| 1. |
- Aartsen, M. G., et al.
(författare)
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The IceProd framework : Distributed data processing for the IceCube neutrino observatory
- 2015
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 0743-7315 .- 1096-0848. ; 75, s. 198-211
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Tidskriftsartikel (refereegranskat)abstract
- IceCube is a one-gigaton instrument located at the geographic South Pole, designed to detect cosmic neutrinos, identify the particle nature of dark matter, and study high-energy neutrinos themselves. Simulation of the IceCube detector and processing of data require a significant amount of computational resources. This paper presents the first detailed description of IceProd, a lightweight distributed management system designed to meet these requirements. It is driven by a central database in order to manage mass production of simulations and analysis of data produced by the IceCube detector. IceProd runs as a separate layer on top of other middleware and can take advantage of a variety of computing resources, including grids and batch systems such as CREAM, HTCondor, and PBS. This is accomplished by a set of dedicated daemons that process job submission in a coordinated fashion through the use of middleware plugins that serve to abstract the details of job submission and job management from the framework. (C) 2014 Elsevier Inc. All rights reserved.
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| 2. |
- Araujo, Victor, et al.
(författare)
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Performance evaluation of FIWARE : A cloud-based IoT platform for smart cities
- 2019
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 132, s. 250-261
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Tidskriftsartikel (refereegranskat)abstract
- As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.
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| 3. |
- Cao, Liang, et al.
(författare)
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GCHAR : An efficient Group-based Context–aware human activity recognition on smartphone
- 2018
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 118:part-1, s. 67-80
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Tidskriftsartikel (refereegranskat)abstract
- With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including health monitoring, self-managing system and fitness tracking. However, one of the main issues of the existing HAR schemes is that the classification accuracy is relatively low, and in order to improve the accuracy, high computation overhead is needed. In this paper, an efficient Group-based Context-aware classification method for human activity recognition on smartphones, GCHAR is proposed, which exploits hierarchical group-based scheme to improve the classification efficiency, and reduces the classification error through context awareness rather than the intensive computation. Specifically, GCHAR designs the two-level hierarchical classification structure, i.e., inter-group and inner-group, and utilizes the previous state and transition logic (so-called context awareness) to detect the transitions among activity groups. In comparison with other popular classifiers such as RandomTree, Bagging, J48, BayesNet, KNN and Decision Table, thorough experiments on the realistic dataset (UCI HAR repository) demonstrate that GCHAR achieves the best classification accuracy, reaching 94.1636%, and time consumption in training stage of GCHAR is four times shorter than the simple Decision Table and is decreased by 72.21% in classification stage in comparison with BayesNet.
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| 4. |
- García Martín, Eva, et al.
(författare)
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Estimation of energy consumption in machine learning
- 2019
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press. - 0743-7315 .- 1096-0848. ; 134, s. 75-88
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Tidskriftsartikel (refereegranskat)abstract
- Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning.
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| 5. |
- Khan, Tanveer, et al.
(författare)
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Towards augmented proactive cyberthreat intelligence
- 2019
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 0743-7315 .- 1096-0848. ; 124, s. 47-59
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Tidskriftsartikel (refereegranskat)abstract
- In cyber crimes, attackers are becoming more inventive with their exploits and use more sophisticated techniques to bypass the deployed security system. These attacks are targeted and are commonly referred as Advanced Persistent Threats (APTs). The currently available techniques to tackle these attacks are mostly reactive and signature based. Security Information and Event Management (SIEM), a proactive approach is the best solution. However, the major problem with SIEM is tackling huge amount of data in real time that makes it a time consuming and tedious task for security analyst. The use of threat intelligence caters to such issue by prioritizing the level of threat. In this paper, we assign risk score and confidence value to each feed generated at our product “T-Eye platform”. On the basis of these values, we assign a severity score to each feed type. Severity score assigns a level to the threat means prioritize the threat. The results, we achieved for prioritizing the threat is more apparent and accurate. In addition, we optimize the rules of IBM-Q-Radar by using threat feeds generated at T-Eye platform. Furthermore, a huge amount of false positive alarms generated at IBM Q-Radar is reduced to a certain extent.
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| 6. |
- Kim, S. H., et al.
(författare)
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A multi-hop pointer forwarding scheme for efficient location update in low-rate wireless mesh networks
- 2018
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press Inc.. - 0743-7315 .- 1096-0848. ; 122, s. 109-121
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Tidskriftsartikel (refereegranskat)abstract
- Recently, a pointer forwarding scheme (PFS) was proposed to reduce location update overhead in wireless mesh networks. Using PFS, a location update is replaced with a simple forwarding pointer setup between two neighboring mesh routers (MRs). However, in PFS, if the two MRs are not one hop neighbors, PFS fails to set up a forwarding pointer, thus increasing location update overhead. To improve PFS, we present a multi-hop pointer forwarding scheme (MPFS). MPFS allows forwarding pointers to be constructed over multi-hop at once even if MRs are not one hop neighbor by using logical tree distance constructed during network formation. The tree distance is used to relay forwarding pointer packets over multi-hop links without additional control overhead during forwarding pointer setup and to estimate hop distance between two MRs. By doing so, MPFS improves the probability of success in forwarding pointer setup while ensuring k≤km, resulting in lowering the location update overhead. Also, we analyze pointer forwarding success probability and average chain length and discuss why MPFS is suitable for resource-constrained LRWMNs. Using ns-2, we show that MPFS significantly reduces the number of location update events, location update delay and signaling overhead, and packet losses during location updates. With real-world implementation, we also confirm feasibility of MPFS.
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| 7. |
- Lampka, Kai, et al.
(författare)
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Keep it cool and in time : With runtime monitoring to thermal-aware execution speeds for deadline constrained systems
- 2016
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 0743-7315 .- 1096-0848. ; 95, s. 79-91
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Tidskriftsartikel (refereegranskat)abstract
- The Dynamic Power and Thermal Management (DPTM) system of Dynamic Voltage Frequency Scaling (DVFS) enabled processors compensates peak temperatures by slowing or even powering parts of the system down. While ensuring the integrity of computations, this comes with the drawback of losing performance. In the context of hard real-time systems, such unpredictable losses in performance are unacceptable, as they may lead to deadline misses which may yet compromise the integrity of the system. To safely execute hard real-time workloads on such systems, this article presents an online scheme for assigning speeds in such a way that (a) the system executes at low clock speed as often as possible, while (b) deadline violations are strictly ruled out. The proposed scheme is compared with an offline scheme which has complete knowledge about arrival times and execution demands of the workload. The benchmarking shows that for a workload which is always very close to the modelled maximum, our approach performs on-par with the offline scheme. In case of a workload which diverges from the modelled maximum more often, the speed assignments produced by our scheme become more pessimistic, as to ensure that all deadlines are met.
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| 8. |
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| 9. |
- Myllykoski, Mirko, 1988-, et al.
(författare)
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On solving separable block tridiagonal linear systems using a GPU implementation of radix-4 PSCR method
- 2018
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 115, s. 56-66
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Tidskriftsartikel (refereegranskat)abstract
- Partial solution variant of the cyclic reduction (PSCR) method is a direct solver that can be applied to certain types of separable block tridiagonal linear systems. Such linear systems arise, e.g., from the Poisson and the Helmholtz equations discretized with bilinear finite-elements. Furthermore, the separability of the linear system entails that the discretization domain has to be rectangular and the discretization mesh orthogonal. A generalized graphics processing unit (GPU) implementation of the PSCR method is presented. The numerical results indicate up to 24-fold speedups when compared to an equivalent CPU implementation that utilizes a single CPU core. Attained floating point performance is analyzed using roofline performance analysis model and the resulting models show that the attained floating point performance is mainly limited by the off-chip memory bandwidth and the effectiveness of a tridiagonal solver used to solve arising tridiagonal subproblems. The performance is accelerated using off-line autotuning techniques.
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| 10. |
- Otero, Evelyn, 1983-, et al.
(författare)
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OpenACC acceleration for the PN-PN-2 algorithm in Nek5000
- 2019
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press. - 0743-7315 .- 1096-0848. ; 132, s. 69-78
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Tidskriftsartikel (refereegranskat)abstract
- Due to its high performance and throughput capabilities, GPU-accelerated computing is becoming a popular technology in scientific computing, in particular using programming models such as CUDA and OpenACC. The main advantage with OpenACC is that it enables to simply port codes in their "original" form to GPU systems through compiler directives, thus allowing an incremental approach. An OpenACC implementation is applied to the CFD code Nek5000 for simulation of incompressible flows, based on the spectral-element method. The work follows up previous implementations and focuses now on the P-N-PN-2 method for the spatial discretization of the Navier-Stokes equations. Performance results of the ported code show a speed-up of up to 3.1 on multi-GPU for a polynomial order N > 11.
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