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Performance Evaluation of Java/PCJ Implementation of parallel Algorithms on the Cloud

Performance Evaluation of Java/PCJ Implementation of Paralle...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Nowicki, Marek Gorski, Lukasz Bala, Piotr Nicolaus Copernicus Univ Torun Fac Math & Comp Sci Torun Poland Univ Warsaw Interdisciplinary Ctr Math & Computat Modeling Warsaw Poland

ISBN: (纸本)9783030715939;9783030715922

Cloud resources are more often used for large scale computing and data processing. However, the usage of the cloud is different than traditional High-Performance Computing (HPC) systems and both algorithms and codes have to be adjusted. This work is often time-consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (parallel Computing in Java), a novel tool for scalable high-performance computing and big data processing in Java. In this paper, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. The performance evaluation is based on the examples of highly scalable applications that run on the traditional HPC system and Amazon AWS Cloud. For the cloud, we have used Intel x86 and ARM processors running Java codes without changing any line of the program code and without the need for time-consuming recompilation. Presented applications have been parallelized using the PGAS programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a similar performance as for HPC systems.

关键词： Cloud computing parallel computing Performance evaluation Java PCJ HPC Cloud

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A Self-protecting Approach for Service-oriented Mobile applications 23

A Self-protecting Approach for Service-oriented Mobile Appli...

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23rd international conference on Enterprise Information Systems (ICEIS)

作者： Martins, Ronaldo Rodrigues de Oliveira Camargo, Marcos Paulo Passini, William Filisbino Campos, Gabriel Nagassaki Affonso, Frank Jose Sao Paulo State Univ UNESP Dept Stat Appl Math & Computat POB 178 BR-13506900 Rio Claro SP Brazil

ISBN: (纸本)9789897585098

The evolution of software systems in the last 10 years has brought new challenges for the development area, especially for service-oriented Mobile applications (MobApps). In the mobile computing domain, the integration of MobApps into service-based systems has been a feasible alternative to boost the capacity of processing and storage of such applications. In parallel, this type of application needs monitoring approaches mainly due to the need of dealing with a large number of users, continuous changes in the execution environment, and security threats. Besides that, most MobApps do not present the self-protecting property by default, resulting in a number of adverse situations, such as integrity of execution, reliability, security, and adaptations at runtime. The principal contribution of this paper is an approach based on MAPE-K (Monitor-Analyze-Plan-Execute over Knowledge) loop and machine learning techniques to ensure self-protecting features in MobApps, in particular, those based on services. Experimental results showed that this approach can autonomously and dynamically mitigate threats, making these applications more trustworthy and intrusion-safe. Our approach has good potential to contribute to the development of MobApps, going beyond existing approaches.

关键词： Self-protecting Mobile applications Web Service Security

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Scalable SAT Solving in the Cloud 24th

Scalable SAT Solving in the Cloud

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24th international conference on Theory and applications of Satisfiability Testing (SAT)

作者： Schreiber, Dominik Sanders, Peter Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783030802233;9783030802226

Previous efforts on making Satisfiability (SAT) solving fit for high performance computing (HPC) have led to super-linear speedups on particular formulae, but for most inputs cannot make efficient use of a large number of processors. Moreover, long latencies (minutes to days) of job scheduling make large-scale SAT solving on demand impractical for most applications. We address both issues with Mallob, a framework for job scheduling in the context of SAT solving which exploits malleability, i.e., the ability to add or remove processing power from a job during its computation. Mallob includes a massively parallel, distributed, and malleable SAT solving engine based on HordeSat with a more succinct and communication-efficient approach to clause sharing and numerous further improvements over its precursor. Experiments with up to 2560 cores show that Mallob outperforms an improved version of HordeSat and scales significantly better. Moreover, Mallob can solve many formulae in parallel while dynamically adapting the assigned resources, and jobs arriving in the system are usually initiated within a fraction of a second.

关键词： parallel SAT solving distributed computing Malleable load balancing

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Multi-Population-based parallelization of Ensemble of Differential Evolution Variants for Constrained Real Parameter Optimization

Multi-Population-based Parallelization of Ensemble of Differ...

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international conference on Embedded & distributed Systems (EDiS)

作者： Leyla Belaiche Laid Kahloul Manel Houimli Selma Sahraoui Saber Benharzallah Maroua Grid Nedjma Abidallah LINFI Laboratory University of Biskra Biskra Algeria University of Biskra Biskra Algeria LAMIE Laboratory University of Biskra Batna Algeria

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.

关键词： Codes Sociology parallel processing Main-secondary Statistics Optimization Faces

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An Open-Source Virtualization Layer for CUDA applications

An Open-Source Virtualization Layer for CUDA Applications

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Eiling, Niklas Lankes, Stefan Monti, Antonello Rhein Westfal TH Aachen E ON Energy Res Ctr Inst Automat Complex Power Syst Aachen Germany

ISBN: (纸本)9783030715939;9783030715922

GPUs have achieved widespread adoption for High-Performance Computing and Cloud applications. However, the closed-source nature of CUDA has hindered the development of otherwise commonly used virtualization techniques. In this paper, we evaluate the feasibility of building a GPU virtualization layer that isolates the GPU and CPU parts of CUDA applications to achieve better control of the interactions between applications and the CUDA libraries. We present our open-source tool that transparently intercepts CUDA library calls and executes them in a separate process using remote procedure calls. This allows the execution of CUDA applications on machines without a GPU and provides a basis for the development of tools that require fine-grained control of the GPU resources, such as checkpoint/restore and job schedulers.

关键词： GPU Virtualization CUDA Remote execution

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Efficient processing of Streaming Data using Multiple Abstractions 14

Efficient Processing of Streaming Data using Multiple Abstra...

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IEEE 14th international conference on Cloud Computing (CLOUD)

作者： Qadeer, Abdul Heidemann, John Univ Southern Calif Informat Sci Inst Los Angeles CA 90007 USA

ISBN: (纸本)9781665400602

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions-these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

关键词： programming model abstraction big-data abstraction large-block streaming programmer productivity

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Proceedings of PAW-ATM 2020: 3rd Annual parallel applications Workshop, Alternatives to MPI+X, Held in conjunction with SC 2020: The international conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of PAW-ATM 2020: 3rd Annual Parallel Application...

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3rd IEEE/ACM Annual parallel applications Workshop, Alternatives to MPI+X, PAW-ATM 2020

ISBN: (纸本)9781728154503

The proceedings contain 6 papers. The topics discussed include: hedgehog: understandable scheduler-free heterogeneous asynchronous multithreaded data-flow graphs;TaskTorrent: a lightweight distributed task-based runtime system in C++;evaluation of multiple HPC parallelization frameworks in a shallow water proxy application with multi-rate local time stepping;an implicitly parallel meshfree solver in regent;HOOVER: leveraging OpenSHMEM for high performance, flexible streaming graph applications;and exploring hybrid MPI+Kokkos tasks programming model.

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Cloud-Based Smart Energy Framework for Accelerated Data Analytics with parallel Computing of Orchestrated Containers: Study Case of CU-BEMS 3

Cloud-Based Smart Energy Framework for Accelerated Data Anal...

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3rd international conference on Advanced Information Science and System, AISS 2021

作者： Saengkaenpetch, Kittipat Aswakul, Chaodit Thailand and Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Thailand Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Thailand

ISBN: (纸本)9781450385862

This paper proposes a practical smart energy framework for data analytic on energy management system at Chulalongkorn University, called CU-BEMS. This serves as an example of demand-sided smart energy application that copes with the challenges of big data analytic and real-time processing needs. The framework is based on the divide and conquer paradigm to accelerate data analytics with parallel computing. The workload is containerized and deployed on the Kubernetes cloud facility of our internationally collaborated IoTcloudServe@TEIN playground. With this playground, the workload scalability and portability can be achieved. Applying the proposed framework, this paper reports on a practical data log analysis to determine the wasted energy consumption. Based on the experimental result, the wasted energy consumption of the whole data set of CU-BEMS's communication research laboratory area from March 2014 to August 2017 can be computed within 81 seconds by using 32 cores running in parallel. The framework is expected to serve as a basis template for further research ongoing at CU-BEMS and smart energy applications that can be computationally enhanced by data analytic pipelining with containerized services as orchestrated by Kubernetes. © 2021 ACM.

关键词： Data Analytics

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Automatic Low-Overhead Load-Imbalance Detection in MPI applications 27th

Automatic Low-Overhead Load-Imbalance Detection in MPI Appli...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Arzt, Peter Fischler, Yannic Lehr, Jan-Patrick Bischof, Christian Tech Univ Darmstadt Dept Comp Sci Sci Comp Darmstadt Germany

ISBN: (纸本)9783030856656;9783030856649

Load imbalances are a major reason for efficiency loss in highly parallel applications. Hence, their identification is of high relevance in performance analysis and tuning. We present a low-overhead approach to automatically identify load-imbalanced regions and filter out irrelevant ones based on new selection heuristics in our PIRA tool for automatic instrumentation refinement for the Score-P measurement system. For the LULESH mini-app as well as the Ice-sheet and Sea-level System Model simulation package we, thus, correctly identify existing load imbalances while maintaining a runtime overhead of less than 10% for all but one input. Moreover, the traces generated are suitable for Scalasca's automatic trace analysis.

关键词： Score-P Load imbalance HPC Performance analysis

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27th international European conference on parallel and distributed Computing, Euro-Par 2021

27th International European Conference on Parallel and Distr...

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27th international European conference on parallel and distributed Computing, Euro-Par 2021

ISBN: (纸本)9783030856649

The proceedings contain 38 papers. The special focus in this conference is on parallel and distributed Computing. The topics include: An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architectures;pipelined Model parallelism: Complexity Results and Memory Considerations;efficient and Systematic Partitioning of Large and Deep Neural Networks for parallelization;A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks;Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs;smart distributed DataSets for Stream processing;colony: parallel Functions as a Service on the Cloud-Edge Continuum;horizontal Scaling in Cloud Using Contextual Bandits;geo-distribute Cloud applications at the Edge;Automatic Low-Overhead Load-Imbalance Detection in MPI applications;A Fault Tolerant and Deadline Constrained Sequence Alignment Application on Cloud-Based Spot GPU Instances;sustaining Performance While Reducing Energy Consumption: A Control Theory Approach;algorithm Design for Tensor Units;a Scalable Approximation Algorithm for Weighted Longest Common Subsequence;TSLQueue: An Efficient Lock-Free Design for Priority Queues;G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU;accelerating Graph applications Using Phased Transactional Memory;Efficient GPU Computation Using Task Graph parallelism;towards High Performance Resilience Using Performance Portable Abstractions;Enhancing Load-Balancing of MPI applications with Workshare;trace-Based Workload Generation and Execution;particle-In-Cell Simulation Using Asynchronous Tasking;Exploiting Co-execution with OneAPI: Heterogeneity from a Modern Perspective;designing a 3D parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems;Fault-Tolerant LU Factorization Is Low Cost;Mixed Precision Incomplete and Factorized Sparse Approximate Inverse Preconditioning on GPUs;GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis.

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