In recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and...
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In recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GRPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports distributed and hybrid (distributed+shared-memory) parallel executions of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GRPPI shared-memory one. These patterns internally leverage distributed queues, which can be configured to use two-sided or one-sided MPI primitives to communicate items among nodes. A detailed analysis of the GOP! MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation of two different streaming applications with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GRPPI overheads. (C) 2019 Elsevier B.V. All rights reserved.
In the recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed...
详细信息
ISBN:
(纸本)9781450364928
In the recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GrPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports the distributed and hybrid (distributed and shared-memory) parallel execution of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GrPPI shared-memory one. A detailed analysis of the GrPPI MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation on a streaming application with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GrPPI overheads.
A novel approach is introduced for the design of a Unique Word (UW) for frame synchronization in Time Division Multiplexing/Time Division Multiple Access (TDM/TDMA) systems. In particular, the UW symbols are distribut...
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A novel approach is introduced for the design of a Unique Word (UW) for frame synchronization in Time Division Multiplexing/Time Division Multiple Access (TDM/TDMA) systems. In particular, the UW symbols are distributed in each frame in a specific pattern in order to optimize the UW autocorrelation properties. The distribution patterns are scalable, since they preserve their autocorrelation properties under truncation. The resulting frame structure enables the use of several types of UWs, including the Silent-UW (SUW) and the Unit-UW (UUW), that are novel approaches for which the optimal Bayes detector is derived. An analytical performance characterization is presented along with comparisons with state-of-the-art alternatives. We show that SUW can significantly outperform the classic UW in the presence of frequency offsets or whenever data interference is the main impairment to cope with, i.e. at moderate to high signal to noise ratios. Interestingly, this performance improvement comes with the further advantage of a considerable complexity reduction. Hence UUW is shown to provide both performance improvement and complexity reduction.
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