36,700 research outputs found

    A Pattern Language for High-Performance Computing Resilience

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    High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.Comment: Proceedings of the 22nd European Conference on Pattern Languages of Program

    Cross-layer system reliability assessment framework for hardware faults

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    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.Peer ReviewedPostprint (author's final draft

    Identifying Bugs in Make and JVM-Oriented Builds

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    Incremental and parallel builds are crucial features of modern build systems. Parallelism enables fast builds by running independent tasks simultaneously, while incrementality saves time and computing resources by processing the build operations that were affected by a particular code change. Writing build definitions that lead to error-free incremental and parallel builds is a challenging task. This is mainly because developers are often unable to predict the effects of build operations on the file system and how different build operations interact with each other. Faulty build scripts may seriously degrade the reliability of automated builds, as they cause build failures, and non-deterministic and incorrect build results. To reason about arbitrary build executions, we present buildfs, a generally-applicable model that takes into account the specification (as declared in build scripts) and the actual behavior (low-level file system operation) of build operations. We then formally define different types of faults related to incremental and parallel builds in terms of the conditions under which a file system operation violates the specification of a build operation. Our testing approach, which relies on the proposed model, analyzes the execution of single full build, translates it into buildfs, and uncovers faults by checking for corresponding violations. We evaluate the effectiveness, efficiency, and applicability of our approach by examining hundreds of Make and Gradle projects. Notably, our method is the first to handle Java-oriented build systems. The results indicate that our approach is (1) able to uncover several important issues (245 issues found in 45 open-source projects have been confirmed and fixed by the upstream developers), and (2) orders of magnitude faster than a state-of-the-art tool for Make builds

    Software reliability through fault-avoidance and fault-tolerance

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    The use of back-to-back, or comparison, testing for regression test or porting is examined. The efficiency and the cost of the strategy is compared with manual and table-driven single version testing. Some of the key parameters that influence the efficiency and the cost of the approach are the failure identification effort during single version program testing, the extent of implemented changes, the nature of the regression test data (e.g., random), and the nature of the inter-version failure correlation and fault-masking. The advantages and disadvantages of the technique are discussed, together with some suggestions concerning its practical use
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