Predicting parallel applications performance using signatures: The workload effect

Canillas, J. Martinez; Wong, Alvaro; Rexachs, Dolores; Luque, Emilio

doi:10.1109/aiccsa.2011.6126624

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The signature is associated with the behavior of a specific application [4]. For example, if we want to predict another parallel application's execution time or change the data set, the signature must be generated again.…”

Section: Proposed Methodologymentioning

confidence: 99%

Scalable performance analysis method for SPMD applications

et al. 2022

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The analysis of parallel scientific applications allows us to understand their computational and communication behavior. One way of obtaining performance information is through performance tools. One such tool is parallel application signatures for performance prediction (PAS2P), based on parallel application repeatability, focusing on performance analysis and prediction. The same resources that execute the parallel application are used to perform its analysis, creating a machine independent model of the application and identifying its common patterns. However, the analysis is costly in terms of execution time due to the high number of synchronization communications performed by PAS2P, degrading performance as the number of processes increases. To solve this problem, we propose a model that reduces data dependency between processes, reducing the number of communications performed by PAS2P in the analysis stage and taking advantage of the characteristics of single program, multiple sata applications. Our analysis proposal allows us to decrease the analysis time by 29 times when the application scales to 256 processes, while keeping error levels below 11% in the runtime prediction. It is important to mention that the analysis time is not considerably affected by increasing the number of application processes.

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Section: Proposed Methodologymentioning

confidence: 99%

Scalable performance analysis method for SPMD applications

et al. 2022

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“…PAS2P analyzes the application behavior with a specific data set (workload), in a previous work [2], we explain how we can predict the performance changing the workloads.…”

Section: Introductionmentioning

confidence: 99%

Parallel Application Signature for Performance Analysis and Prediction

Wong

Rexachs

Luque

2015

IEEE Trans. Parallel Distrib. Syst.

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Predicting the performance of parallel scientific applications is becoming increasingly complex. Our goal was to characterize the behavior of message-passing applications on different target machines. To achieve this goal, we developed a method called parallel application signature for performance prediction (PAS2P), which strives to describe an application based on its behavior. Based on the application's message-passing activity, we identified and extracted representative phases, with which we created a parallel application signature that enabled us to predict the application's performance. We experimented with using different scientific applications on different clusters. We were able to predict execution times with an average accuracy greater than 97 percent.

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Predicting parallel applications performance using signatures: The workload effect

Cited by 2 publications

References 5 publications

Scalable performance analysis method for SPMD applications

Scalable performance analysis method for SPMD applications

Parallel Application Signature for Performance Analysis and Prediction

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