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Evaluation of an MPI-Based Implementation of the Tascell Task-parallel Language on Massively parallel systems

Evaluation of an MPI-Based Implementation of the Tascell Tas...

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international conference on parallel Processing Workshops (ICPPW)

作者： Daisuke Muraoka Masahiro Yasugi Tasuku Hiraishi Seiji Umatani Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Kyushu Institute of Technology Academic Center for Computing and Media Studies Kyoto University Graduate School of Infomatics Kyoto University

ISBN: (纸本)9781509028269

Tascell is a task parallel language that supports distributed memory environments. the conventional implementation of Tascell realizes inter-node communication with TCP/IP communication via Tascell servers. this implementation is suitable for dynamic addition of computation nodes and wide-area distributed environments. On the other hand, in supercomputer environments, TCP/IP may not be available for inter-node communication and there may be no appropriate places for deploying Tascell servers. In this study, we have developed a server-less implementation of Tascell that realizes inter-node communication with MPI communication in order to evaluate its performance on massively parallel systems. It performs well on four Xeon Phi coprocessors (with 456 workers) and the K computer, for instance, our 19-queens solver achieves a 4615-fold speedup relative to a serial implementation with 7168 workers on the K computer. Our server-less implementation realizes deadlock freedom, although it only requires the two-sided communication paradigm and the MPI_thREAD_FUNNELED support level. On Xeon Phi coprocessors, we compare our implementation with other implementations that employ TCP/IP or the MPI_thREAD_MULTIPLE support level.

关键词： Message systems Servers System recovery Computers Coprocessors TCPIP parallel processing

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Neuromorphic processing: a new frontier in scaling computer architecture 14

Neuromorphic processing: a new frontier in scaling computer ...

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Proceedings of the 19th international conference on Architectural support for programming languages and operating systems

作者： Jeff Gehlhaar Qualcomm Technologies Inc. San Diego CA USA

ISBN: (纸本)9781450323055

the desire to build a computer that operates in the same manner as our brains is as old as the computer itself. Although computer engineering has made great strides in hardware performance as a result of Dennard scaling, and even great advances in 'brain like' computation, the field still struggles to move beyond sequential, analytical computing architectures. Neuromorphic systems are being developed to transcend the barriers imposed by silicon power consumption, develop new algorithms that help machines achieve cognitive behaviors, and both exploit and enable further research in neuroscience. In this talk I will discuss a system im-plementing spiking neural networks. these systems hold the promise of an architecture that is event based, broad and shallow, and thus more power efficient than conventional computing solu-tions. this new approach to computation based on modeling the brain and its simple but highly connected units presents a host of new challenges. Hardware faces tradeoffs such as density or lower power at the cost of high interconnection overhead. Consequently, software systems must face choices about new language design. Highly distributed hardware systems require complex place and route algorithms to distribute the execution of the neural network across a large number of highly interconnected processing units. Finally, the overall design, simulation and testing process has to be entirely reimagined. We discuss these issues in the context of the Zeroth processor and how this approach compares to other neuromorphic systems that are becoming available.

关键词： spiking neural network parallel computing low power zeroth neural network neuromorphic

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High-Performance Mining of COVID-19 Open Research Datasets for Text Classification and Insights in Cloud computing Environments

High-Performance Mining of COVID-19 Open Research Datasets f...

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IEEE international conference on Utility and Cloud computing (UCC)

作者： Jie Zhao Maria A. Rodriguez Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9780738123943

ISBN: (纸本)9781665415637

the COVID-19 global pandemic is an unprecedented health crisis. Many researchers around the world have produced an extensive collection of literature since the outbreak. Analysing this information to extract knowledge and provide meaningful insights in a timely manner requires a considerable amount of computational power. Cloud platforms are designed to provide this computational power in an on-demand and elastic manner. Specifically, hybrid clouds, composed of private and public data centers, are particularly well suited to deploy computationally intensive workloads in a cost-efficient, yet scalable manner. In this paper, we developed a system utilising the Aneka Platform as a Service middleware with parallel processing and multi-cloud capability to accelerate the data process pipeline and article categorising process using machine learning on a hybrid cloud. the results are then persisted for further referencing, searching and visualising. the performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.

关键词： Cloud computing Unified modeling language Task analysis COVID-19 Data visualization Computer architecture Scalability

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the role of FPGAs in a converged future with heterogeneous programmable processors: pre-conference workshop 11

The role of FPGAs in a converged future with heterogeneous p...

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Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays

作者： Jonathan Rose Guy Lemieux University of Toronto Toronto ON Canada University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781450305549

the battle of fixed function devices vs. programmable devices has been won by the programmables. the question facing us now is to determine what kinds of programmability to place on next generation systems/devices. Research and development on many applications has shown that different kinds of hardware and software programmability succeed for different application classes: powerful, singlethread-optimized CPUs continue to do very well for many applications; the General Purpose GPU is carving a niche in high throughput, parallel floating point codes in addition to its home turf of graphics; the FPGA is particularly good at variable bit-size computations and data steering, as well as parallel distributed control of networks. Future systems may well need all three types of these types of engines, and perhaps interesting mixtures of them. this is particularly true when we deal with the combined goals of optimizing cost, performance and *** this workshop, we will look to the future of the FPGA within these types of 'converged' programmable computing engines, and reflectively ask ourselves: What role can the FPGA play? What future applications in areas such as networking, mobile, and artificial intelligence can be driven by FPGAs? How do FPGAs fit into the architecture realm of CPUs, general purpose GPUs, and DSPs? How should the designer/programme express their intent in the most effective way possible? What are the requirements for a compilation and optimization environment that allow FPGAs to intermix within a heterogeneous and converged future?the intent of this workshop is to open a discussion on these questions. there will be a series of short, invited talks interspersed with free and open discussion.

关键词： heterogeneity reconfigurable computing high level programming models fpga heterogeneous computing systems signal processing dsp

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Optimizing parallel Applications via Dynamic Concurrency throttling and Turbo Boosting

Optimizing Parallel Applications via Dynamic Concurrency Thr...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Sandro M. Marques thiarles S. Medeiros Matheus Serpa Fábio D. Rossi Marcelo C. Luizelli Philippe O. Navaux Antonio Carlos S. Beck Arthur F. Lorenzon Federal University of Pampa - Campus Alegrete Brazil Institute of Informatics - Federal University of Rio Grande do Sul Brazil Science and Technology Farroupilha Federal Institute of Education Brazil

With the increasing number of cores in modern systems, dynamic concurrency throttling (DCT) and turbo-boosting techniques are becoming a solution to better use the hardware resources. While DCT techniques tune the number of running threads, boosting techniques speed up sequential phases or unbalanced threads. However, as each region of an application may behave differently, optimizing both knobs is not straightforward. Hence, we propose two strategies that apply DCT and turbo-boosting: DBF, which aims to find an ideal configuration for each parallel/sequential region, and DBC, which considers the combination of parallel/sequential regions during the optimization. We show that DBF and DBC improve the EDP by up to 19% and 27% compared to a DCT-only strategy and by up to 95% and 96% compared to a Boost-only technique. We also show that DBF is more suitable for applications with high variability in the CPU workload, while DBC is better when there is low workload variability.

关键词： Concurrent computing System dynamics Instruction sets Boosting Hardware Discrete cosine transforms Optimization

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PASSENGERS' CHOICES ON AIRPORT DROP-OFF SERVICE: A DECISION FORECAST BASED ON SOCIAL LEARNING AND MACHINE LEARNING

PASSENGERS' CHOICES ON AIRPORT DROP-OFF SERVICE: A DECISION ...

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2016 4th IEEE international conference on Cloud computing and Intelligence systems（IEEE CCIS2016）

作者： Zhongqiang Wang Jian Peng Yu Chen Limiao Ning College of Computer Science Sichuan University

ISBN: (纸本)9781509012572

Airport drop-off service provided by airlines is a chauffeur-driven service(i.e. Uber and Di Di) as an emerging travel choice for travelers. More and more passenger enjoy the drop-off service. In practice, we find an interesting question: if a passenger has ever choice the drop-off service, whether they are willing to recommend this service to other traveler? Although the acknowledgment that social learning is related to travel decision is promoted, quantitative analysis about how social learning shape and impact the decision of passengers is still limited. We study and estimate a diffusion probability between different passengers by proposing a CCM(Co-travel Link Cascade Model) based on a modified EM iterative algorithm. then, we segment passengers into three types(Influenced, Unchecked and Immune). the three types of passengers are predicated by approaches of IC-like model, Random Forest model and probabilistic model, respectively. In addition, we also design a parallel implementation of our proposed algorithm in the Apache Spark distributed data processing environment. Experimental results on a real aviation data set demonstrate that CCM can efficiently infer the decision of travelers.

关键词： Airport drop-off service Social influence EM algorithm Machine learning parallel algorithm

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Using Stream Processing for Real-Time Clock Drift Correction in distributed Data Processing systems

Using Stream Processing for Real-Time Clock Drift Correction...

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international Computer Sciences and Information Technologies (CSIT)

作者： Roman Moravskyi Yevheniya Levus Software Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

In large-scale smart device networks, such as those used in metering systems, the internal clocks of one-way communication devices can drift over time, leading to inaccuracies that impact data-driven decision-making and operational efficiency. Existing solutions often rely on postcollection corrections or synchronization methods that are not feasible for systems requiring real-time accuracy. this study evaluates a method for correcting time drift in smart devices using stream processing. By leveraging a distributed architecture with Apache Kafka, Flink, and OpenSearch, the proposed system processes a continuous stream of device time points at a high rate, ensuring accurate and scalable time correction. Benchmark tests demonstrate the system's capability to maintain low backpressure and high throughput under varying conditions. the findings suggest that the proposed approach is effective for real-time clock drift correction in large-scale networks, offering insights for future integration into Meter Data Management systems (MDMS).

关键词： Performance evaluation Meters Computer network reliability parallel processing Benchmark testing Data processing Real-time systems Synchronization Smart devices Clocks

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A distributed Ensemble of Diverse Deep Learning Models for Predicting COVID-19 Cases

A Distributed Ensemble of Diverse Deep Learning Models for P...

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Eurocon

作者： Mohammad Mehedi Hassan Mabrook S. AlRakhami Ahmed Zohier Elhendi Salman A. AlQahtani Information Systems Department CCIS King Saud University Riadh Saudi Arabia Science Technology and Innovation Dept. King Saud University Riyadh Saudi Arabia Computer Engineering Department CCIS King Saud University Riadh Saudi Arabia

the outbreak of the COVID-19 pandemic has resulted in a significant impact on global health and economy. Forecasting the spread of COVID-19 cases is essential for policymakers to make informed decisions and allocate resources accordingly. Deep learning has shown promising results in predicting the spread of infectious diseases, including COVID-19. However, selecting the most appropriate model for a given forecasting task can be challenging, as no single model is guaranteed to perform well for all scenarios. In this paper, we propose a distributed ensemble approach that combines the predictions of different deep learning models, including Autoregressive Integrated Moving Average (Auto-ARIMA), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), to improve the accuracy of COVID-19 case forecasts. Specifically, we train each deep learning model on a subset of the data and aggregate their predictions to obtain an ensemble forecast. To achieve a distributed ensemble, we use an edge computing framework to train the deep learning models on multiple nodes in a parallel and distributed manner. We evaluate our approach on publicly available COVID-19 datasets, and our results show that the proposed ensemble model outperforms the individual models in terms of forecasting accuracy. Furthermore, our distributed ensemble approach significantly reduces the training and prediction time compared to training each model separately.

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Numerical Analysis of Complex Physical systems on Networked Mobile Devices

Numerical Analysis of Complex Physical Systems on Networked ...

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IEEE international conference on Mobile Ad Hoc and Sensor systems

作者： Christoph Dibak Frank Durr Kurt Rothermel Institute of Parallel and Distributed Systems University of Stuttgart

ISBN: (纸本)9781467391023

Recently, a new class of mobile applications has appeared that takes into account the behavior of physical phenomenon. Prominent examples of such applications include augmented reality applications visualizing physical processes on a mobile device or mobile cyber-physical systems like autonomous vehicles or robots. Typically, these applications need to solve partial differential equations (PDE) to simulate the behavior of a physical system. there are two basic strategies to numerically solve these PDEs: (1) offload all computations to a remote server;(2) solve the PDE on the resource-constrained mobile device. However, both strategies have severe drawbacks. Offloading will fail if the mobile device is disconnected, and resource constraints require to reduce the quality of the solution. therefore, we propose a new approach for mobile simulations using a hybrid strategy that is robust to communication failures and can still benefit from powerful server resources. the basic idea of this approach is to dynamically decide on the placement of the PDE solver based on a prediction of the wireless link availability using Markov Chains. Our tests based on measurement in real cellular networks and real mobile devices show that this approach is able to keep deadline constraints in more than 61% of the cases compared to a pure offloading approach, while saving up to 74% of energy compared to a simplified approach.

关键词： Mobile cloud computing Numerical applications Mobile cyber-physical systems Augmented reality augmented reality Mobile devices UNLOADING Numerical Analysis Cloud computing Mobile Cellular networks Autonomous Vehicles Mobile Applications New classes

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Increasing the Efficiency of Code Offloading through Remote-side Caching

Increasing the Efficiency of Code Offloading through Remote-...

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IEEE international conference on Wireless and Mobile computing, Networking and Communications

作者： Florian Berg Frank Dürr Kurt Rothermel Institute of Parallel and Distributed Systems University of Stuttgart 70569 Stuttgart Germany

ISBN: (纸本)9781467377027

End users execute today on their smart phones different kinds of mobile applications like calendar apps or high-end mobile games, differing in local resource usage. Utilizing local resources of a smart phone heavily, like playing high-end mobile games, drains its limited energy resource in few hours. To prevent the limited energy resource from a quick exhaustion, smart phones benefit from executing resource-intensive application parts on a remote server in the cloud (code offloading). During the remote execution on the remote server, a smart phone waits in idle mode until it receives a result. However, code offloading introduces computation and communication overhead, which decreases the energy efficiency and induces monetary cost. For instance, sending or receiving execution state information to or from a remote server consumes energy. Moreover, executing code on a remote server instance in a commercial cloud causes monetary cost. To keep consumed energy and monetary cost low, we present in this paper the concept of remote-side caching for code offloading, which increases the efficiency of code offloading. the remote-side cache serves as a collective storage of results for already executed application parts on remote servers, avoiding the repeated execution of previously run application parts. the smart phone queries the remote-side cache for corresponding results of resource-intensive application parts. In case of a cache hit, the smart phone gets immediately a result and continues the application execution. Otherwise, it migrates the application part and waits for a result of the remote execution. We show in our evaluation that the use of a remote-side cache decreases energy consumption and monetary cost for mobile applications by up to 97% and 99%, respectively.

关键词： Mobile Cloud computing Code Offloading distributed Execution Data Replication Function Caching Smartphone Energy sources UNLOADING Cache memory Mobile games Codes Mobile Applications Money Servers Data replication Remote

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