www.ecco-group.org/abstracts/woce_heimbach.txt

Poster Title:  An efficient exact adjoint of the parallel MIT general 
circulation model, generated via automatic differentiation

Authors: Patrick Heimbach(1), Chris Hill(1), Ralf Giering(2)
Institutions of authors (in author order): (1): Department of Earth, 
Atmospheric and Planetary Sciences, Massachusetts Institute of 
Technology, Cambridge, MA 02139, USA
(2): FastOpt, Martinistr. 21, 20251 Hamburg, Germany

Abstract: We describe computational aspects of automatic differentiation
applied to global ocean circulation modeling and state estimation.
The task of minimizing a cost function measuring the ocean simulation
vs. observation misfit is achieved through efficient calculation of
the cost gradient w.r.t. a set of controls via the adjoint technique.
The adjoint code of the parallel MIT general circulation
model is generated using TAMC or its successor TAF.
The adjoint can be generated for a variety of configurations, including
different mixing schemes such as KPP and GM, time-varying surface flux
or atmospheric state controls, and open boundary controls.
To achieve a tractable problem in both CPU and memory  requirements, in
the light of control flow reversal, the adjoint code relies heavily on
the balancing of storing vs. recomputation via the checkpointing method.
Further savings are achieved by exploiting self-adjointedness of part
of the computation. To retain scalability of domain decomposition
based parallelism, hand-written adjoint routines are provided.
These complement routines of the parallel support package
to perform corresponding operations in reverse mode.
A unique feature of the TAF tool which enables to dump the adjoint
state and restart the adjoint integration is exploited to overcome
batch execution limitations on HPC machines for large-scale ocean and
climate simulations.
The size of a typical adjoint application is illustrated for the global
ocean state estimation problem. Results for a sensitivity study and an
estimation problem are given by way of example.

1	heimbach	1.1	Poster Title: An efficient exact adjoint of the parallel MIT general
2			circulation model, generated via automatic differentiation
3
4			Authors: Patrick Heimbach(1), Chris Hill(1), Ralf Giering(2)
5			Institutions of authors (in author order): (1): Department of Earth,
6			Atmospheric and Planetary Sciences, Massachusetts Institute of
7			Technology, Cambridge, MA 02139, USA
8			(2): FastOpt, Martinistr. 21, 20251 Hamburg, Germany
9
10			Abstract: We describe computational aspects of automatic differentiation
11			applied to global ocean circulation modeling and state estimation.
12			The task of minimizing a cost function measuring the ocean simulation
13			vs. observation misfit is achieved through efficient calculation of
14			the cost gradient w.r.t. a set of controls via the adjoint technique.
15			The adjoint code of the parallel MIT general circulation
16			model is generated using TAMC or its successor TAF.
17			The adjoint can be generated for a variety of configurations, including
18			different mixing schemes such as KPP and GM, time-varying surface flux
19			or atmospheric state controls, and open boundary controls.
20			To achieve a tractable problem in both CPU and memory requirements, in
21			the light of control flow reversal, the adjoint code relies heavily on
22			the balancing of storing vs. recomputation via the checkpointing method.
23			Further savings are achieved by exploiting self-adjointedness of part
24			of the computation. To retain scalability of domain decomposition
25			based parallelism, hand-written adjoint routines are provided.
26			These complement routines of the parallel support package
27			to perform corresponding operations in reverse mode.
28			A unique feature of the TAF tool which enables to dump the adjoint
29			state and restart the adjoint integration is exploited to overcome
30			batch execution limitations on HPC machines for large-scale ocean and
31			climate simulations.
32			The size of a typical adjoint application is illustrated for the global
33			ocean state estimation problem. Results for a sensitivity study and an
34			estimation problem are given by way of example.
35