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A predictive approach for dynamic replication of operators in distributed stream processing systems 34

A predictive approach for dynamic replication of operators i...

引用

34th IEEE international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Wladdimiro, Daniel Arantes, Luciana Sens, Pierre Hidalgo, Nicolas Sorbonne Univ LIP6 CNRS INRIA Paris France Univ Diego Portales Santiago Chile

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Stream Processing Systems (SPSs) can present significant fluctuation in input rate. To address this issue, some existing solutions propose reconfiguring the SPS by replicating its operators. However, such reconfiguration usually induces a high system downtime cost. Moreover, reconfiguration decisions are based only on resource utilization without balancing the load between replicas. We propose in this paper a predictive SPS that dynamically defines the necessary number of replicas of each operator based not only on the current resource utilization and input rate variation but also on the events that, due to the operator's overloading, could not be processed yet and are, thus, kept in the operator's queue. In addition, our SPS implements a load balancer that distributes incoming events more evenly among replicas of an operator. Our solution has been integrated into Storm. To avoid system reconfiguration downtime, our SPS preallocates a pool of replicas where each of them can be activated or deactivated based on per operator input load predictions. Using real traffic traces with different applications, we have conducted experiments on Google Cloud Platform (GCP), evaluating our SPS and comparing it with Storm and DABS-Storm.

关键词： Stream processing Adaptive SPS Predictive algorithm Replication Google Cloud Platform

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Re-visiting the Third Pillar of Science for Synergistic (Bio)computing

Re-visiting the Third Pillar of Science for Synergistic (Bio...

引用

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

作者： Wu Feng Virginia Tech

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

For centuries, theory and experiment have served as the two pillars of science that underpin our understanding of the physical and natural world. Scientists collect measurements and make observations to, in turn, construct theories to describe, explain, and predict the phenomena that occur in the world. More recently, computing has evolved as the third pillar of science that “fills the gap” between theory and experiment by simulating theory (and validating experiment), thus accelerating discoveries that would otherwise take much longer to discover and with significantly more personnel resources, e.g., the Human Genome Project. Today, while computing continues to accelerate discoveries by simulating theory, the doubling of bio{logical | medical | health | informatics} data is far outstripping our ability to compute on the data. Thus, the computing pillar of science must evolve to not only simulate theory (and be validated by experiment), but to also be leveraged to tackle and intelligently mine the “biodata deluge” via traditional data analytics as well as via machine learning and deep learning. In short, we need to not only compute faster but also smarter. As such, this talk re-visits (bio)computing, the third pillar of science, and its evolution into a (bio)computing mosaic, which I refer to here as synergistic (bio)computing.

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parallel Coregistration Algorithm For Sar Images Based On Hadoop

Parallel Coregistration Algorithm For Sar Images Based On Ha...

引用

IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Li Guobing Zeng Huaping Xu School of Electronic and Information Engineering Beihang University Beijing China

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

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A distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

引用

international symposium on parallel and distributed computing

作者： Ciprian-Octavian Truică Neculai-Ovidiu Istrate Elena-Simona Apostol Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.

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iWAPT2023 Keynote Speaker QC & HPC hybrid computing for simulation & data-analysis hybrid applications

iWAPT2023 Keynote Speaker QC & HPC hybrid computing for simu...

引用

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

作者： Hiroaki Kobayashi Special Adviser to President for Digital innovation Tohoku University Japan

In this talk, I will be presenting our on-going project entitled Quantum-Annealing Assisted Next-Generation HPC Infrastructure. In this project, we try to realize transparent accesses to not only classical HPC resources with heterogeneous computing platforms such as x86 and vector accelerator, but also Quantum computing one in a unified fashion. We are also developing next-generation applications in the fields of computational science, data sciences and their fusions best suited for this infrastructure. The target applications are three digital twins: Digital Twin for Disaster Resilience, Digital Twin for Stable Power Generation and Digital Twin for Soft-Material Design. In these applications developments, we introduce quantum annealing and simulated annealing accelerated by classical HPC into optimal evacuation route analysis and data clustering, respectively. Some performance discussion on different type of annealers by quantum and classical computing is also presented in this talk.

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Local and Global Scheduling in Mobile Drop computing

Local and Global Scheduling in Mobile Drop Computing

引用

international symposium on parallel and distributed computing

作者： George-Mircea Grosu Silvia-Elena Nistor Mihaela-Andreea Vasile Radu-Ioan Ciobanu Ciprian Dobre Florin Pop University Politehnica of Bucharest Bucharest Romania National Institute for Research and Development in Informatics (ICI) Bucharest Romania Academy of Romanian Scientists Bucharest Romania

With the emergence of the Internet of Things (IoT), the Cloud computing (CC) paradigm has started to exhibit several limitations caused by the fact that it is too far away from the tens of billions of devices. Thus, the edge computing paradigm has come to the fore, aiming to move the processing power to the edge of the network closer to the data sources. Furthermore, the fog computing paradigm comes as an addition, providing Cloud services decentralized at the geographic region level. This trend of migrating from a centralized to a decentralized approach gives way to a new paradigm, Drop computing (DC), that involves creating opportunistic decentralized ad-hoc social networks consisting of edge and mobile devices. The main goal of this paper is to provide a scheduling model for the Drop computing paradigm, both at the local end device level and at the global ad-hoc network level. We implement various scheduling solutions using the proposed model and, through simulations in the mobile network environment, we analyze their behavior in multiple scenarios to understand the requirements and outcomes of scheduling in DC.

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Interpretable Analysis of Production GPU Clusters Monitoring Data via Association Rule Mining

Interpretable Analysis of Production GPU Clusters Monitoring...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Baolin Li Siddharth Samsi Vijay Gadepally Devesh Tiwari Northeastern University MIT Lincoln Laboratory

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern high-performance computing (HPC) and cloud computing systems are integrating powerful GPUs to accelerate increasingly demanding deep learning workloads. To improve cluster efficiency and better understand user behavior and job characteristics, system operators will collect operational data for trace analysis. However, previous efforts on these system logs have lacked the interpretability aspect, and there is no systematic approach that can be widely applied to different datacenter traces and return interpretable results. In this work, we propose a workflow to discover hidden association relation-ships between collected features of system jobs. The outcome of our analysis approach yields useful association rules that can be directly interpreted into operational insights. Using this approach, we have conducted case studies using the traces of three large-scale multi-tenant GPU clusters running production machine learning workloads. We have focused on the observations of GPU underutilization and job failures, revealing the possible reasons for these job behaviors and suggesting solutions to mitigate them. Our case studies have demonstrated the feasibility of our interpretable analysis workflow, which can be widely used by more HPC and cloud computing system operators.

关键词： Deep learning Cloud computing distributed processing Systematics Data analysis Systems operation High performance computing

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Parameter Servers Placement for distributed Deep Learning in Edge computing 19

Parameter Servers Placement for Distributed Deep Learning in...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Yan, Jiaquan Wu, Yalan Wu, Jigang Chen, Long Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9781665435741

The architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. Therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. Then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Edge computing distributed Deep Learning Parameter Server Placement

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Load Balancing Schemes for Large Synthetic Population-Based Complex Simulators

Load Balancing Schemes for Large Synthetic Population-Based ...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Mucenic, Bogdan Kaligotla, Chaitanya Stevens, Abby Ozik, Jonathan Collier, Nicholson Macal, Charles Univ Chicago Chicago IL 60637 USA Simon Fraser Univ Burnaby BC Canada Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781665435772

We present our development of load balancing algorithms to efficiently distribute and parallelize the running of large-scale complex agent-based modeling (ABM) simulators on High-Performance computing (HPC) resources. Our algorithm is based on partitioning the co-location network that emerges from an ABM's underlying synthetic population. Variations of this algorithm are experimentally applied to investigate algorithmic choices on two factors that affect run-time performance. We report these experiments' results on the CityCOVID ABM, built to model the spread of COVID-19 in the Chicago metropolitan region.

关键词： agent-based modeling load balancing parallel simulation high performance computing

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AFCNNM: Accelerating Fully Connected Neural Network on Mesh-based Optical Network-on-Chip

AFCNNM: Accelerating Fully Connected Neural Network on Mesh-...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wen Yang Yunting Liao Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China College of Computer and Information Technology China Three Gorges University Yichang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Fully Connected Neural Network (FCNN) are widely used in image recognition and natural language processing. However, the time cost of training large datasets is high. Optical Network-on Chip (ONoC) has been proposed to accelerate the parallel computing of FCNN because of its advantages. Therefore, this paper proposes an accelerated FCNN model based on ONoC. We first design an FCNN-aware mapping strategy, and then propose a group-based inter-core communication scheme with low wavelength requirements according to the distribution of mapping cores. The optimal number of cores in each period is obtained by achieving the trade-off between the communication and computation time. The simulation results show that the proposed scheme has the advantages of low wavelength requirement, short training time and good scalability.

关键词： Training Simulation Computational modeling Scalability Neural networks Optical computing Network-on-chip Optical fiber networks parallel processing Optical imaging

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