Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.

Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete tool-chain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal in the presence of single bit upsets caused by radiation induced soft errors. Each system under analysis executes different software workloads both from benchmark suites and from real applications.

The explosive growth of Internet-connected devices will soon result in a flood of generated data, which will increase the demand for network bandwidth as well as compute power to process the generated data. Consequently, there is a need for more energy efficient servers to empower traditional centralized Cloud data-centers as well as emerging decentralized data-centers at the Edges of the Cloud. In this paper, we present our approach, which aims at developing a new class of micro-servers - the UniServer - that exceed the conservative energy and performance scaling boundaries by introducing novel mechanisms at all layers of the design stack. The main idea lies on the realization of the intrinsic hardware heterogeneity and the development of mechanisms that will automatically expose the unique varying capabilities of each hardware. Low overhead schemes are employed to monitor and predict the hardware behavior and report it to the system software. The system software including a virtualization and resource management layer is responsible for optimizing the system operation in terms of energy or performance, while guaranteeing non-disruptive operation under the extended operating points. Our characterization results on a 64-bit ARMv8 micro-server in 28nm process reveal large voltage margins in terms of Vmin variation among the 8 cores of the CPU chip, among three different sigma chips, and among different benchmarks with the potential to obtain up-to 38.8% energy savings. Similarly, DRAM characterizations show that refresh rate and voltage can be relaxed by 35x and 5%, respectively, leading to 23.2% power savings on average.

In this paper, we propose the employment of fast targeted programs (diagnostic micro-viruses) that aim to stress individually the main hardware components of a multicore CPU architecture which most likely determine the limits of voltage scaling, i.e. safe Vmin values. We describe in detail the complex development process for the diagnostic micro-viruses and their comprehensive validation in modern multicore CPU hardware. The combined execution of the micro-viruses takes very short time compared to regular programs execution, and can quickly reveal the voltage limits of the cores and chips at voltage levels below nominal. The micro-virus based characterization flow requires orders of magnitude shorter time while it delivers virtually identical: (a) Vmin values for the different CPU chips, and (b) Vmin values for the different cores within a CPU chip. We evaluate our micro-viruses based characterization flow (and compare it to the SPEC-based flow) on three different chips (a nominal graded and two corner parts) of Applied Micro's X-Gene 2 micro-server family (with 8-core ARMv8-based CPUs manufactured in 28nm). We report detailed validation and evaluation results that prove the effectiveness of the micro-viruses for the fast and accurate identification of the voltage margins variability among the chips and the cores of a multicore CPU.

In this paper, we explore the pessimistic voltage guardbands of two multicore x86-64 microprocessor chips that belong to different microarchitectures (one ultra-low power and one high-performance microprocessor), when programs are executed on individual cores of the CPU chips. We also examine the energy and temperature gains as positive effects of lowering the voltage in both chips while preserving the functional correctness of programs. The behavior of the cores was examined executing 8 different workloads from the SPEC CPU2006 suite. Our differential experimental study is performed on two state-of-the-art x86-64 microprocessors: an ultra-low power Intel Core i5-4200U and a high-performance Intel Core i7-3970X. Based on the results, the cores on each microprocessor chip behave differently for different workloads when undervolted, and the voltage guardbands are more than 15% below the nominal voltage levels. We show that the energy efficiency can be increased by a maximum of 20% and the reduction of temperature can be up to 25%.

Early reliability assessment of hardware structures using microarchitecture level simulators can effectively guide major error protection decisions in microprocessor design. Statistical fault injection on microarchitectural structures modeled in performance simulators is an accurate method to measure their Architectural Vulnerability Factor (AVF) but requires excessively long campaigns to obtain high statistical significance.

In this paper, we present the first automated system-level analysis of multicore CPUs based on ARMv8 64-bit architecture (8-core, 28nm X-Gene 2 micro-server by AppliedMicro) when pushed to operate in scaled voltage conditions. We report detailed system-level effects including SDCs, corrected/uncorrected errors and application/system crashes. Our study reveals large voltage margins (that can be harnessed for energy savings) and also large Vmin variation among the 8 cores of the CPU chip, among 3 different chips (a nominal rated and two sigma chips), and among different benchmarks.

Technology evolution has raised serious reliability considerations, as transistor dimensions shrink and modern microprocessors become denser and more vulnerable to faults. Reliability studies have proposed a plethora of methodologies for assessing system vulnerability which, however, highly rely on traditional reliability metrics that solely express failure rate over time. Although Failures In Time (FIT) is a very strong and representative reliability metric, it may fail to offer an objective comparison of highly diverse systems, such as CPUs against GPUs or other accelerators that are often employed to execute the same algorithms implemented for these platforms. In this paper, we propose a reliability evaluation methodology that takes into account the probability of a workload execution failure in order to compare heterogeneous systems, while we also capture the differences in the performance of these systems. We demonstrate the usefulness of the methodology with a test case scenario that compares the reliability and performance of three different commercial CPUs (different ISAs and microarchitectures) and one GPU. We use statistical fault injection to assess the vulnerability of the register file for the four computing systems of our study. The evaluation was performed using a comprehensive set of benchmarks with the same algorithms implemented for each individual system (serial code for the CPUs and parallel code for the GPU). Our findings show that, even though the GPU proves to be three orders of magnitude more vulnerable than CPUs using traditional reliability metrics, our performance-aware evaluation methodology shrinks this gap by 1-2 orders of magnitude providing more informative and realistic measurements to guide designers or programmers decisions.

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.

Early decisions in microprocessor design require a careful consideration of the corresponding performance and reliability implications of transient faults. The size and organization of important on-chip hardware components such as caches, register files and buffers have a direct impact on both the microprocessor resilience to soft errors and the execution time of the applications. In this paper, we employ a state-of-the-art x86-64 full-system micro-architectural simulator and a comprehensive fault injection framework built on top of it to deliver a detailed evaluation of the reliability and performance tradeoffs for major hardware components across several important parameters of their design (size, associativity, write policy, etc.). We also propose a simple and flexible fitness function that measures the aggregate effect of such design changes on the reliability and the performance of the studied workload.

Nowadays, the scientific community is looking for ways to understand the effect of software execution on the reliability of a complex system when the hardware layer is unreliable. This paper proposes a statistical reliability analysis model able to estimate system reliability considering both the hardware and the software layer of a system. Bayesian Networks are employed to model hardware resources of the processor and instructions of program traces. They are exploited to investigate the probability of input errors to alter both the correct behavior and the output of the program. Experimental results show that Bayesian networks prove to be a promising model, allowing to get accurate and fast reliability estimations w.r.t. fault injection/simulation approaches.

Analyzing the impact of software execution on the reliability of a complex digital system is an increasing challenging task. Current approaches mainly rely on time consuming fault injections experiments that prevent their usage in the early stage of the design process, when fast estimations are required in order to take design decisions. To cope with these limitations, this paper proposes a statistical reliability analysis model based on Bayesian Networks. The proposed approach is able to estimate system reliability considering both the hardware and the software layer of a system, in presence of hardware transient and permanent faults. In fact, when digital system reliability is under analysis, hardware resources of the processor and instructions of program traces are employed to build a Bayesian Network. Finally, the probability of input errors to alter both the correct behavior of the system and the output of the program is computed. According to experimental results presented in this paper, it can be stated that Bayesian Network model is able to provide accurate reliability estimations in a very short period of time. As a consequence it can be a valid alternative to fault injection, especially in the early stage of the design.

Forthcoming technologies hold the promise of a significant increase in integration density, performance and functionality. However, a dramatic change in microprocessor's reliability is also expected. Developing mechanisms for early and accurate reliability estimation will save significant design effort, resources and consequently will positively impact product's time-to-market (TTM). In this paper, we propose a versatile architecture-level fault injection framework, built on top of a state-of-the-art x86 microprocessor simulator, for thorough and fast characterization of a wide range of hardware components with respect to various fault models.

Forthcoming many-core processors are expected to be highly unreliable due to their high design complexity and aggressive manufacturing technology scaling. Online functional testing is an attractive low-cost error detection solution. A functional error detection scheme for many-core architectures can easily employ existing techniques from single-core microprocessors and exploit the available massive parallelism to reduce the total test execution time. However, the straightforward execution of test programs on such parallel architectures does not achieve the maximum theoretical speedup due to severe congestion on common hardware resources, especially the shared memory and the interconnection network. In this paper, we first identify the memory hierarchy parameters of many-core architectures that slow down the execution of parallel test programs. Then, we study typical test programs to identify which of their parts can be parallelized to improve performance. Finally, we propose a test program parallelization methodology for many-core architectures to accelerate online detection of permanent faults. We evaluate the proposed methodology on a popular many-core architecture, Intel's Single-chip Cloud Computer (SCC) showing an up to 47.6X speedup compared to a serial test program execution approach.

Multicore architectures are employed in the majority of computing domains (general-purpose microprocessors as well as specialized high-performance architectures such as network processors). Online error detection in such chips can employ effective techniques from single core microprocessors, however, effective test scheduling should be employed to minimize the overall chip test execution time which can significantly increase due to congestion on common hardware resources used by the cores. In this paper, we analyze the most important aspects of online error detection and scheduling in multiprocessor chips and evaluate test execution time in several different configurations of Intel's SCC architecture.