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

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2162
Author(s):  
Wellington Silva-de-Souza ◽  
Arman Iranfar ◽  
Anderson Bráulio ◽  
Marina Zapater ◽  
Samuel Xavier-de-Souza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Image Classification ◽  
Configuration Space ◽  
Energy Savings ◽  
Video Transcoding ◽  
Next Generation ◽  
Performance Profiling ◽  
Run Time ◽  
And Performance

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.

scholarly journals Towards an Autonomous Framework for HPC Optimization: Using Machine Learning for Energy and Performance Modeling

2019 ◽  
Author(s):  
Vinícius Klôh ◽  
Matheus Gritz ◽  
Bruno Schulze ◽  
Mariza Ferro
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
High Performance ◽  
Performance Modeling ◽  
Application Performance ◽  
Ongoing Research ◽  
And Performance

Performance and energy efficiency are now critical concerns in high performance scientific computing. It is expected that requirements of the scientific problem should guide the orchestration of different techniques of energy saving, in order to improve the balance between energy consumption and application performance. To enable this balance, we propose the development of an autonomous framework to make this orchestration and present the ongoing research to this development, more specifically, focusing in the characterization of the scientific applications and the performance modeling tasks using Machine Learning.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

CLASS-BASED MACHINE LEARNING FOR NEXT GENERATION WELLBORE DATA PROCESSING AND INTERPRETATION

2019 ◽  
Author(s):  
Vikas Jain ◽  
Po-Yen Wu ◽  
Ridvan Akkurt ◽  
Brook Hodenfield ◽  
Tianmin Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Next Generation

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

An Analysis of Actual Energy Savings in an Indian Cement Industry Through an Energy Efficiency Index

2012 ◽  
Vol 9 (8) ◽  
pp. 829-840 ◽  
Author(s):  
R. Saidur ◽  
M. T. Sambandam ◽  
M. Hasanuzzaman ◽  
D. Devaraj ◽  
S. Rajakarunakaran
Keyword(s):  
Energy Efficiency ◽  
Energy Savings ◽  
Efficiency Index ◽  
Cement Industry

Increasing the energy efficiency of a data center based on machine learning

2021 ◽  
Author(s):  
Zhen Yang ◽  
Jinhong Du ◽  
Yiting Lin ◽  
Zhen Du ◽  
Li Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Center
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