102 research outputs found
DeltaTree: A Practical Locality-aware Concurrent Search Tree
As other fundamental programming abstractions in energy-e cient computing, search trees are expected to support both high parallelism and data locality. However, existing highly-concurrent search trees such as red-black trees and AVL trees do not consider data locality while existing locality-aware search trees such as those
based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations.
This paper presents DeltaTree, a practical locality-aware concurrent search tree that combines both locality-optimisation techniques from vEB-based trees and concurrency-optimisation techniques from non-blocking highly-concurrent search trees.
DeltaTree is a k-ary leaf-oriented tree of DeltaNodes in which each DeltaNode is a size- xed tree-container with the van Emde Boas layout. The expected memory transfer costs of DeltaTree's Search, Insert and Delete operations are O(logBN),
where N;B are the tree size and the unknown memory block size in the ideal cache model, respectively. DeltaTree's Search operation is wait-free, providing prioritised lanes for Search operations, the dominant operation in search trees. Its Insert and Delete operations are non-blocking to other Search, Insert and Delete operations, but they may be occasionally blocked by maintenance operations that are sometimes
triggered to keep DeltaTree in good shape. Our experimental evaluation using the latest implementation of AVL, red-black, and speculation friendly trees from the Synchrobench benchmark has shown that DeltaTree is up to 5 times faster than all of the three concurrent search trees for searching operations and up to 1.6 times
faster for update operations when the update contention is not too high
Evaluation of the power efficiency of UPC, OpenMP and MPI
In this study we compare the performance
and power efficiency of Unified Parallel C (UPC), MPI
and OpenMP by running a set of kernels from the NAS
Benchmark. One of the goals of this study is to focus
on the Partitioned Global Address Space (PGAS)
model, in order to describe it and compare it to MPI
and OpenMP. In particular we consider the power effi-
ciency expressed in millions operations per second per
watt as a criterion to evaluate the suitability of PGAS
compared to MPI and OpenMP. Based on these measurements,
we provide an analysis to explain the difference
of performance between UPC, MPI, and OpenMP
HyperProv: Decentralized Resilient Data Provenance at the Edge with Blockchains
Data provenance and lineage are critical for ensuring integrity and
reproducibility of information in research and application. This is
particularly challenging for distributed scenarios, where data may be
originating from decentralized sources without any central control by a single
trusted entity. We present HyperProv, a general framework for data provenance
based on the permissioned blockchain Hyperledger Fabric (HLF), and to the best
of our knowledge, the first system that is ported to ARM based devices such as
Raspberry Pi (RPi). HyperProv tracks the metadata, operation history and data
lineage through a set of built-in queries using smart contracts, enabling
lightweight retrieval of provenance data. HyperProv provides convenient
integration through a NodeJS client library, and also includes off-chain
storage through the SSH file system. We evaluate HyperProv's performance,
throughput, resource consumption, and energy efficiency on x86-64 machines, as
well as on RPi devices for IoT use cases at the edge
Demo abstract: Towards IoT service deployments on edge community network microclouds
Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.Peer ReviewedPostprint (author's final draft
Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model
International audienceSentiment analysis or opinion mining used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods
Toward a multitask aspect-based sentiment analysis model using deep learning
International audienceSentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches
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