167,177 research outputs found
Distributed Recovery in Applicative Systems
Applicative systems are promising candidates for achieving high performance computing through aggregation of processors. This paper studies the fault recovery problems in a class of applicative systems. The concept of functional checkpointing is proposed as the nucleus of a distributed recovery mechanism. This entails incrementally building a resilient structure as the evaluation of an applicative program proceeds. A simple rollback algorithm is suggested to regenerate the corrupted structure by redoing the most effective functional checkpoints. Another algorithm, which attempts to recover intermediate results, is also presented. The parent of a faulty task reproduces a functional twin of the failed task. The regenerated task inherits all offspring of the faulty task so that partial results can be salvaged
Wait-Free Global Virtual Time Computation in Shared Memory Time-Warp Systems
Global Virtual Time (GVT) is a powerful abstraction used to discriminate what events belong (and what do not belong) to the past history of a parallel/distributed computation. For high performance simulation systems based on the Time Warp synchronization protocol, where concurrent simulation objects are allowed to process their events speculatively and causal consistency is achieved via rollback/recovery techniques, GVT is used to determine which portion of the simulation can be considered as committed. Hence it is the base for actuating memory recovery (e.g. of obsolete logs that were taken in order to support state recoverability) and nonrevocable operations (e.g. I/O). For shared memory implementations of simulation platforms based on the Time Warp protocol, the reference GVT algorithm is the one presented by Fujimoto and Hybinette [1]. However, this algorithm relies on critical sections that make it non-wait-free, and which can hamper scalability. In this article we present a waitfree shared memory GVT algorithm that requires no critical section. Rather, correct coordination across the processes while computing the GVT value is achieved via memory atomic operations, namely compare-and-swap. The price paid by our proposal is an increase in the number of GVT computation phases, as opposed to the single phase required by the proposal in [1]. However, as we show via the results of an experimental study, the wait-free nature of the phases carried out in our GVT algorithm pays-off in reducing the actual cost incurred by the proposal in [1]
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A Decentralized Bayesian Algorithm for Distributed Compressive Sensing in Networked Sensing Systems
Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intra-signal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra- and inter-signal correlations. The proposed approach is able to address networked sensing system applications with privacy concerns and/or for a fusion-centre-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quicklyThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TWC.2015.248798
Improving Performance of Iterative Methods by Lossy Checkponting
Iterative methods are commonly used approaches to solve large, sparse linear
systems, which are fundamental operations for many modern scientific
simulations. When the large-scale iterative methods are running with a large
number of ranks in parallel, they have to checkpoint the dynamic variables
periodically in case of unavoidable fail-stop errors, requiring fast I/O
systems and large storage space. To this end, significantly reducing the
checkpointing overhead is critical to improving the overall performance of
iterative methods. Our contribution is fourfold. (1) We propose a novel lossy
checkpointing scheme that can significantly improve the checkpointing
performance of iterative methods by leveraging lossy compressors. (2) We
formulate a lossy checkpointing performance model and derive theoretically an
upper bound for the extra number of iterations caused by the distortion of data
in lossy checkpoints, in order to guarantee the performance improvement under
the lossy checkpointing scheme. (3) We analyze the impact of lossy
checkpointing (i.e., extra number of iterations caused by lossy checkpointing
files) for multiple types of iterative methods. (4)We evaluate the lossy
checkpointing scheme with optimal checkpointing intervals on a high-performance
computing environment with 2,048 cores, using a well-known scientific
computation package PETSc and a state-of-the-art checkpoint/restart toolkit.
Experiments show that our optimized lossy checkpointing scheme can
significantly reduce the fault tolerance overhead for iterative methods by
23%~70% compared with traditional checkpointing and 20%~58% compared with
lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1
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