JOHN G. MICHALAKES
Argonne National Laboratory National
Center for Atmospheric Research
Mathematics and Computer
Science Division Mesoscale and
Microscale Division
P.O
Box 3000
Boulder,
Colorado 80307-3000
University of Chicago Phone:
303-497-8199
Fellow, Computational
Institute FAX:
303-497-8181
email: michalak@ucar.edu
http://www.mcs.anl.gov/people/michalakes
EDUCATION:
|
M.S. Computer
Science, Kent State University, 1988. B.A. English,
Cleveland State University, 1984. |
EXPERIENCE:
University of Chicago Computational
Institute 2001
- present
Fellow
National Center for Atmospheric Research 1998 - present
Visitor, Mesoscale and Microscale Meteorology Division
Argonne National Laboratory 1989
- present
Staff
Software Engineer, Mathematics and Computer Science Division
Mr. Michalakes conducts
research in software tools and parallel algorithms for efficiently implementing
atmospheric models on parallel computers. Michalakes has been active in the
U.S. high-performance simulation community for ten years and his work is
internationally recognized. He has given invited seminars and tutorials in
Europe, Asia, and Australia. He helped organized two international conferences
on parallel computing for atmospheric and oceanographic applications and
co-edited a special issue of the journal Parallel
Computing on regional weather modeling. As a long-term visitor to the NCAR
Mesoscale and Microscale Meteorology Division, Michalakes is principal software
architect of the Weather Research and Forecast (WRF) model development effort,
a large multi-institution effort developing the next generation U.S. weather
model and leader of the Software Architecture, Standards and Implementation
Working group within the WRF collaboration. He is also working with Air Force
Research Laboratory on development of a massively parallel implementation of
the MM5 four-dimensional variational data assimilation (4DVAR) system for
assimilation of satellite radiances, and with Pacific Northwest National
Laboratory on a massively parallel implementation of the PNNL regional climate
model. He helped develop the distributed-memory parallel implementation of two
large community models: the Penn State/NCAR Mesoscale Model (MM5) (more than
500 users world-wide) and the message passing version of the NCAR Community
Climate Model (CCM2). His research goals include development of same-source
enablement tools and techniques that will allow development and maintenance of
a single source code that will run efficiently on diverse high-performance
computing platforms. Mr. Michalakes
developed RSL, a parallel library for finite difference weather models using
nested grids, and FLIC, a Fortran source translation tool for same-source
parallelization.
HONORS:
Summa Cum Laude, Cleveland State University
Best Paper Award (shared), Supercomputing95
SELECTED PUBLICATIONS:
Michalakes,
J.: The same-source parallel MM5. Journal of Scientific Programming, 8 (2000),
5-12.
Michalakes,
J.: RSL: A Parallel Runtime System Library for Regional Atmospheric Models with
Nesting, in Structured Adaptive Mesh
Refinement (SAMR) Grid Methods, IMA Volumes in Mathematics and its
Applications (117), Springer, New York, 2000, pp. 59-74.
Michalakes,
J., J. Dudhia, D. Gill, J. Klemp and W. Skamarock: Design of a next-generation
regional weather research and forecast model : Towards Teracomputing, World
Scientific, River Edge, New Jersey, 1998, pp. 117-124.
Michalakes,
J.: FLIC: A Translator for Same-source Parallel Implementation of Regular Grid
Applications, Tech. Rep. ANL/MCS-TM-223, Argonne National Laboratory, 1997.
Michalakes,
J., C. Baillie, and R. Skålin, 1997: Regional weather modeling on parallel
computers. Parallel Computing, 23,
2135-2142.
Michalakes, J.,1997: MM90: A scalable Parallel Implementation of
the Penn State/ NCAR Mesoscale Model (MM5).
Parallel Computing, 23, 2173-2186.
Foster, I.
and J. Michalakes, Parallel Supercomputing in Atmospheric Science, World
Scientific, River Edge, New Jersey (1993),
pp. 354—363.
Drake, J.,
I. Foster, J. Michalakes, B. Toonen, and P. Worley, 1995: Design and
performance of a scalable parallel community climate model. Parallel
Computing, 21, 1571-1592.