Scaling supercomputers comes with an increase in failure rates due to the increasing number of hardware components. In standard practice, applications are made resilient through checkpointing data and restarting execution after a failure occurs to resume from the latest checkpoint. However, redeploying an application incurs overhead by tearing down and reinstating execution, and possibly limiting checkpointing retrieval from slow permanent storage. In this paper we present Reinit ++ , a new design and implementation of the Reinit approach for global-restart recovery, which avoids application re-deployment. We extensively evaluate Reinit ++ contrasted with the leading MPI fault-tolerance approach of ULFM, implementing globalrestart recovery, and the typical practice of restarting an application to derive new insight on performance. Experimentation with three different HPC proxy applications made resilient to withstand process and node failures shows that Reinit ++ recovers much faster than restarting, up to 6×, or ULFM, up to 3×, and that it scales excellently as the number of MPI processes grows.

As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to such errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient, but few studies have shown the code patterns-combinations or sequences of computations-that make an application naturally resilient. In this paper, we present FlipTracker, a framework designed to extract these patterns using fine-grained tracking of error propagation and resilience properties, and we use it to present a set of computation patterns that are responsible for making representative HPC applications naturally resilient to errors. This not only enables a deeper understanding of resilience properties of these codes, but also can guide future application designs towards patterns with natural resilience.

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Frame structure estimation from line segments is an important yet challenging problem in understanding indoor scenes. In practice, line segment extraction can be affected by occlusions, illumination variations, and weak object boundaries. To address this problem, an approach for frame structure recovery based on line segment refinement and voting is proposed. We refined line segments by the revising, connecting, and adding operations. We then propose an iterative voting mechanism for selecting refined line segments, where a cross ratio constraint is enforced to build crab-like models. Our algorithm outperforms state-of-the-art approaches, especially when considering complex indoor scenes.