[LA-SiGMA-gpu] Doodle poll - Supada Laosooksathit (candidate to lead the GPU team) will talk at tomorrow 11am

[sjlee at cct.lsu.edu]

Hello Everyone,

Please click on the doodle poll link below to indicate when you can  
meet with Supada Laosooksathit tomorrow.

Please note, that each individual scheduled to meet with Supada  
Laosooksathit is responsible for escorting her to her next  
appointment. If you cannot do so, please arrange for an escort.

http://www.doodle.com/7pmmi9vmatz7si86

Regards,
Shelley Lee
Project Coordinator
578-0465
----- Forwarded message from phy.kaming at gmail.com -----
     Date: Tue, 4 Jun 2013 15:35:34 -0400
     From: Ka Ming Tam <phy.kaming at gmail.com>
Reply-To: Ka Ming Tam <phy.kaming at gmail.com>
  Subject: [LA-SiGMA-gpu] Supada Laosooksathit (candidate to lead the  
GPU team) will talk at tomorrow 11am
       To: lasigma-gpu at loni.org
       Cc: Bety Rodriguez-Milla <brodrig at cct.lsu.edu>

Dear GPU Group Members,

A candidate for replacing Zhifeng to lead the GPU team, Supada
Laosooksathit,
will give a presentation tomorrow 11am at 338 Johnston.

Please see the following for the title and abstract of her talk.

Best,
Ka-Ming

Time : 11am June 5th (tomorrow).

Place : 338 Johnston

Title : Performance Model for Predicting Run-time of a GPGPU Application

Abstract:

Due to the fact that the reliability of very large scaled systems is
inversely related to the number of computing elements, fault tolerance has
become a major concern in high performance computing (HPC) including the
most recent deployment with GPUs. Many fault tolerance strategies, such as
checkpoint/restart mechanism, have been studied in order to mitigate
failures and the effectiveness.

This talk provides an idea to model techniques that explore interplay
between application performance and system reliability. More importantly,
these two parameters play significant roles toward an optimal outcome in
mitigating faults in a very large system. We taken into account in our
fault tolerance strategies that balance between the application
time-to-completion, and the time-to-failure (TTF) of the system. The former
factor can be estimated by a performance model while the latter can be
approximated by a reliability model.


----- End forwarded message -----

-------------- next part --------------
Dear GPU Group Members,

A candidate for replacing Zhifeng to lead the GPU team, Supada
Laosooksathit,
will give a presentation tomorrow 11am at 338 Johnston.

Please see the following for the title and abstract of her talk.

Best,
Ka-Ming

Time : 11am June 5th (tomorrow).

Place : 338 Johnston

Title : Performance Model for Predicting Run-time of a GPGPU Application

Abstract:

Due to the fact that the reliability of very large scaled systems is
inversely related to the number of computing elements, fault tolerance has
become a major concern in high performance computing (HPC) including the
most recent deployment with GPUs. Many fault tolerance strategies, such as
checkpoint/restart mechanism, have been studied in order to mitigate
failures and the effectiveness.

This talk provides an idea to model techniques that explore interplay
between application performance and system reliability. More importantly,
these two parameters play significant roles toward an optimal outcome in
mitigating faults in a very large system. We taken into account in our
fault tolerance strategies that balance between the application
time-to-completion, and the time-to-failure (TTF) of the system. The former
factor can be estimated by a performance model while the latter can be
approximated by a reliability model.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: https://mail.loni.org/mailman/private/lasigma-gpu/attachments/20130604/b3eba62a/attachment.html 
-------------- next part --------------
_______________________________________________
LASiGMA-gpu mailing list
LASiGMA-gpu at loni.org
https://mail.loni.org/mailman/listinfo/lasigma-gpu
wiki: https://wiki.cct.lsu.edu/la-sigma/
http://www.institute.loni.org/lasigma/lasigma-gpu.php