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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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PARSIR: a Package for Effective parallel Discrete Event Simulation on Multi-processor Machines

PARSIR: a Package for Effective Parallel Discrete Event Simu...

引用

international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Francesco Quaglia DICII - University of Rome Tor Vergata

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

In this article we present PARSIR (parallel SImulation Runner), a package that enables the effective exploitation of shared-memory multi-processor machines for running discrete event simulation models. PARSIR is a compile/run-time environment for discrete event simulation models developed with the C programming language. The architecture of PARSIR has been designed in order to keep low the amount of CPU-cycles required for running models. This is achieved via the combination of a set of techniques like: 1) causally consistent batch-processing of simulation events at an individual simulation object for caching effectiveness; 2) high likelihood of disjoint access parallelism; 3) the favoring of memory accesses on local NUMA (Non-Uniform-Memory-Access) nodes in the architecture, while still enabling well balanced workload distribution via work-stealing from remote nodes; 4) the use of RMW (Read-Modify-Write) machine instructions for fast access to simulation engine data required by the worker threads for managing the concurrent simulation objects and distributing the workload. Furthermore, any architectural solution embedded in the PARSIR engine is fully transparent to the application level code implementing the simulation model. We also provide experimental results showing the effectiveness of PARSIR when running the reference PHOLD benchmark on a NUMA shared-memory multi-processor machine equipped with 40 CPUs.

关键词： Instruction sets Computational modeling Scalability Benchmark testing parallel processing Real-time systems Discrete event simulation Synchronization Usability Engines

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Machine Learning based Outlier Detection in IoT Greenhouse

Machine Learning based Outlier Detection in IoT Greenhouse

引用

international symposium on Object-Oriented Real-Time distributed Computing

作者： Aymen Abid Omar Cheikhrouhou Ghada Zaïbi Abdennaceur Kachouri CES-Lab ENIS University of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia University of Monastir Tunisia National Engineering School of Monastir University of Monastir Tunisia AFD2E-Lab ENIS University of Sfax Sfax Tunisia

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Data Monitoring becomes mandatory for several IoT applications including smart greenhouse. It aims to increase the quality of information by identifying existing errors and anomalies, especially using outlier detection process by machine learning-data classification. Existing approaches require the knowledge of data characteristics in advance. However, this requirement is not always possible in IoT due to the heterogeneity of devices. Therefore, this paper provides VoteIoT: a new monitoring method based on data analytic and vote clustering outcome. In this way, we increase the probability of making a good decision and guaranteeing a good harvest of greenhouses. To evaluate the proposed solution, we used a real database extended by augmented data. The results show a good response time, below 0.01 seconds, as well as a good detection accuracy of 97%. Additionally, the false alarms are below 3% and therefore, a low useful data loss. Moreover, we have managed to increase the probability of a good decision compared to existing solutions.

关键词： Temperature measurement Data analysis Decision making distributed databases Machine learning Real-time systems Internet of Things

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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jincheng Lu Miao Cai Baoliu Ye College of Computer Science and Software Engineering Hohai University Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs.

关键词： Schedules distributed processing Prototypes Tail parallel processing Throughput Sorting

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Analysis of key indicators based on large-scale drug testing data

Analysis of key indicators based on large-scale drug testing...

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Computer Applications and Information Technology (ISCAIT), international symposium on

作者： Yun Cao School of Cyber Science and Engineering Southeast University Nanjing Jiangsu China Information Center of Jiangsu Medical Products Administration Nanjing Jiangsu China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The safeguard of drug security is an important prerequisite to guarantee public health and life safety. The pre-warning mechanism is an important means to monitor drug risks quickly and effectively. Based on the data mining technology, oriented to the massive data from each procedure of the drug industry chain, this paper mainly discusses how to pre-find the potential risks of the whole drug industry chain, and provides early-warning guidance via risk indicators. From the view of data modeling and technical processing, the key is how to integrate and analyze multi-source heterogeneous data (across space and time) and use the analysis of key indicators for streaming data. In specific, we first construct a distributed system and carry standardization for complex time-series data from different objects. Secondly, the potential risk of drug data is evaluated by using the key indicator analysis. At last, obtain the abnormal data objects and their fluctuating indicators of different periods. The application of the proposed method can provide potential hints or guidance for the sampling inspection of drugs, which is advantageous to improve the efficiency of the drug safety supervision department.

关键词： Drugs Industries Accuracy Time series analysis distributed databases systems architecture Standardization Inspection Safety Testing

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Exponential Speedup over Locality in MPC with Optimal Memory 36

Exponential Speedup over Locality in MPC with Optimal Memory

引用

36th international symposium on distributed Computing, DISC 2022

作者： Balliu, Alkida Brandt, Sebastian Fischer, Manuela Latypov, Rustam Maus, Yannic Olivetti, Dennis Uitto, Jara Gran Sasso Science Institute L'Aquila Italy CISPA Helmholtz Center for Information Security Saarbrücken Germany ETH Zürich Switzerland Aalto University Espoo Finland TU Graz Austria

ISBN: (纸本)9783959772556

Locally Checkable Labeling (LCL) problems are graph problems in which a solution is correct if it satisfies some given constraints in the local neighborhood of each node. Example problems in this class include maximal matching, maximal independent set, and coloring problems. A successful line of research has been studying the complexities of LCL problems on paths/cycles, trees, and general graphs, providing many interesting results for the LOCAL model of distributed computing. In this work, we initiate the study of LCL problems in the low-space Massively parallel Computation (MPC) model. In particular, on forests, we provide a method that, given the complexity of an LCL problem in the LOCAL model, automatically provides an exponentially faster algorithm for the low-space MPC setting that uses optimal global memory, that is, truly linear. While restricting to forests may seem to weaken the result, we emphasize that all known (conditional) lower bounds for the MPC setting are obtained by lifting lower bounds obtained in the distributed setting in tree-like networks (either forests or high girth graphs), and hence the problems that we study are challenging already on forests. Moreover, the most important technical feature of our algorithms is that they use optimal global memory, that is, memory linear in the number of edges of the graph. In contrast, most of the state-of-the-art algorithms use more than linear global memory. Further, they typically start with a dense graph, sparsify it, and then solve the problem on the residual graph, exploiting the relative increase in global memory. On forests, this is not possible, because the given graph is already as sparse as it can be, and using optimal memory requires new solutions. © Alkida Balliu, Sebastian Brandt, Manuela Fischer, Rustam Latypov, Yannic Maus, Dennis Olivetti, and Jara Uitto.

关键词： distributed computer systems

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On Decentralized Linearly Separable Computation With the Minimum Computation Cost

On Decentralized Linearly Separable Computation With the Min...

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IEEE international symposium on Information Theory

作者： Haoning Chen Minquan Cheng Ziyu Zhang Kai Liang Zhenhao Huang Youlong Wu ShanghaiTech University Shanghai China Guangxi Normal University Guilin China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The distributed linearly separable computation problem finds extensive applications across domains such as dis-tributed gradient coding, distributed linear transform, real-time rendering, etc. In this paper, we investigate this problem in a fully decentralized scenario, where $\mathrm{N}$ workers collaboratively perform the computation task without a central master. Each worker aims to compute a linearly separable computation that can be manifested as $\mathrm{K}_{\mathrm{c}}$ linear combinations of $\mathrm{K}$ messages, where each message is a function of a distinct dataset. We require that each worker successfully fulfill the task based on the transmissions from any $\mathrm{N}_{\mathrm{r}}$ workers, such that the system can tolerate any $\mathrm{N}-\mathrm{N}_{\mathrm{r}}$ stragglers. We focus on the scenario where the computation cost (the number of uncoded datasets assigned to each worker) is minimum, and aim to minimize the communication cost (the number of symbols the fastest $\mathrm{N}_{\mathrm{r}}$ workers transmit). We propose a novel distributed computing scheme that is optimal under the widely used cyclic data assignment. Interestingly, we demonstrate that the side information at each worker is ineffective in reducing the communication cost when $\mathrm{K}_{\mathrm{c}}\leq \text{KN}_{\mathrm{r}}/\mathrm{N}$ , while it helps reduce the communication cost as $\mathrm{K}_{\mathrm{c}}$ increases.

关键词： Costs Symbols Focusing distributed databases Transforms Rendering (computer graphics) Real-time systems

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1st Workshop on Composable systems (COMPSYS 2022)

1st Workshop on Composable Systems (COMPSYS 2022)

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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The First international Workshop on COmputing using EmeRging EXotic AI-Inspired systems (CORtEX'22)

The First International Workshop on COmputing using EmeRging...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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3rd IEEE international Workshop on Extreme-Scale Storage and Analysis (ESSA 2022)

3rd IEEE International Workshop on Extreme-Scale Storage and...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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Asynchrony and Failure Masking via Pseudo-Local Process Recovery in MPI Applications

Asynchrony and Failure Masking via Pseudo-Local Process Reco...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mathhew Whitlock Hemanth Kolla Aurelien Bouteiller Jackson R. Mayo Nicolas M. Morales Keita Teranishi George Bosilca Sandia National Laboratories Innovative Computing Laboratory University of Tennessee Knoxville Oak Ridge National Laboratory

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

For parallel solvers susceptible to hardware-related failures, localizing recovery to the processes directly affected by the failure allows preserving asynchronous progress and exhibits “failure masking” due to limited propagation of recovery delays. This results in improved scalability compared to global recovery which is a disproportionate response. However, localizing recovery from hard failures is challenging because such failures are not transparent to the MPI runtime, requiring reconstruction of the communication layers and of a consistent application state. In this work we present the process- and data-recovery concepts that enable the performance and scalability of localized recovery despite the inherently non-local nature of some recovery steps. We present design enhancements to existing resilience middleware-the Fenix library and MPI User-Level Failure Mitigation-to robustly support larger-scale execution and “pseudo-local” checkpointing and recovery from many process failures. Using an example stencil solver with emulated hard failures we present an experimental evaluation, with runs on up to ~1000 ranks subject to ~100 process failures, which confirms that that pseudo-local recovery has significantly improved weak scaling compared to the roughly exponential slowdown of global recovery. Our work shows how fault tolerance infrastructure originally designed for global checkpoint/restart can be repurposed to enable greater efficiency in a resilience-aware application.

关键词： Checkpointing Fault tolerance distributed processing Runtime Scalability Conferences Fault tolerant systems

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