Evaluating application vulnerability to soft errors in multi-level cache hierarchy

As the capacity of cache increases dramatically with new processors, soft errors originating in cache has become a major reliability concern for high performance processors. This paper presents application specific soft error vulnerability analysis in order to understand an application's responses to soft errors from different levels of caches. Based on a high-performance processor simulator called Graphite, we have implemented a fault injection framework that can selectively inject bit flips to different levels of caches. We simulated a wide range of relevant bit error patterns and measured the applications' vulnerabilities to bit errors. Our experimental results have shown the various vulnerabilities of applications to bit errors from different levels of caches; the results have also indicated the probabilities of different behaviors from the applications

A vulnerability factor for ECC-protected memory

Fault injection studies and vulnerability analyses have been used to estimate the reliability of data structures in memory. We survey these metrics and look at their adequacy to describe the data stored in ECC-protected memory. We also introduce FEA, a new metric improving on the memory derating factor by ignoring a class of false errors. We measure all metrics using simulations and compare them to the outcomes of injecting errors in real runs. This in-depth study reveals that FEA provides more accurate results than any state-of-the-art vulnerability metric. Furthermore, FEA gives an upper bound on the failure probability due to an error in memory, making this metric a tool of choice to quantify memory vulnerability. Finally, we show that ignoring these false errors reduces the failure rate on average by 12.75% and up to over 45%.This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Economy and Competitiveness (contract TIN2015-65316- P), by the Generalitat de Catalunya (contracts 2017-SGR-1414 and 2017- SGR-1328), by the Spanish Government (Severo Ochoa grant SEV-2015- 0493) and by the European Union’s Horizon 2020 research and innovation programme (grant agreements 671697 and 779877). L. Jaulmes has been partially supported by the Spanish Ministry of Education, Culture and Sports under grant FPU2013/06982. M. Moreto and M. Casas have been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal fellowships RYC-2016-21104 and RYC-2017-23269.Peer ReviewedPostprint (author's final draft

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft

Optimizing soft error reliability through scheduling on heterogeneous multicore processors

Reliability to soft errors is an increasingly important issue as technology continues to shrink. In this paper, we show that applications exhibit different reliability characteristics on big, high-performance cores versus small, power-efficient cores, and that there is significant opportunity to improve system reliability through reliability-aware scheduling on heterogeneous multicore processors. We monitor the reliability characteristics of all running applications, and dynamically schedule applications to the different core types in a heterogeneous multicore to maximize system reliability. Reliability-aware scheduling improves reliability by 25.4 percent on average (and up to 60.2 percent) compared to performance-optimized scheduling on a heterogeneous multicore processor with two big cores and two small cores, while degrading performance by 6.3 percent only. We also introduce a novel system-level reliability metric for multiprogram workloads on (heterogeneous) multicores. We provide a trade-off analysis among reliability-, power- and performance-optimized scheduling, and evaluate reliability-aware scheduling under performance constraints and for unprotected L1 caches. In addition, we also extend our scheduling mechanisms to multithreaded programs. The hardware cost in support of our reliability-aware scheduler is limited to 296 bytes per core

GPU devices for safety-critical systems: a survey

Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.This work has been partially supported by the European Research Council with Horizon 2020 (grant agreements No. 772773 and 871465), the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the HiPEAC Network of Excellence and the Basque Government under grant KK-2019-00035. The Spanish Ministry of Economy and Competitiveness has also partially supported Leonidas Kosmidis with a Juan de la Cierva Incorporación postdoctoral fellowship (FJCI-2020- 045931-I).Peer ReviewedPostprint (author's final draft

Cross layer reliability estimation for digital systems

Forthcoming manufacturing technologies hold the promise to increase multifuctional computing systems performance and functionality thanks to a remarkable growth of the device integration density. Despite the benefits introduced by this technology improvements, reliability is becoming a key challenge for the semiconductor industry. With transistor size reaching the atomic dimensions, vulnerability to unavoidable fluctuations in the manufacturing process and environmental stress rise dramatically. Failing to meet a reliability requirement may add excessive re-design cost to recover and may have severe consequences on the success of a product. %Worst-case design with large margins to guarantee reliable operation has been employed for long time. However, it is reaching a limit that makes it economically unsustainable due to its performance, area, and power cost. One of the open challenges for future technologies is building ``dependable'' systems on top of unreliable components, which will degrade and even fail during normal lifetime of the chip. Conventional design techniques are highly inefficient. They expend significant amount of energy to tolerate the device unpredictability by adding safety margins to a circuit's operating voltage, clock frequency or charge stored per bit. Unfortunately, the additional cost introduced to compensate unreliability are rapidly becoming unacceptable in today's environment where power consumption is often the limiting factor for integrated circuit performance, and energy efficiency is a top concern. Attention should be payed to tailor techniques to improve the reliability of a system on the basis of its requirements, ending up with cost-effective solutions favoring the success of the product on the market. Cross-layer reliability is one of the most promising approaches to achieve this goal. Cross-layer reliability techniques take into account the interactions between the layers composing a complex system (i.e., technology, hardware and software layers) to implement efficient cross-layer fault mitigation mechanisms. Fault tolerance mechanism are carefully implemented at different layers starting from the technology up to the software layer to carefully optimize the system by exploiting the inner capability of each layer to mask lower level faults. For this purpose, cross-layer reliability design techniques need to be complemented with cross-layer reliability evaluation tools, able to precisely assess the reliability level of a selected design early in the design cycle. Accurate and early reliability estimates would enable the exploration of the system design space and the optimization of multiple constraints such as performance, power consumption, cost and reliability. This Ph.D. thesis is devoted to the development of new methodologies and tools to evaluate and optimize the reliability of complex digital systems during the early design stages. More specifically, techniques addressing hardware accelerators (i.e., FPGAs and GPUs), microprocessors and full systems are discussed. All developed methodologies are presented in conjunction with their application to real-world use cases belonging to different computational domains

Soft-error resilient on-chip memory structures

Soft errors induced by energetic particle strikes in on-chip memory structures, such as L1 data/instruction caches and register files, have become an increasing challenge in designing new generation reliable microprocessors. Due to their transient/random nature, soft errors cannot be captured by traditional verification and testing process due to the irrelevancy to the correctness of the logic. This dissertation is thus focusing on the reliability characterization and cost-effective reliable design of on-chip memories against soft errors. Due to various performance, area/size, and energy constraints in various target systems, many existing unoptimized protection schemes on cache memories may eventually prove significantly inadequate and ineffective. This work develops new lifetime models for data and tag arrays residing in both the data and instruction caches. These models facilitate the characterization of cache vulnerability of the stored items at various lifetime phases. The design methodology is further exemplified by the proposed reliability schemes targeting at specific vulnerable phases. Benchmarking is carried out to showcase the effectiveness of these approaches. The tag array demands high reliability against soft errors while the data array is fully protected in on-chip caches, because of its crucial importance to the correctness of cache accesses. Exploiting the address locality of memory accesses, this work proposes a Tag Replication Buffer (TRB) to protect information integrity of the tag array in the data cache with low performance, energy and area overheads. To provide a comprehensive evaluation of the tag array reliability, this work also proposes a refined evaluation metric, detected-without-replica-TVF (DOR-TVF), which combines the TVF and access-with-replica (AWR) analysis. Based on the DOR-TVF analysis, a TRB scheme with early write-back (TRB-EWB) is proposed, which achieves a zero DOR-TVF at a negligible performance overhead. Recent research, as well as the proposed optimization schemes in this cache vulnerability study, have focused on the design of cost-effective reliable data caches in terms of performance, energy, and area overheads based on the assumption of fixed error rates. However, for systems in operating environments that vary with time or location, those schemes will be either insufficient or over-designed for the changing error rates. This work explores the design of a self-adaptive reliable data cache that dynamically adapts its employed reliability schemes to the changing operating environments in order to maintain a target reliability. The experimental evaluation shows that the self-adaptive data cache achieves similar reliability to a cache protected by the most reliable scheme, while simultaneously minimizing the performance and power overheads. Besides the data/instruction caches, protecting the register file and its data buses is crucial to reliable computing in high-performance microprocessors. Since the register file is in the critical path of the processor pipeline, any reliable design that increases either the pressure on the register file or the register file access latency is not desirable. This work proposes to exploit narrow-width register values, which represent the majority of generated values, for making the duplicates within the same register data item. A detailed architectural vulnerability factor (AVF) analysis shows that this in-register duplication (IRD) scheme significantly reduces the AVF in the register file compared to the conventional design. The experimental evaluation also shows that IRD provides superior read-with-duplicate (RWD) and error detection/recovery rates under heavy error injection as compared to previous reliability schemes, while only incurring a small power overhead. By integrating the proposed reliable designs in data/instruction caches and register files, the vulnerability of the entire microprocessor is dramatically reduced. The new lifetime model, the self-adaptive design and the narrow-width value duplication scheme proposed in this work can also provide guidance to architects toward highly efficient reliable system design

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

Balancing reliability, cost, and performance tradeoffs with FreeFault

Abstract—Memory errors have been a major source of system failures and fault rates may rise even further as memory continues to scale. This increasing fault rate, especially when combined with advent of integrated on-package memories, may exceed the capabilities of traditional fault tolerance mecha-nisms or significantly increase their overhead. In this paper, we present FreeFault as a hardware-only, transparent, and nearly-free resilience mechanism that is implemented entirely within a processor and can tolerate the majority of DRAM faults. FreeFault repurposes portions of the last-level cache for storing retired memory regions and augments a hardware memory scrubber to monitor memory health and aid retirement decisions. Because it relies on existing structures (cache associativity) for retirement/remapping type repair, FreeFault has essentially no hardware overhead. Because it requires a very modest portion of the cache (as small as 8KB) to cover a large fraction of DRAM faults, FreeFault has almost no impact on performance. We explain how FreeFault adds an attractive layer in an overall resilience scheme of highly-reliable and highly-available systems by delaying, and even entirely avoiding, calling upon software to make tradeoff decisions between memory capacity, performance, and reliability. I