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Titlebook: Algorithms and Architectures for Parallel Processing; 13th International C Joanna Ko?odziej,Beniamino Martino,Kaiqi Xiong Conference procee

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發(fā)表于 2025-3-21 17:03:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Algorithms and Architectures for Parallel Processing
期刊簡(jiǎn)稱13th International C
影響因子2023Joanna Ko?odziej,Beniamino Martino,Kaiqi Xiong
視頻videohttp://file.papertrans.cn/154/153092/153092.mp4
發(fā)行地址Fast-track conference proceedings.State-of-the-art research.Up to date results
學(xué)科分類Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Algorithms and Architectures for Parallel Processing; 13th International C Joanna Ko?odziej,Beniamino Martino,Kaiqi Xiong Conference procee
影響因子.This two volume set LNCS 8285 and 8286 constitutes the proceedings of the 13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013, held in Vietri sul Mare, Italy in December 2013. The first volume contains 10 distinguished and 31 regular papers selected from 90 submissions and covering topics such as big data, multi-core programming and software tools, distributed scheduling and load balancing, high-performance scientific computing, parallel algorithms, parallel architectures, scalable and distributed databases, dependability in distributed and parallel systems, wireless and mobile computing. The second volume consists of four sections including 35 papers from one symposium and three workshops held in conjunction with ICA3PP 2013 main conference. These are 13 papers from the 2013 International Symposium on Advances of Distributed and Parallel Computing (ADPC 2013), 5 papers of the International Workshop on Big Data Computing (BDC 2013), 10 papers of the International Workshop on Trusted Information in Big Data (TIBiDa 2013) as well as 7 papers belonging to Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-Smart CPS 2013)..
Pindex Conference proceedings 2013
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,Verbot der Rückzahlung von Stammkapital, instruction of a CUDA program which is not only portable but also accurate. With the selected PTX instructions based on empirical study, we apply linear regression to build the GPU energy model. One appealing advantage of our model is that it does not require any instrumentation or profiling of the
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https://doi.org/10.1007/3-540-27551-7rawback, we propose a dynamic resource management framework and mechanism to satisfy the requirements of both HPC and Cloud computing. Firstly we present a prediction model that is used to predict the arrival rate of all kinds of jobs (HPC types and Cloud types). Based on the prediction results, we
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Load and Thermal-Aware VM Scheduling on the Cloudboth load-balancing and temperature-balancing with a final goal of reducing the energy consumption in a Cloud centre. Using the strategy of VM migration it is ensured that none of the physical hosts suffers from either high temperature or over-utilization. The proposed scheduling mechanism has been
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