2016 2nd International Conference on Green High Performance Computing (ICGHPC)最新文献

Extracting usage patterns from web server log 从web服务器日志中提取使用模式

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-07-11 DOI: 10.1109/ICGHPC.2016.7508074

J. Monisha, P. Jeba, M. Bhuvaneswari, K. Muneeswaran

引用次数: 7

Optimized programmable hardware scheduler for reconfigurable MPSoCs 优化可编程硬件调度程序的可重构mpsoc

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508063

P. Lalley, T. Latha

{"title":"Optimized programmable hardware scheduler for reconfigurable MPSoCs","authors":"P. Lalley, T. Latha","doi":"10.1109/ICGHPC.2016.7508063","DOIUrl":"https://doi.org/10.1109/ICGHPC.2016.7508063","url":null,"abstract":"Embedded System plays a vital role in consumer Industry. Complex applications need systems which contains multiple heterogeneous processors, running in parallel to speed up the system. Also due to area constraints, the processors are evolved in a single System on Chip called Multiprocessor System on Chip (MPSoC). The system should be reusable and debuggable, hence the designers designed and developed Reconfigurable MPSoCs rather than Application Specific Integrated Circuits (ASIC) in Field Programmable Gate Arrays (FPGA). Multiprocessor System on Chip (MPSoC) platform plays a vital role in parallel processor architecture design. However the growth of number of processing elements in one chip, task decomposition and scheduling become major bottlenecks of MPSoC architecture. To execute the applications, the application software is splitted as tasks and mapped to the different available processors and scheduled the tasks as when to execute in the available processors when the resources are ready. Selection of most suitable candidates for execution in a particular processor is very much important. Hardware related tasks are executed in different hardware accelerators and software tasks in processors. The area occupied by the schedulers in memory is more in internal memory. For scheduling these tasks, a programmable hardware is developed as hardware scheduler in the reconfigurable MPSoC using NIOS II processor. The algorithm for optimized scheduling in the target architecture is proposed. The literature survey is made with the hardware scheduler and new target MPSoC architecture. Quartus II version 12.1 and SOPC Builder are used to configure the NIOS II processer. Nios II EDS software tool has been used to build the application code.","PeriodicalId":268630,"journal":{"name":"2016 2nd International Conference on Green High Performance Computing (ICGHPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127521149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic and adaptive load balancing in transaction oriented grid service 面向事务网格服务的动态自适应负载平衡

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508067

D. P. Mahato, A. Maurya, A. Tripathi, Ravi Shankar Singh

引用次数: 12

An architecture for flexible auto-tuning: The Periscope Tuning Framework 2.0 灵活的自动调优架构:Periscope调优框架2.0

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508066

Robert Mijakovic, Michael Firbach, M. Gerndt

{"title":"An architecture for flexible auto-tuning: The Periscope Tuning Framework 2.0","authors":"Robert Mijakovic, Michael Firbach, M. Gerndt","doi":"10.1109/ICGHPC.2016.7508066","DOIUrl":"https://doi.org/10.1109/ICGHPC.2016.7508066","url":null,"abstract":"Due to the complexity and diversity of new parallel architectures, automatic tuning of parallel applications has become increasingly important for achieving acceptable performance levels, as well as performance portability. The European AutoTune project developed a tuning framework that closely integrates and automates performance analysis and performance tuning. The Periscope Tuning Framework (PTF) relies on a flexible plugin mechanism and provides tuning plugins for various different tuning aspects. Each plugin provides codified expert knowledge for performance or energy efficiency tuning. PTF is able to tune serial and parallel codes for homogeneous and heterogeneous target hardware. The output of the framework is tuning recommendations that can be integrated into the production version of the code. In this paper, we present the latest development in the design of PTF aiming at (1) achieving higher portability and scalability by using the Score-P measurement infrastructure, (2) extending Score-P with tuning capabilities, (3) increasing analysis capabilities by providing new analysis strategies, and (4) increasing tuning capabilities by providing new plugins.","PeriodicalId":268630,"journal":{"name":"2016 2nd International Conference on Green High Performance Computing (ICGHPC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123533012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Event attributed Spatial Entity Knowledge (EASE) based Spatio-Temporal reasoning to infer geographic processes 基于事件属性空间实体知识(EASE)的时空推理对地理过程的推断

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508072

Jayanthi G, V Uma

{"title":"Event attributed Spatial Entity Knowledge (EASE) based Spatio-Temporal reasoning to infer geographic processes","authors":"Jayanthi G, V Uma","doi":"10.1109/ICGHPC.2016.7508072","DOIUrl":"https://doi.org/10.1109/ICGHPC.2016.7508072","url":null,"abstract":"Remote Sensing of resource in a geographic space at regular temporal intervals has paved way for the evolution of geo-spatial information processing. Knowledge engineering of facts acquired through this technology primarily aims at qualitative results to support human in solving complex tasks that cannot be solved through quantitative relational query processing methods with Database Management Systems (DBMS). This necessitates the need for automated inference mechanism to be built over relational databases. Automated reasoning, a systematic process of formal symbolic representation to codify the acquired facts enables the system to infer new knowledge which can further update the facts. A formal representation of Event Attributed Spatial Entity (EASE) Knowledge base is proposed using the theory of Allen's Interval calculus and Randel's RCC-8. The objective of the proposed knowledge base is to formalize spatial entities in a geographic region whose temporal attributes are events occurring in an interval, at time instant and over successive intervals to qualitatively answer the event-based queries on prediction of spatial process. The significance of this formal approach is shown using query evaluation on real datasets. The working of proposed knowledge base is explained with illustrative results. Towards the end of this work, the direction for enhancement of EASE to explore its use is discussed.","PeriodicalId":268630,"journal":{"name":"2016 2nd International Conference on Green High Performance Computing (ICGHPC)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130219175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

DVFS automatic tuning plugin for energy related tuning objectives DVFS自动调谐插件能源相关的调谐目标

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508061

Carla Guillén, C. Navarrete, D. Brayford, Wolfram Hesse, Matthias Brehm

引用次数: 6

A scalable GPU-enabled framework for training deep neural networks 用于训练深度神经网络的可扩展gpu支持框架

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508071

Bonaventura Del Monte, R. Prodan

{"title":"A scalable GPU-enabled framework for training deep neural networks","authors":"Bonaventura Del Monte, R. Prodan","doi":"10.1109/ICGHPC.2016.7508071","DOIUrl":"https://doi.org/10.1109/ICGHPC.2016.7508071","url":null,"abstract":"In the last fifteen years, Big Data created a new generation of data analysis problems, which does not only involve the problems themselves but also the way these data are handled. Since managing terabytes of data without a proper infrastructure is unfeasible, a smart way to process these data is also necessary. A solution to this aspect deals with the creation of general algorithms that learn from observations. In this context, Deep Learning promises general, powerful, and fast machine learning algorithms, moving them one step closer to artificial intelligence. Nevertheless, fitting a deep learning model may require an huge amount of time, thus, the need of scalable infrastructures for processing large scale data sets has become ever more meaningful. In this paper, we present a framework for training these deep neural networks using heterogeneous computing resources of either grid or cloud infrastructures. The framework lets the end-users define the deep architecture they need for processing their own Big Data, while dealing with the execution of the learning algorithms on a distributed set of nodes (through Apache Flink) as well as with offloading the computation on multiple Graphics Processing Units.","PeriodicalId":268630,"journal":{"name":"2016 2nd International Conference on Green High Performance Computing (ICGHPC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Message from the organizing chair 来自组织主席的信息

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508055

Shajulin Benedict

引用次数: 0

A Modified Ant Colony based optimization for managing Cloud resources in manufacturing sector 基于改进蚁群的制造业云资源管理优化

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508068

N. Brintha, J. Jappes, S. Benedict

{"title":"A Modified Ant Colony based optimization for managing Cloud resources in manufacturing sector","authors":"N. Brintha, J. Jappes, S. Benedict","doi":"10.1109/ICGHPC.2016.7508068","DOIUrl":"https://doi.org/10.1109/ICGHPC.2016.7508068","url":null,"abstract":"Resource scheduling and management is an important problem in Cloud Manufacturing. The concept of optimization for scheduling jobs is an important issue to be considered in scheduling of different resources among heterogeneous users. The resources are placed across diversified locations in cloud and the major task is to distribute the resources effectively such that the makespan and completion time is reduced. In this paper, a Modified Ant Colony based optimization technique is proposed to optimize the resources through distributed computation. ACO is used to choose one among the different alternative rules to determine the processing order of each resource. Rather than having a larger search space, this approach reduces the search space and gives better solution. This reduces the delay in allocating resources to the user by providing an adaptive and global search technique. This approach reduces the total completion time of jobs and also takes in to account the migration time of the process. A series of experiments were conducted and the results of the experiment are compared with other heuristic algorithms like PSO. The results have shown that this approach can produce optimal solutions quickly by reducing delays.","PeriodicalId":268630,"journal":{"name":"2016 2nd International Conference on Green High Performance Computing (ICGHPC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114696480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Fuzzy association rule based Cluster head selection in wireless Sensor Network 基于模糊关联规则的无线传感器网络簇头选择

2016 2nd International Conference on Green High Performance Computing (ICGHPC) Pub Date : 2016-02-01 DOI: 10.1109/ICGHPC.2016.7508073

S. Nalini, A. Valarmathi

引用次数: 2