em Hardware (HTM) ¿ disponível em processadores modernos como Intel Core e IBM POWER8. HTM implementa três características essenciais para TLS: detecção de conflitos, armazenamento especulativo, e roll-back de transações. Antes de aplicar TLS a um laço quente, é necessário determinar se o laço tem potencial para ser especulado. Um laço pode ser adequado para TLS se a probabilidade de dependências loop-carried em tempo de execução for baixa; para estimar esta probabilidade um perfilamento de dependências do laço deve ser usado. Este trabalho apresenta um verificador das dependências loop-carried integrado como uma nova extensão de OpenMP, a diretiva parallel for check, a qual pode ser usada para ajudar desenvolvedores a identificarem a existência destas dependências em construções parallel for. Este trabalho também apresenta uma análise detalhada da aplicação de HTM para a paralelização de laços com TLS e descreve uma avaliação cuidadosa da implementação de TLS usando HTMs disponíveis em processadores modernos. Como resultado, esta tese proporciona evidências para validar várias afirmações importantes sobre o desempenho de TLS nestas arquiteturas. Os resultados experimentais mostram que TLS usando HTM produz speedups de até 3.8× para alguns laços. Finalmente, este trabalho descreve uma nova técnica de especulação para a otimização, e execução simultânea, de múltiplos traços de regiões de código quente. Esta técnica, chamada Speculative Trace Optimization (STO), enumera, otimiza, e executa especulativamente traços de laços quentes. Isto requer o suporte em hardware disponível em sistemas HTM. Este trabalho discute as características necessárias para suportar STO: multi-versão, resolução de conflitos tardia, detecção de conflitos prematura, e sincronização das transações. Uma revisão das arquiteturas HTM existentes ¿ Intel TSX, IBM BG/Q, e IBM POWER8 ¿ mostra que nenhuma delas tem todas as características requeridas para implementar STO. Entretanto, este trabalho mostra que STO pode ser implementado nas arquiteturas HTM existentes através da adição de privatização e código para esperar/retomarAbstract: Thread-Level Speculation (TLS) is a hardware/software technique that enables the execution of multiple loop iterations in parallel, even in the presence of some loop-carried dependences. TLS requires hardware mechanisms to support conflict detection, speculative storage, in-order commit of transactions, and transaction roll-back. Prior research has investigated approaches to implement TLS, either on dedicated hardware or purely in software, and has attempted to predict the performance of future TLS hardware implementations. Nevertheless, there is no off-the-shelf processor that provides direct support for TLS. Speculative execution is supported, however, in the form of Hardware Transactional Memory (HTM) ¿ available in recent processors such as the Intel Core and the IBM POWER8. HTM implements three key features required by TLS: conflict detection, speculative storage, and transaction roll-back. Before applying TLS to a hot loop, it is necessary to determine if the loop has potential to be amenable. A loop could be amenable if the probability of loop-carried dependences at runtime is low; to measure this probability loop dependence profiling is used. This project presents a novel dynamic loop-carried dependence checker integrated as a new extension to OpenMP, the parallel for check construct, which can be used to help programmers identify the existence of loop-carried dependences in parallel for constructs. This work also presents a detailed analysis of the application of HTM support for loop parallelization with TLS and describes a careful evaluation of the implementation of TLS on the HTM extensions available in such machines. As a result, it provides evidence to support several important claims about the performance of TLS over HTM in the Intel Core and the IBM POWER8 architectures. Experimental results reveal that by implementing TLS on top of HTM, speed-ups of up to 3.8× can be obtained for some loops. Finally, this work describes a novel speculation technique for the optimization, and simultaneous execution, of multiple alternative traces of hot code regions. This technique, called Speculative Trace Optimization (STO), enumerates, optimizes, and speculatively executes traces of hot loops. It requires hardware support that can be provided in a similar fashion as that available in HTM systems. This work discusses the necessary features to support STO, namely multi-versioning, lazy conflict resolution, eager conflict detection, and transaction synchronization. A review of existing HTM architectures ¿ Intel TSX, IBM BG/Q, and IBM POWER8 ¿ shows that none of them has all the features required to implement STO. However, this work demonstrates that STO can be implemented on top of existing HTM architectures through the addition of privatization and wait/resume codeDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoCAPESFAPESPCNP

Design methodology and productivity improvement in high speed VLSI circuits

2017 Spring.Includes bibliographical references.To view the abstract, please see the full text of the document

Exploiting task-based programming models for resilience

Hardware errors become more common as silicon technologies shrink and become more vulnerable, especially in memory cells, which are the most exposed to errors. Permanent and intermittent faults are caused by manufacturing variability and circuits ageing. While these can be mitigated once they are identified, their continuous rate of appearance throughout the lifetime of memory devices will always cause unexpected errors. In addition, transient faults are caused by effects such as radiation or small voltage/frequency margins, and there is no efficient way to shield against these events. Other constraints related to the diminishing sizes of transistors, such as power consumption and memory latency have caused the microprocessor industry to turn to increasingly complex processor architectures. To solve the difficulties arising from programming such architectures, programming models have emerged that rely on runtime systems. These systems form a new intermediate layer on the hardware-software abstraction stack, that performs tasks such as distributing work across computing resources: processor cores, accelerators, etc. These runtime systems dispose of a lot of information, both from the hardware and the applications, and offer thus many possibilities for optimisations. This thesis proposes solutions to the increasing fault rates in memory, across multiple resilience disciplines, from algorithm-based fault tolerance to hardware error correcting codes, through OS reliability strategies. These solutions rely for their efficiency on the opportunities presented by runtime systems. The first contribution of this thesis is an algorithmic-based resilience technique, allowing to tolerate detected errors in memory. This technique allows to recover data that is lost by performing computations that rely on simple redundancy relations identified in the program. The recovery is demonstrated for a family of iterative solvers, the Krylov subspace methods, and evaluated for the conjugate gradient solver. The runtime can transparently overlap the recovery with the computations of the algorithm, which allows to mask the already low overheads of this technique. The second part of this thesis proposes a metric to characterise the impact of faults in memory, which outperforms state-of-the-art metrics in precision and assurances on the error rate. This metric reveals a key insight into data that is not relevant to the program, and we propose an OS-level strategy to ignore errors in such data, by delaying the reporting of detected errors. This allows to reduce failure rates of running programs, by ignoring errors that have no impact. The architectural-level contribution of this thesis is a dynamically adaptable Error Correcting Code (ECC) scheme, that can increase protection of memory regions where the impact of errors is highest. A runtime methodology is presented to estimate the fault rate at runtime using our metric, through performance monitoring tools of current commodity processors. Guiding the dynamic ECC scheme online using the methodology's vulnerability estimates allows to decrease error rates of programs at a fraction of the redundancy cost required for a uniformly stronger ECC. This provides a useful and wide range of trade-offs between redundancy and error rates. The work presented in this thesis demonstrates that runtime systems allow to make the most of redundancy stored in memory, to help tackle increasing error rates in DRAM. This exploited redundancy can be an inherent part of algorithms that allows to tolerate higher fault rates, or in the form of dead data stored in memory. Redundancy can also be added to a program, in the form of ECC. In all cases, the runtime allows to decrease failure rates efficiently, by diminishing recovery costs, identifying redundant data, or targeting critical data. It is thus a very valuable tool for the future computing systems, as it can perform optimisations across different layers of abstractions.Los errores en memoria se vuelven más comunes a medida que las tecnologías de silicio reducen su tamaño. La variabilidad de fabricación y el envejecimiento de los circuitos causan fallos permanentes e intermitentes. Aunque se pueden mitigar una vez identificados, su continua tasa de aparición siempre causa errores inesperados. Además, la memoria también sufre de fallos transitorios contra los cuales no se puede proteger eficientemente. Estos fallos están causados por efectos como la radiación o los reducidos márgenes de voltaje y frecuencia. Otras restricciones coetáneas, como el consumo de energía y la latencia de la memoria, obligaron a las arquitecturas de computadores a volverse cada vez más complejas. Para programar tales procesadores, se desarrollaron modelos de programación basados en entornos de ejecución. Estos sistemas forman una nueva abstracción entre hardware y software, realizando tareas como la distribución del trabajo entre recursos informáticos: núcleos de procesadores, aceleradores, etc. Estos entornos de ejecución disponen de mucha información tanto sobre el hardware como sobre las aplicaciones, y ofrecen así muchas posibilidades de optimización. Esta tesis propone soluciones a los fallos en memoria entre múltiples disciplinas de resiliencia, desde la tolerancia a fallos basada en algoritmos, hasta los códigos de corrección de errores en hardware, incluyendo estrategias de resiliencia del sistema operativo. La eficiencia de estas soluciones depende de las oportunidades que presentan los entornos de ejecución. La primera contribución de esta tesis es una técnica a nivel algorítmico que permite corregir fallos encontrados mientras el programa su ejecuta. Para corregir fallos se han identificado redundancias simples en los datos del programa para toda una clase de algoritmos, los métodos del subespacio de Krylov (gradiente conjugado, GMRES, etc). La estrategia de recuperación de datos desarrollada permite corregir errores sin tener que reinicializar el algoritmo, y aprovecha el modelo de programación para superponer las computaciones del algoritmo y de la recuperación de datos. La segunda parte de esta tesis propone una métrica para caracterizar el impacto de los fallos en la memoria. Esta métrica supera en precisión a las métricas de vanguardia y permite identificar datos que son menos relevantes para el programa. Se propone una estrategia a nivel del sistema operativo retrasando la notificación de los errores detectados, que permite ignorar fallos en estos datos y reducir la tasa de fracaso del programa. Por último, la contribución a nivel arquitectónico de esta tesis es un esquema de Código de Corrección de Errores (ECC por sus siglas en inglés) adaptable dinámicamente. Este esquema puede aumentar la protección de las regiones de memoria donde el impacto de los errores es mayor. Se presenta una metodología para estimar el riesgo de fallo en tiempo de ejecución utilizando nuestra métrica, a través de las herramientas de monitorización del rendimiento disponibles en los procesadores actuales. El esquema de ECC guiado dinámicamente con estas estimaciones de vulnerabilidad permite disminuir la tasa de fracaso de los programas a una fracción del coste de redundancia requerido para un ECC uniformemente más fuerte. El trabajo presentado en esta tesis demuestra que los entornos de ejecución permiten aprovechar al máximo la redundancia contenida en la memoria, para contener el aumento de los errores en ella. Esta redundancia explotada puede ser una parte inherente de los algoritmos que permite tolerar más fallos, en forma de datos inutilizados almacenados en la memoria, o agregada a la memoria de un programa en forma de ECC. En todos los casos, el entorno de ejecución permite disminuir los efectos de los fallos de manera eficiente, disminuyendo los costes de recuperación, identificando datos redundantes, o focalizando esfuerzos de protección en los datos críticos.Postprint (published version

Methods for Robust and Energy-Efficient Microprocessor Architectures

Σήμερα, η εξέλιξη της τεχνολογίας επιτρέπει τη βελτίωση τριών βασικών στοιχείων της σχεδίασης των επεξεργαστών: αυξημένες επιδόσεις, χαμηλότερη κατανάλωση ισχύος και χαμηλότερο κόστος παραγωγής του τσιπ, ενώ οι σχεδιαστές επεξεργαστών έχουν επικεντρωθεί στην παραγωγή επεξεργαστών με περισσότερες λειτουργίες σε χαμηλότερο κόστος. Οι σημερινοί επεξεργαστές είναι πολύ ταχύτεροι και διαθέτουν εξελιγμένες λειτουργικές μονάδες συγκριτικά με τους προκατόχους τους, ωστόσο, καταναλώνουν αρκετά μεγάλη ενέργεια. Τα ποσά ηλεκτρικής ισχύος που καταναλώνονται, και η επακόλουθη έκλυση θερμότητας, αυξάνονται παρά τη μείωση του μεγέθους των τρανζίστορ. Αναπτύσσοντας όλο και πιο εξελιγμένους μηχανισμούς και λειτουργικές μονάδες για την αύξηση της απόδοσης και βελτίωση της ενέργειας, σε συνδυασμό με τη μείωση του μεγέθους των τρανζίστορ, οι επεξεργαστές έχουν γίνει εξαιρετικά πολύπλοκα συστήματα, καθιστώντας τη διαδικασία της επικύρωσής τους σημαντική πρόκληση για τη βιομηχανία ολοκληρωμένων κυκλωμάτων. Συνεπώς, οι κατασκευαστές επεξεργαστών αφιερώνουν επιπλέον χρόνο, προϋπολογισμό και χώρο στο τσιπ για να διασφαλίσουν ότι οι επεξεργαστές θα λειτουργούν σωστά κατά τη διάθεσή τους στη αγορά. Για τους λόγους αυτούς, η εργασία αυτή παρουσιάζει νέες μεθόδους για την επιτάχυνση και τη βελτίωση της φάσης της επικύρωσης, καθώς και για τη βελτίωση της ενεργειακής απόδοσης των σύγχρονων επεξεργαστών. Στο πρώτο μέρος της διατριβής προτείνονται δύο διαφορετικές μέθοδοι για την επικύρωση του επεξεργαστή, οι οποίες συμβάλλουν στην επιτάχυνση αυτής της διαδικασίας και στην αποκάλυψη σπάνιων σφαλμάτων στους μηχανισμούς μετάφρασης διευθύνσεων των σύγχρονων επεξεργαστών. Και οι δύο μέθοδοι καθιστούν ευκολότερη την ανίχνευση και τη διάγνωση σφαλμάτων, και επιταχύνουν την ανίχνευση του σφάλματος κατά τη φάση της επικύρωσης. Στο δεύτερο μέρος της διατριβής παρουσιάζεται μια λεπτομερής μελέτη χαρακτηρισμού των περιθωρίων τάσης σε επίπεδο συστήματος σε δύο σύγχρονους ARMv8 επεξεργαστές. Η μελέτη του χαρακτηρισμού προσδιορίζει τα αυξημένα περιθώρια τάσης που έχουν προκαθοριστεί κατά τη διάρκεια κατασκευής του κάθε μεμονωμένου πυρήνα του επεξεργαστή και αναλύει τυχόν απρόβλεπτες συμπεριφορές που μπορεί να προκύψουν σε συνθήκες μειωμένης τάσης. Για την μελέτη και καταγραφή της συμπεριφοράς του συστήματος υπό συνθήκες μειωμένης τάσης, παρουσιάζεται επίσης σε αυτή τη διατριβή μια απλή και ενοποιημένη συνάρτηση: η συνάρτηση πυκνότητας-σοβαρότητας. Στη συνέχεια, παρουσιάζεται αναλυτικά η ανάπτυξη ειδικά σχεδιασμένων προγραμμάτων (micro-viruses) τα οποία υποβάλουν της θεμελιώδεις δομές του επεξεργαστή σε μεγάλο φορτίο εργασίας. Αυτά τα προγράμματα στοχεύουν στην γρήγορη αναγνώριση των ασφαλών περιθωρίων τάσης. Τέλος, πραγματοποιείται ο χαρακτηρισμός των περιθωρίων τάσης σε εκτελέσεις πολλαπλών πυρήνων, καθώς επίσης και σε διαφορετικές συχνότητες, και προτείνεται ένα πρόγραμμα το οποίο εκμεταλλεύεται όλες τις διαφορετικές πτυχές του προβλήματος της κατανάλωσης ενέργειας και παρέχει μεγάλη εξοικονόμηση ενέργειας διατηρώντας παράλληλα υψηλά επίπεδα απόδοσης. Αυτή η μελέτη έχει ως στόχο τον εντοπισμό και την ανάλυση της σχέσης μεταξύ ενέργειας και απόδοσης σε διαφορετικούς συνδυασμούς τάσης και συχνότητας, καθώς και σε διαφορετικό αριθμό νημάτων/διεργασιών που εκτελούνται στο σύστημα, αλλά και κατανομής των προγραμμάτων στους διαθέσιμους πυρήνες.Technology scaling has enabled improvements in the three major design optimization objectives: increased performance, lower power consumption, and lower die cost, while system design has focused on bringing more functionality into products at lower cost. While today's microprocessors, are much faster and much more versatile than their predecessors, they also consume much power. As operating frequency and integration density increase, the total chip power dissipation increases. This is evident from the fact that due to the demand for increased functionality on a single chip, more and more transistors are being packed on a single die and hence, the switching frequency increases in every technology generation. However, by developing aggressive and sophisticated mechanisms to boost performance and to enhance the energy efficiency in conjunction with the decrease of the size of transistors, microprocessors have become extremely complex systems, making the microprocessor verification and manufacturing testing a major challenge for the semiconductor industry. Manufacturers, therefore, choose to spend extra effort, time, budget and chip area to ensure that the delivered products are operating correctly. To meet high-dependability requirements, manufacturers apply a sequence of verification tasks throughout the entire life-cycle of the microprocessor to ensure the correct functionality of the microprocessor chips from the various types of errors that may occur after the products are released to the market. To this end, this work presents novel methods for ensuring the correctness of the microprocessor during the post-silicon validation phase and for improving the energy efficiency requirements of modern microprocessors. These methods can be applied during the prototyping phase of the microprocessors or after their release to the market. More specifically, in the first part of the thesis, we present and describe two different ISA-independent software-based post-silicon validation methods, which contribute to formalization and modeling as well as the acceleration of the post-silicon validation process and expose difficult-to-find bugs in the address translation mechanisms (ATM) of modern microprocessors. Both methods improve the detection and diagnosis of a hardware design bug in the ATM structures and significantly accelerate the bug detection during the post-silicon validation phase. In the second part of the thesis we present a detailed system-level voltage scaling characterization study for two state-of-the-art ARMv8-based multicore CPUs. We present an extensive characterization study which identifies the pessimistic voltage guardbands (the increased voltage margins set by the manufacturer) of each individual microprocessor core and analyze any abnormal behavior that may occur in off-nominal voltage conditions. Towards the formalization of the any abnormal behavior we also present a simple consolidated function; the Severity function, which aggregates the effects of reduced voltage operation. We then introduce the development of dedicated programs (diagnostic micro-viruses) that aim to accelerate the time-consuming voltage margins characterization studies by stressing the fundamental hardware components. Finally, we present a comprehensive exploration of how two server-grade systems behave in different frequency and core allocation configurations beyond nominal voltage operation in multicore executions. This analysis aims (1) to identify the best performance per watt operation points, (2) to reveal how and why the different core allocation options affect the energy consumption, and (3) to enhance the default Linux scheduler to take task allocation decisions for balanced performance and energy efficiency

Large-scale quantum hybrid solution for linear systems of equations

State-of-the-art noisy intermediate-scale quantum devices (NISQ), although imperfect, enable computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, present quantum computations are restricted to exploring specific simplified protocols, whereas the implementation of full-scale quantum algorithms aimed at solving concrete large scale problems arising in data analysis and numerical modelling remains a challenge. Here we introduce and implement a hybrid quantum algorithm for solving linear systems of equations with exponential speedup, utilizing quantum phase estimation, one of the exemplary core protocols for quantum computing. We introduce theoretically classes of linear systems that are suitable for current generation quantum machines and solve experimentally a $2^{17}$-dimensional problem on superconducting IBMQ devices, a record for linear system solution on quantum computers. The considered large-scale algorithm shows superiority over conventional solutions, demonstrates advantages of quantum data processing via phase estimation and holds high promise for meeting practically relevant challenges.Comment: 8 pages, 6 figure

Lazy State Determination for SQL databases

Transactional systems have seen various efforts to increase their throughput, mainly by making use of parallelism and efficient Concurrency Control techniques. Most approaches optimize the systems’ behaviour when under high contention. In this work, we strive towards reducing the system’s overall contention through Lazy State Determination (LSD). LSD is a new transactional API that leverages on futures to delay the accesses to the Database as much as possible, reducing the amount of time that transactions require to operate under isolation and, thus, reducing the contention window. LSD was shown to be a promising solution for Key-Value Stores. Now, our focus turns to Relational Database Management Systems, as we attempt to implement and evaluate LSD in this new setting. This implementation was done through a custom JDBC driver to minimize required modifications to any external platform. Results show that the reduction of the contention window effectively improves the success rate of transactional applications. However, our current implementation exhibits some performance issues that must be further investigated and addressed.Os sistemas transacionais têm sido alvo de esforços variados para aumentar a sua velocidade de processamento, principalmente através de paralelismo e de técnicas de controlo de concorrência mais eficazes. A maior parte das soluções propostas visam a otimização do comportamento destes sistemas em ambientes de elevada contenção. Neste trabalho, nós iremos reduzir a contenção no sistema recorrendo ao Lazy State Determination (LSD). O LSD é uma nova API transacional que promove a utilização de futuros para adiar o máximo os acessos à Base de Dados, reduzindo assim o tempo que cada transação requer para executar em isolamento e, por consequência, reduzindo também a janela de contenção. O LSD tem-se mostrado uma solução promissora para bases de dados Chave-Valor. O nosso foco foi agora redirecionado para Sistemas de Gestão de Bases de Dados Relacionais, com uma tentativa de implementação e avaliação do LSD neste novo contexto. Este objetivo foi concretizado através da implementação de um controlador JDBC para minimizar quaisquer alterações a plataformas externas. Os resultados mostram que a redução da janela de contenção efetivamente melhora a taxa de sucesso de aplicações transacionais. No entanto, a nossa implementação atual tem alguns problemas de desempenho que necessitam de ser investigados e endereçados

Decompose and Conquer: Addressing Evasive Errors in Systems on Chip

Modern computer chips comprise many components, including microprocessor cores, memory modules, on-chip networks, and accelerators. Such system-on-chip (SoC) designs are deployed in a variety of computing devices: from internet-of-things, to smartphones, to personal computers, to data centers. In this dissertation, we discuss evasive errors in SoC designs and how these errors can be addressed efficiently. In particular, we focus on two types of errors: design bugs and permanent faults. Design bugs originate from the limited amount of time allowed for design verification and validation. Thus, they are often found in functional features that are rarely activated. Complete functional verification, which can eliminate design bugs, is extremely time-consuming, thus impractical in modern complex SoC designs. Permanent faults are caused by failures of fragile transistors in nano-scale semiconductor manufacturing processes. Indeed, weak transistors may wear out unexpectedly within the lifespan of the design. Hardware structures that reduce the occurrence of permanent faults incur significant silicon area or performance overheads, thus they are infeasible for most cost-sensitive SoC designs. To tackle and overcome these evasive errors efficiently, we propose to leverage the principle of decomposition to lower the complexity of the software analysis or the hardware structures involved. To this end, we present several decomposition techniques, specific to major SoC components. We first focus on microprocessor cores, by presenting a lightweight bug-masking analysis that decomposes a program into individual instructions to identify if a design bug would be masked by the program's execution. We then move to memory subsystems: there, we offer an efficient memory consistency testing framework to detect buggy memory-ordering behaviors, which decomposes the memory-ordering graph into small components based on incremental differences. We also propose a microarchitectural patching solution for memory subsystem bugs, which augments each core node with a small distributed programmable logic, instead of including a global patching module. In the context of on-chip networks, we propose two routing reconfiguration algorithms that bypass faulty network resources. The first computes short-term routes in a distributed fashion, localized to the fault region. The second decomposes application-aware routing computation into simple routing rules so to quickly find deadlock-free, application-optimized routes in a fault-ridden network. Finally, we consider general accelerator modules in SoC designs. When a system includes many accelerators, there are a variety of interactions among them that must be verified to catch buggy interactions. To this end, we decompose such inter-module communication into basic interaction elements, which can be reassembled into new, interesting tests. Overall, we show that the decomposition of complex software algorithms and hardware structures can significantly reduce overheads: up to three orders of magnitude in the bug-masking analysis and the application-aware routing, approximately 50 times in the routing reconfiguration latency, and 5 times on average in the memory-ordering graph checking. These overhead reductions come with losses in error coverage: 23% undetected bug-masking incidents, 39% non-patchable memory bugs, and occasionally we overlook rare patterns of multiple faults. In this dissertation, we discuss the ideas and their trade-offs, and present future research directions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147637/1/doowon_1.pd

Exploiting task-based programming models for resilience

Hardware errors become more common as silicon technologies shrink and become more vulnerable, especially in memory cells, which are the most exposed to errors. Permanent and intermittent faults are caused by manufacturing variability and circuits ageing. While these can be mitigated once they are identified, their continuous rate of appearance throughout the lifetime of memory devices will always cause unexpected errors. In addition, transient faults are caused by effects such as radiation or small voltage/frequency margins, and there is no efficient way to shield against these events. Other constraints related to the diminishing sizes of transistors, such as power consumption and memory latency have caused the microprocessor industry to turn to increasingly complex processor architectures. To solve the difficulties arising from programming such architectures, programming models have emerged that rely on runtime systems. These systems form a new intermediate layer on the hardware-software abstraction stack, that performs tasks such as distributing work across computing resources: processor cores, accelerators, etc. These runtime systems dispose of a lot of information, both from the hardware and the applications, and offer thus many possibilities for optimisations. This thesis proposes solutions to the increasing fault rates in memory, across multiple resilience disciplines, from algorithm-based fault tolerance to hardware error correcting codes, through OS reliability strategies. These solutions rely for their efficiency on the opportunities presented by runtime systems. The first contribution of this thesis is an algorithmic-based resilience technique, allowing to tolerate detected errors in memory. This technique allows to recover data that is lost by performing computations that rely on simple redundancy relations identified in the program. The recovery is demonstrated for a family of iterative solvers, the Krylov subspace methods, and evaluated for the conjugate gradient solver. The runtime can transparently overlap the recovery with the computations of the algorithm, which allows to mask the already low overheads of this technique. The second part of this thesis proposes a metric to characterise the impact of faults in memory, which outperforms state-of-the-art metrics in precision and assurances on the error rate. This metric reveals a key insight into data that is not relevant to the program, and we propose an OS-level strategy to ignore errors in such data, by delaying the reporting of detected errors. This allows to reduce failure rates of running programs, by ignoring errors that have no impact. The architectural-level contribution of this thesis is a dynamically adaptable Error Correcting Code (ECC) scheme, that can increase protection of memory regions where the impact of errors is highest. A runtime methodology is presented to estimate the fault rate at runtime using our metric, through performance monitoring tools of current commodity processors. Guiding the dynamic ECC scheme online using the methodology's vulnerability estimates allows to decrease error rates of programs at a fraction of the redundancy cost required for a uniformly stronger ECC. This provides a useful and wide range of trade-offs between redundancy and error rates. The work presented in this thesis demonstrates that runtime systems allow to make the most of redundancy stored in memory, to help tackle increasing error rates in DRAM. This exploited redundancy can be an inherent part of algorithms that allows to tolerate higher fault rates, or in the form of dead data stored in memory. Redundancy can also be added to a program, in the form of ECC. In all cases, the runtime allows to decrease failure rates efficiently, by diminishing recovery costs, identifying redundant data, or targeting critical data. It is thus a very valuable tool for the future computing systems, as it can perform optimisations across different layers of abstractions.Los errores en memoria se vuelven más comunes a medida que las tecnologías de silicio reducen su tamaño. La variabilidad de fabricación y el envejecimiento de los circuitos causan fallos permanentes e intermitentes. Aunque se pueden mitigar una vez identificados, su continua tasa de aparición siempre causa errores inesperados. Además, la memoria también sufre de fallos transitorios contra los cuales no se puede proteger eficientemente. Estos fallos están causados por efectos como la radiación o los reducidos márgenes de voltaje y frecuencia. Otras restricciones coetáneas, como el consumo de energía y la latencia de la memoria, obligaron a las arquitecturas de computadores a volverse cada vez más complejas. Para programar tales procesadores, se desarrollaron modelos de programación basados en entornos de ejecución. Estos sistemas forman una nueva abstracción entre hardware y software, realizando tareas como la distribución del trabajo entre recursos informáticos: núcleos de procesadores, aceleradores, etc. Estos entornos de ejecución disponen de mucha información tanto sobre el hardware como sobre las aplicaciones, y ofrecen así muchas posibilidades de optimización. Esta tesis propone soluciones a los fallos en memoria entre múltiples disciplinas de resiliencia, desde la tolerancia a fallos basada en algoritmos, hasta los códigos de corrección de errores en hardware, incluyendo estrategias de resiliencia del sistema operativo. La eficiencia de estas soluciones depende de las oportunidades que presentan los entornos de ejecución. La primera contribución de esta tesis es una técnica a nivel algorítmico que permite corregir fallos encontrados mientras el programa su ejecuta. Para corregir fallos se han identificado redundancias simples en los datos del programa para toda una clase de algoritmos, los métodos del subespacio de Krylov (gradiente conjugado, GMRES, etc). La estrategia de recuperación de datos desarrollada permite corregir errores sin tener que reinicializar el algoritmo, y aprovecha el modelo de programación para superponer las computaciones del algoritmo y de la recuperación de datos. La segunda parte de esta tesis propone una métrica para caracterizar el impacto de los fallos en la memoria. Esta métrica supera en precisión a las métricas de vanguardia y permite identificar datos que son menos relevantes para el programa. Se propone una estrategia a nivel del sistema operativo retrasando la notificación de los errores detectados, que permite ignorar fallos en estos datos y reducir la tasa de fracaso del programa. Por último, la contribución a nivel arquitectónico de esta tesis es un esquema de Código de Corrección de Errores (ECC por sus siglas en inglés) adaptable dinámicamente. Este esquema puede aumentar la protección de las regiones de memoria donde el impacto de los errores es mayor. Se presenta una metodología para estimar el riesgo de fallo en tiempo de ejecución utilizando nuestra métrica, a través de las herramientas de monitorización del rendimiento disponibles en los procesadores actuales. El esquema de ECC guiado dinámicamente con estas estimaciones de vulnerabilidad permite disminuir la tasa de fracaso de los programas a una fracción del coste de redundancia requerido para un ECC uniformemente más fuerte. El trabajo presentado en esta tesis demuestra que los entornos de ejecución permiten aprovechar al máximo la redundancia contenida en la memoria, para contener el aumento de los errores en ella. Esta redundancia explotada puede ser una parte inherente de los algoritmos que permite tolerar más fallos, en forma de datos inutilizados almacenados en la memoria, o agregada a la memoria de un programa en forma de ECC. En todos los casos, el entorno de ejecución permite disminuir los efectos de los fallos de manera eficiente, disminuyendo los costes de recuperación, identificando datos redundantes, o focalizando esfuerzos de protección en los datos críticos