Modeling energy consumption for master–slave applications

Almeida, Francisco; Blanco, Vicente; Cabrera, Alberto; Ruiz, Javier Zazo

doi:10.1007/s11227-013-0914-y

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…EML has been successfully used to obtain energy analytical models [51], showing that it is viable to get structural and algorithmic parameters that affect energy consumption and efficiency. Analytical energy models have been achieved for master-slave applications [52], the High-Performance Linpack benchmark [53], and MPI communications OSU microbenchmarks [54]. From experience, we learnt that it is possible to obtain analytical models for energy consumption suitable for scheduling purposes, performance prediction, or scalability analysis.…”

Section: Results and Lessons Learntmentioning

confidence: 99%

Energy monitoring as an essential building block towards sustainable ultrascale systems

Almeida

Assunção

Barbosa

et al. 2018

Sustainable Computing: Informatics and Systems

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An Ultrascale System (USS) joins parallel and distributed computing systems that will be two to three orders of magnitude larger than today's infrastructure regarding scale, performance, the number of components and their complexity. For such systems to become a reality, however, advances must be made in High Performance Computing (HPC), largescale distributed systems, and big data solutions, also tackling challenges such as improving the energy efficiency of the IT infrastructure. Monitoring the power consumed by underlying IT resources is essential towards optimising the manner IT resources are used and hence improve the sustainability of such systems. Nevertheless, monitoring the energy consumed by USSs is a challenging endeavour as the system can comprise thousands of heterogeneous server resources spanning multiple data centres. Moreover, the amount of monitoring data, its gathering, and processing, should never become a bottleneck nor profoundly impact the energy efficiency of the overall system. This work surveys state of the art on energy monitoring of large-scale systems and methodologies for monitoring the power consumed by large systems and discusses some of the challenges to be addressed towards monitoring and improving the energy efficiency of USSs. Next, we present efforts made on designing monitoring solutions $ . Finally, we discuss potential gaps in existing solutions when tackling emerging large-scale monitoring scenarios and present some directions for future research on the topic.

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Section: Results and Lessons Learntmentioning

confidence: 99%

Energy monitoring as an essential building block towards sustainable ultrascale systems

Almeida

Assunção

Barbosa

et al. 2018

Sustainable Computing: Informatics and Systems

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“…In work [7] authors have presented a detailed energy usage model for parallel master-slave applications, including modeling energy consumption of communication operations, based on execution times. Furthermore, the model was verified in a real environment with a master and 4 or 6 slaves for single or dual core configurations with error rate lower than 4% across the tested configurations.…”

Section: Related Workmentioning

confidence: 99%

Modeling energy consumption of parallel applications

Czarnul

Kuchta

Rościszewski

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-The paper presents modeling and simulation of energy consumption of two types of parallel applications: geometric Single Program Multiple Data (SPMD) and divide-and-conquer (DAC). Simulation is performed in a new MERPSYS (Modeling Efficiency, Reliability and Power consumption of multilevel parallel HPC SYStems using CPUs and GPUs) environment. Model of an application uses the Java language with extensions representing message exchange between processes working in parallel. Simulation is performed by running threads representing distinct process codes of an application, with consideration of process counts. Instead of running time consuming calculations, their times are simulated using functions representing computational time dependent on input data sizes. The simulator considers performance and power consumption values for compute devices stored in its database. We performed verification of running the two applications on up to 512 and 1024 processes respectively on a large cluster from Academic Computer Center in Gdansk demonstrating a high degree of accuracy between simulated and measured results.

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“…In the past yeas, several classical heuristic algorithm have been proposed for solving this problem. Unfortunately, early studies mainly concentrate on system performance, i.e., throughput [21], load-balance [22], response time [16][17][18][19] and etc., instead of energy efficiency. Recently, researchers began to take more efforts on energy saving when scheduling workflow applications.…”

Section: Related Workmentioning

confidence: 99%