Exploiting Hardware Monitoring in Software Engineering

Walcott-Justice, Kristen

doi:10.1016/b978-0-12-800162-2.00002-6

Cited by 1 publication

(1 citation statement)

References 69 publications

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“…It is also compatible with perf events and is capable to profile: a single process, through direct command execution or an already running process using its identifier (PID); multiple processes, using system-wide profiling or CPU list. However, it supports only a maximum sampling granularity of 100 ms [9] and there is no support for Precision Event-Based Sampling (PEBS), degrading quality of results [10]. Also, it presents issues (application hang) with PID monitoring and processes that frequently create and destroy threads [11].…”

Section: Related Workmentioning

confidence: 99%

Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

et al. 2020

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Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former.

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Section: Related Workmentioning

confidence: 99%