the problems of creating complex IT on the example of distributed information systems are considered. Also there was reviewed the structure of processes for elements interaction while creating such systems. Proposed a...
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ISBN:
(纸本)9781538606971
the problems of creating complex IT on the example of distributed information systems are considered. Also there was reviewed the structure of processes for elements interaction while creating such systems. Proposed an model of change impact on project elements in complex projects management and was suggested an approach to configuration management of such projects, that makes it possible to build a scheme for the process of effective project management.
Withthe development of mobile network and corresponding techniques, more and more works focus on providing efficient services based on mobile devices. Furthermore, motivated by IoT, studies of local distributed mobil...
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ISBN:
(纸本)9781538607527
Withthe development of mobile network and corresponding techniques, more and more works focus on providing efficient services based on mobile devices. Furthermore, motivated by IoT, studies of local distributed mobile devices attract attentions of both industry and academia in recent years. However, existing storage systems cannot manage data and support the QoS of mobile services well. this paper presents LKSM, a light weight key-value storage system, which can be deployed on either one node or multiple nodes. To the best of our knowledge, it is the first attempt to propose key-value store in this scenario. We carefully analyze the challenges when designing the system on mobile cluster, and further propose RDS for addressing. Withthe help of RDS, LKSM achieves the goal of lower latency, better scalability, and higher availability. We organize LKSM using a log-structured merge-tree, and implement it based on LevelDB, an open source key-value storage system proposed by Google. Experiments on physical smartphones demonstrate that LKSM presents much higher performance compared withthe ported LevelDB on mobile devices.
Presents the introductory welcome message from the conferenceproceedings. May include the conference officers' congratulations to all involved withthe conference event and publication of the proceedings record.
Presents the introductory welcome message from the conferenceproceedings. May include the conference officers' congratulations to all involved withthe conference event and publication of the proceedings record.
Summary form only given, as follows. the complete presentation was not made available for publication as part of the conferenceproceedings. What constitutes a “Big Data” problem? What application domains are best s...
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ISBN:
(纸本)9781538614402
Summary form only given, as follows. the complete presentation was not made available for publication as part of the conferenceproceedings. What constitutes a “Big Data” problem? What application domains are best suited to benefit from Big Data analytics and computing? What are the traits and characteristics of an application that make it suited to exploit Big Data analytics? How can Big Data systems and frameworks be designed to allow the integration and analysis of complex data sets? How can research in Big Data Analytics benefit from the latest advances in supercomputing and High Performance computing (HPC) architectures? the goal of this workshop is to address questions like these that are fundamental to the advancement of Big Data computing, and in the process, build a diverse research community that has a shared vision to advance the state of knowledge and discovery through Big Data computing.
distributed algorithms for data analytics partition their input data across many machines for parallel execution. At scale, it is likely that some machines will perform worse than others because they are slower, power...
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ISBN:
(纸本)9781538610428
distributed algorithms for data analytics partition their input data across many machines for parallel execution. At scale, it is likely that some machines will perform worse than others because they are slower, power constrained or dependent on undesirable, dirty energy sources. It is challenging to balance analytics workloads across heterogeneous machines because the algorithms are sensitive to statistical skew in data partitions. A skewed partition can slow down the whole workload or degrade the quality of results. Sizing partitions in proportion to each machine's performance may introduce or further exacerbate skew. In this paper, we propose a scheme that controls the statistical distribution of each partition and sizes partitions according to the heterogeneity of the computing environment. We model heterogeneity as a multi-objective optimization, withthe objectives being functions for execution time and dirty energy consumption. We use stratification to control skew. Experiments show that our computational heterogeneity-aware (Het-Aware) partitioning strategy speeds up running time by up to 51% over the stratified partitioning scheme baseline. We also have a heterogeneity and energy aware (Het-Energy-Aware) partitioning scheme which is slower than the Het-Aware solution but can lower the dirty energy footprint by up to 26%. For some analytic tasks, there is also a significant qualitative benefit when using such partitioning strategies.
Upcoming HPC clusters will feature hybrid memories and storage devices per compute node. In this work, we propose to use the MPI one-sided communication model and MPI windows as unique interface for programming memory...
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SkePU is a state-of-the-art skeleton programming library for high-level portable programming and efficient execution on heterogeneous parallel computer systems, with a publically available implementation for general-p...
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ISBN:
(纸本)9781467387767
SkePU is a state-of-the-art skeleton programming library for high-level portable programming and efficient execution on heterogeneous parallel computer systems, with a publically available implementation for general-purpose multicore CPU and multi-GPU systems. this paper presents the design, implementation and evaluation of a new back-end of the SkePU skeleton programming library for the new low-power multicore processor Myriad2 by Movidius Ltd. this enables seamless code portability of SkePU applications across both HPC and embedded (Myriad2) parallel computingsystems, with decent performance, on these architecturally very diverse types of execution platforms.
the extreme scale, complexity and performance variability of future high performance computingsystems pose many new challenges to parallel programming models and runtime systems. the Open Community Runtime (OCR) is a...
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ISBN:
(纸本)9781467387767
the extreme scale, complexity and performance variability of future high performance computingsystems pose many new challenges to parallel programming models and runtime systems. the Open Community Runtime (OCR) is a recent effort for a task-based runtime system for extreme scale parallel systems. We have implemented the OCR specification in a shared-memory environment on top of TBB, providing an alternative to the implementation created by the OCR consortium. We have created an experimental extension that supports parallel accelerators programmed with OpenCL. We also have an implementation that targets distributed-memory systems. Despite being in an early stage of development, our implementations can achieve reasonable performance with some applications. We describe the main aspects of our OCR implementations and report on early experimental results on shared-memory and distributed-memory systems.
the increased computational needs in many sectors place huge demands on cloud computing. Power consumption and resource pool capacity are two of the challenges faced by the next generation of high performance computin...
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ISBN:
(纸本)9781467387767
the increased computational needs in many sectors place huge demands on cloud computing. Power consumption and resource pool capacity are two of the challenges faced by the next generation of high performance computing (HPC). this paper aims at minimising the computing-energy consumption in decentralised multi-cloud systems using Dynamic Voltage and Frequency Scaling (DVFS) when scheduling dependent HPC tasks under deadline constraints. We propose an energy-aware scheduling algorithm EAGS. To demonstrate the efficiency of our algorithm EAGS, we compare it withthe Cloud min-min Scheduling (CMMS) algorithm in different experiments. the simulation results show that our algorithm can produce energy consumption lower than CMMS by an average of 63.9%.
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