Adaptive on-the-Fly Application Performance Modeling for Many Cores

2015 ◽  
Author(s):  
Sebastian Kobbe ◽  
Lars Bauer ◽  
Jörg Henkel
Keyword(s):  
Performance Modeling ◽  
Application Performance

scholarly journals FECBench: A Holistic Interference-aware Approach for Application Performance Modeling

2019 ◽  
Author(s):  
Yogesh D. Barve ◽  
Shashank Shekhar ◽  
Ajay Chhokra ◽  
Shweta Khare ◽  
Anirban Bhattacharjee ◽  
...  
Keyword(s):  
Performance Modeling ◽  
Application Performance

scholarly journals Adaptive Learning for Concept Drift in Application Performance Modeling

2019 ◽  
Author(s):  
Sandeep Madireddy ◽  
Prasanna Balaprakash ◽  
Philip Carns ◽  
Robert Latham ◽  
Glenn K. Lockwood ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Performance Modeling ◽  
Concept Drift ◽  
Application Performance

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.

scholarly journals USE OF PREDICTIVE PERFORMANCE MODELING DURING LARGE-SCALE SYSTEM INSTALLATION

2005 ◽  
Vol 15 (04) ◽  
pp. 387-395 ◽  
Author(s):  
DARREN J. KERBYSON ◽  
ADOLFY HOISIE ◽  
HARVEY J. WASSERMAN
Keyword(s):  
Adaptive Mesh Refinement ◽  
Large Scale ◽  
Performance Modeling ◽  
Adaptive Mesh ◽  
Predictive Performance ◽  
Performance Model ◽  
Pci Bus ◽  
Application Performance ◽  
Large Scale System ◽  
Real Performance

In this paper we describe an important use of predictive application performance modeling - the validation of measured performance during a new large-scale system installation. Using a previously-developed and validated performance model for SAGE, a multidimensional, 3D, multi-material hydrodynamics code with adaptive mesh refinement, we were able to help guide the stabilization of the first phase of the Los Alamos ASCI Q supercomputer. We review the salient features of an analytical model for this code that has been applied to predict its performance on a large class of Tera-scale parallel systems. We describe the methodology applied during system installation and upgrades to establish a baseline for the achievable "real" performance of the system. We also show the effect on overall application performance of certain key subsystems such as PCI bus speed and multi-rail networks. We show that utilization of predictive performance models is also a powerful system debugging tool.

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