Performance Analysis of Parallel Python Applications

Wagner, Michael; Llort, Germán; Mercadal, Estanislao; Giménez, Judit; Labarta, Jesús

doi:10.1016/j.procs.2017.05.203

Cited by 18 publications

(10 citation statements)

References 3 publications

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“…In the past, it was difficult to collect precisely the data related to energy consumption; however, the Industry 4.0 technologies (CPS, IoT, and cloud computing) enhance this process [45]. For this reason, the machine needs to be integrated into a cyber-physical system for ECL calculations in a machine tool so that energy consumption data can be obtained through IoT technology in real time and then treated and used for ECL calculations by Python, a programming language that allows the development of software quickly and easily [46].…”

Section: Cyber-physical System Implementationmentioning

confidence: 99%

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

et al. 2020

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This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

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Section: Cyber-physical System Implementationmentioning

confidence: 99%

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

et al. 2020

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“…Once the CPS is implemented the acquired data are treated and used for KPI calculations by Python. This programming language has been used due to its capability to develop software quickly and easily [54].…”

Section: Cps Implementationmentioning

confidence: 99%

Development of a New KPI for the Economic Quantification of Six Big Losses and Its Implementation in a Cyber Physical System

et al. 2020

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The purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.

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“…The benefits of using Extrae and Paraver to evaluate the performance of parallel applications and architectures has been thoroughly proved in the context of HPC systems [19,32,43]. However, there exist a number of challenges to benefit from the virtues of Extrae in embedded systems: (1) adapt Extrae to work in constrained embedded systems, and (2) augment Extrae with the features needed to evaluate the non-functional characteristics of the system, so these can be related with the parallel programming model and the performance.…”

Section: Analysis Tools: Extrae/paravermentioning

confidence: 99%

Experiences on the characterization of parallel applications in embedded systems with Extrae/Paraver

Munera

Royuela

Llort

et al. 2020

49th International Conference on Parallel Processing - ICPP

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Cutting-edge functionalities in embedded systems require the use of parallel architectures to meet their performance requirements. This imposes the introduction of a new layer in the software stacks of embedded systems: the parallel programming model. Unfortunately, the tools used to analyze embedded systems fall short to characterize the performance of parallel applications at a parallel programming model level, and correlate this with information about non-functional requirements such as real-time, energy, memory usage, etc. HPC tools, like Extrae, are designed with that level of abstraction in mind, but their main focus is on performance evaluation. Overall, providing insightful information about the performance of parallel embedded applications at the parallel programming model level, and relate it to the non-functional requirements, is of paramount importance to fully exploit the performance capabilities of parallel embedded architectures. This paper contributes to the state-of-the-art of analysis tools for embedded systems by: (1) analyzing the particular constraints of embedded systems compared to HPC systems (e.g., static setting, restricted memory, limited drivers) to support HPC analysis tools; (2) porting Extrae, a powerful tracing tool from the HPC domain, to the GR740 platform, a SoC used in the space domain; and (3) augmenting Extrae with new features needed to correlate the parallel execution with the following non-functional requirements: energy, temperature and memory usage. Finally, the paper presents the usefulness of Extrae to characterize OpenMP applications and its non-functional requirements, evaluating different aspects of the applications running in the GR740. CCS CONCEPTS • Software and its engineering → Parallel programming languages; Empirical software validation; Software performance; • Computer systems organization → Embedded systems; • Hardware → Power estimation and optimization; Temperature monitoring.

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Performance Analysis of Parallel Python Applications

Cited by 18 publications

References 3 publications

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Development of a New KPI for the Economic Quantification of Six Big Losses and Its Implementation in a Cyber Physical System

Experiences on the characterization of parallel applications in embedded systems with Extrae/Paraver

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