scholarly journals Systematic performance modeling and characterization of heterogeneous IP networks

2006 ◽  
Vol 72 (7) ◽  
pp. 1134-1143 ◽  
Author(s):  
Alessio Botta ◽  
Donato Emma ◽  
Antonio Pescapé ◽  
Giorgio Ventre
Keyword(s):  
Performance Modeling ◽  
Ip Networks

Towards Textbook Efficiency for Parallel Multigrid

2015 ◽  
Vol 8 (1) ◽  
pp. 22-46 ◽  
Author(s):  
Björn Gmeiner ◽  
Ulrich Rüde ◽  
Holger Stengel ◽  
Christian Waluga ◽  
Barbara Wohlmuth
Keyword(s):  
Finite Element ◽  
Saddle Point ◽  
Large Scale ◽  
Performance Modeling ◽  
Linear Equations ◽  
Massively Parallel ◽  
Classical View ◽  
Varying Coefficients ◽  
Modeling Techniques

In this work, we extend Achi Brandt's notion of textbook multigrid efficiency (TME) to massively parallel algorithms. Using a finite element based geometric multigrid implementation, we recall the classical view on TME with experiments for scalar linear equations with constant and varying coefficients as well as linear systems with saddle-point structure. To extend the idea of TME to the parallel setting, we give a new characterization of a work unit (WU) in an architecture-aware fashion by taking into account performance modeling techniques. We illustrate our newly introduced parallel TME measure by large-scale computations, solving problems with up to 200 billion unknowns on a TOP-10 supercomputer.

Characterization of the busy-hour traffic of IP networks based on their intrinsic features

2011 ◽  
Vol 55 (9) ◽  
pp. 2111-2125 ◽  
Author(s):  
José Luis García-Dorado ◽  
José Alberto Hernández ◽  
Javier Aracil ◽  
Jorge E. López de Vergara ◽  
Sergio Lopez-Buedo
Keyword(s):  
Ip Networks

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.

Morphological Characterization of Mycobacteriophage R1

1974 ◽  
Vol 32 ◽  
pp. 254-255
Author(s):  
B. L. Soloff ◽  
T. A. Rado
Keyword(s):  
Carbon Source ◽  
Stationary Phase ◽  
Growth Phase ◽  
Minimal Medium ◽  
Morphological Characterization ◽  
Logarithmic Growth Phase ◽  
Complete Lysis ◽  
Lysogenic Culture ◽  
Logarithmic Growth

Mycobacteriophage R1 was originally isolated from a lysogenic culture of M. butyricum. The virus was propagated on a leucine-requiring derivative of M. smegmatis, 607 leu−, isolated by nitrosoguanidine mutagenesis of typestrain ATCC 607. Growth was accomplished in a minimal medium containing glycerol and glucose as carbon source and enriched by the addition of 80 μg/ ml L-leucine. Bacteria in early logarithmic growth phase were infected with virus at a multiplicity of 5, and incubated with aeration for 8 hours. The partially lysed suspension was diluted 1:10 in growth medium and incubated for a further 8 hours. This permitted stationary phase cells to re-enter logarithmic growth and resulted in complete lysis of the culture.

Sign in / Sign up

Export Citation Format

Share Document