SCHEDULE: NOV 10-16, 2012
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A Practical Method for Estimating Performance Degradation on Multicore Processors and its Application to HPC Workloads
SESSION: Performance Modeling
EVENT TYPE: Papers
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Dimitris Nikolopoulos
AUTHOR(S):Tyler Dwyer, Alexandra Fedorova, Sergey Blagodurov, Mark Roth, Fabien Gaud, Jian Pei
ROOM:355-EF
ABSTRACT:
When multiple threads or processes run on a multicore CPU they compete
for shared resources, such as caches and memory controllers, and can
suffer performance degradation as high as 200%. We design and evaluate a
new machine learning model that estimates this degradation online, on
previously unseen workloads, and without perturbing the execution.
Our motivation is to help data center and HPC cluster operators
effectively use workload consolidation. Consolidation places many
runnable entities on the same server to maximize hardware utilization,
but may sacrifice performance as threads compete for resources. Our
model helps determine when consolidation is overly harmful to
performance. Our work is the first to apply machine learning to this
problem domain, and we report on our experience reaping the advantages
of machine learning while navigating around its limitations. We
demonstrate how the model can be used to improve performance fidelity
and save power for HPC workloads.
Chair/Author Details:
Dimitris Nikolopoulos (Chair) - Queen's University Belfast
Tyler Dwyer - Simon Fraser University
Alexandra Fedorova - Simon Fraser University
Sergey Blagodurov - Simon Fraser University
Mark Roth - Simon Fraser University
Fabien Gaud - Simon Fraser University
Jian Pei - Simon Fraser University
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A Practical Method for Estimating Performance Degradation on Multicore Processors and its Application to HPC Workloads
SESSION: Performance Modeling
EVENT TYPE:
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Dimitris Nikolopoulos
AUTHOR(S):Tyler Dwyer, Alexandra Fedorova, Sergey Blagodurov, Mark Roth, Fabien Gaud, Jian Pei
ROOM:355-EF
ABSTRACT:
When multiple threads or processes run on a multicore CPU they compete
for shared resources, such as caches and memory controllers, and can
suffer performance degradation as high as 200%. We design and evaluate a
new machine learning model that estimates this degradation online, on
previously unseen workloads, and without perturbing the execution.
Our motivation is to help data center and HPC cluster operators
effectively use workload consolidation. Consolidation places many
runnable entities on the same server to maximize hardware utilization,
but may sacrifice performance as threads compete for resources. Our
model helps determine when consolidation is overly harmful to
performance. Our work is the first to apply machine learning to this
problem domain, and we report on our experience reaping the advantages
of machine learning while navigating around its limitations. We
demonstrate how the model can be used to improve performance fidelity
and save power for HPC workloads.
Chair/Author Details:
Dimitris Nikolopoulos (Chair) - Queen's University Belfast
Tyler Dwyer - Simon Fraser University
Alexandra Fedorova - Simon Fraser University
Sergey Blagodurov - Simon Fraser University
Mark Roth - Simon Fraser University
Fabien Gaud - Simon Fraser University
Jian Pei - Simon Fraser University
Click here to download .ics calendar file