Computer Science

SCIENTIFIC APPLICATIONS PERFORMANCE MODELING AND PREDICTION, [Marlon
Bright^1^]{.underline}, Javier Delgado^1^, E. Javier Figueroa^1,2^, S.
Masoud Sadjadi\*^1^ , Florida International University^1^, School of
Computing and Information Sciences, Miami, FL 33199, University of
Miami^2^, College of Arts & Sciences, Coral Gables, FL 33146,
[sadjadi@cs.fiu.edu]{.underline}

To cope with the high demand for more accurate forecasts of weather,
over the past decades Meteorologists have developed and continued to
improve weather simulation software. Their latest attempt has resulted
in the Weather Research and Forecasting (WRF) model; a complex software
application with more than 160,000 lines of legacy Fortran 90 and C. WRF
is based on its predecessor, the Mesoscale Model 5 (MM5), inheriting its
architecture and older stub functions. Initially, as part of our effort
to enable WRF in a grid environment to provide more accurate forecast in
less time, we were obligated to conduct a detailed code inspection in
order to discover its actual functionality and flow of execution. We
continued the effort started by UCAR (University Corporation for
Atmospheric Research, the originator and coordinator of WRF code) to
develop a document that is aimed to give in-depth knowledge to those
software engineers who need to understand the functionality of WRF.
Moreover, once we gained sufficient understanding of this application we
initiated the original task of adapting WRF to a Grid computing
environment. Using a transparent approach to the code, we have been able
to profile WRF processor-power versus number-of-nodes behavior, and as a
result we developed a simplified mathematical model that predicts
application execution time in a distributed environment. The model was
implemented by the development of a set of monitoring and prediction
tools, most specially Amon (A Monitoring Tool) and Aprof (A Profiling
Tool). Amon is a process-monitoring program running on each compute node
of a cluster and Aprof is a regression analysis-based program running on
the head node of the same cluster or in an offline state. The latter is
capable of making predictions by using output data from Amon. To
validate our approach, experiments were conducted using WRF on two
relatively small clusters at Florida International University and a
large cluster, MareNostrum Super Computer at the Barcelona Super
Computing Center. The data generated in these experiments reflects
different cpu clock speeds throughout a cluster. The different cpu power
utilization was achieved through the use of the *cpulimit* tool which
effectively limits the percentage of processor speed allocated to an
specified process, in our case, WRF. The data gathered served to compare
against predictions made by Aprof for different combinations of numbers
of nodes with different cpu percentage utilization. The results obtained
let to the validation of our modeling approach concluding to be
successful within clusters and scalable to bigger environments.
Additionally, our proposed process of prediction shows efficiency of
less than 10% error and it shows to be fast and architecture agnostic.

Marlon Bright was supported by NSF-REU grant No. IIS-0552555.

Javier Delgado was supported in part by NSF grants No. IIS-0552555 and
OCI-0636031.\
E. Javier Figueroa was supported in part by NSF-REU No. IIS-0552555 and
in part by IBM.\
S. Masoud Sadjadi was supported in part by IBM and NSF grants No.
OISE-0730065, HRD-0317692, OCI-0636031, and IIS-0552555.