Keywords
 A2.1.1. Semantics of programming languages
 A2.2.1. Static analysis
 A2.5. Software engineering
 A6.1.1. Continuous Modeling (PDE, ODE)
 A6.2.6. Optimization
 A6.2.7. High performance computing
 A6.3.1. Inverse problems
 A6.3.2. Data assimilation
 B1.1.2. Molecular and cellular biology
 B3.2. Climate and meteorology
 B3.3.2. Water: sea & ocean, lake & river
 B3.3.4. Atmosphere
 B5.2.3. Aviation
 B5.2.4. Aerospace
 B9.6.3. Economy, Finance
1 Team members, visitors, external collaborators
Research Scientists
 Laurent Hascoët [Team leader, Inria, Senior Researcher, HDR]
 Alain Dervieux [Inria, Emeritus, HDR]
 Valérie Pascual [Inria, Researcher, until Jul 2021]
PhD Students
 Matthieu Gschwend [Inria, until Jul 2021]
 Bastien Sauvage [Inria, from Oct 2021]
Administrative Assistant
 Christine Claux [Inria]
External Collaborator
 Bruno Koobus [Univ de Montpellier]
2 Overall objectives
Team Ecuador studies Algorithmic Differentiation (AD) of computer programs, blending :
 AD theory: We study software engineering techniques, to analyze and transform programs mechanically. Algorithmic Differentiation (AD) transforms a program P that computes a function $F$, into a program P' that computes analytical derivatives of $F$. We put emphasis on the adjoint mode of AD, a sophisticated transformation that yields gradients for optimization at a remarkably low cost.
 AD application to Scientific Computing: We adapt the strategies of Scientific Computing to take full advantage of AD. We validate our work on realsize applications.
We aim to produce AD code that can compete with handwritten sensitivity and adjoint programs used in the industry. We implement our algorithms into the tool Tapenade, one of the most popular AD tools at present.
Our research directions :
 Efficient adjoint AD of frequent dialects e.g. FixedPoint loops.
 Development of the adjoint AD model towards Dynamic Memory Management.
 Evolution of the adjoint AD model to keep in pace with with modern programming languages constructs.
 Optimal shape design and optimal control for steady and unsteady simulations. Higherorder derivatives for uncertainty quantification.
 Adjointdriven mesh adaptation.
3 Research program
3.1 Algorithmic Differentiation
Participants: Laurent Hascoët, Valérie Pascual.
 algorithmic differentiation (AD, aka Automatic Differentiation) Transformation of a program, that returns a new program that computes derivatives of the initial program, i.e. some combination of the partial derivatives of the program's outputs with respect to its inputs.
 adjoint Mathematical manipulation of the Partial Differential Equations that define a problem, obtaining new differential equations that define the gradient of the original problem's solution.
 checkpointing General tradeoff technique, used in adjoint AD, that trades duplicate execution of a part of the program to save some memory space that was used to save intermediate results.
Algorithmic Differentiation (AD) differentiates programs. The input of AD is a source program $P$ that, given some $X\in {\mathbb{R}}^{n}$, returns some $Y=F\left(X\right)\phantom{\rule{0.222222em}{0ex}}\in {\mathbb{R}}^{m}$, for a differentiable $F$. AD generates a new source program ${P}^{\text{'}}$ that, given $X$, computes some derivatives of $F$ 4.
Any execution of $P$ amounts to a sequence of instructions, which is identified with a composition of vector functions. Thus, if
where each ${f}_{k}$ is the elementary function implemented by instruction ${I}_{k}$. AD applies the chain rule to obtain derivatives of $F$. Calling ${X}_{k}$ the values of all variables after instruction ${I}_{k}$, i.e. ${X}_{0}=X$ and ${X}_{k}={f}_{k}\left({X}_{k1}\right)$, the Jacobian of $F$ is
which can be mechanically written as a sequence of instructions ${I}_{k}^{\text{'}}$. This can be generalized to higher level derivatives, Taylor series, etc. Combining the ${I}_{k}^{\text{'}}$ with the control of $P$ yields ${P}^{\text{'}}$, and therefore this differentiation is piecewise.
The above computation of ${F}^{\text{'}}\left(X\right)$, albeit simple and mechanical, can be prohibitively expensive on large codes. In practice, many applications only need cheaper projections of ${F}^{\text{'}}\left(X\right)$ such as:

Sensitivities, defined for a given direction $\dot{X}$ in the input space as:
$${F}^{\text{'}}\left(X\right).\dot{X}={f}_{p}^{\text{'}}\left({X}_{p1}\right)\phantom{\rule{0.222222em}{0ex}}.\phantom{\rule{0.222222em}{0ex}}{f}_{p1}^{\text{'}}\left({X}_{p2}\right)\phantom{\rule{0.222222em}{0ex}}.\phantom{\rule{0.222222em}{0ex}}\cdots \phantom{\rule{0.222222em}{0ex}}.\phantom{\rule{0.222222em}{0ex}}{f}_{1}^{\text{'}}\left({X}_{0}\right)\phantom{\rule{0.222222em}{0ex}}.\phantom{\rule{0.222222em}{0ex}}\dot{X}\phantom{\rule{1.em}{0ex}}.$$ 3This expression is easily computed from right to left, interleaved with the original program instructions. This is the tangent mode of AD.

Adjoints, defined after transposition (${F}^{\text{'}*}$), for a given weighting $\overline{Y}$ of the outputs as:
$${F}^{\text{'}*}\left(X\right).\overline{Y}={f}_{1}^{\text{'}*}\left({X}_{0}\right).{f}_{2}^{\text{'}*}\left({X}_{1}\right).\phantom{\rule{0.222222em}{0ex}}\cdots \phantom{\rule{0.222222em}{0ex}}.{f}_{p1}^{\text{'}*}\left({X}_{p2}\right).{f}_{p}^{\text{'}*}\left({X}_{p1}\right).\overline{Y}\phantom{\rule{1.em}{0ex}}.$$ 4This expression is most efficiently computed from right to left, because matrix$\times $vector products are cheaper than matrix$\times $matrix products. This is the adjoint mode of AD, most effective for optimization, data assimilation 28, adjoint problems 22, or inverse problems.
Adjoint AD builds a very efficient program 24, which computes the gradient in a time independent from the number of parameters $n$. In contrast, computing the same gradient with the tangent mode would require running the tangent differentiated program $n$ times.
However, the ${X}_{k}$ are required in the inverse of their computation order. If the original program overwrites a part of ${X}_{k}$, the differentiated program must restore ${X}_{k}$ before it is used by ${f}_{k+1}^{\text{'}*}\left({X}_{k}\right)$. Therefore, the central research problem of adjoint AD is to make the ${X}_{k}$ available in reverse order at the cheapest cost, using strategies that combine storage, repeated forward computation from available previous values, or even inverted computation from available later values.
Another research issue is to make the AD model cope with the constant evolution of modern language constructs. From the old days of Fortran77, novelties include pointers and dynamic allocation, modularity, structured data types, objects, vectorial notation and parallel programming. We keep developing our models and tools to handle these new constructs.
3.2 Static Analysis and Transformation of programs
Participants: Laurent Hascoët, Valérie Pascual.
 abstract syntax tree Tree representation of a computer program, that keeps only the semantically significant information and abstracts away syntactic sugar such as indentation, parentheses, or separators.
 control flow graph Representation of a procedure body as a directed graph, whose nodes, known as basic blocks, each contain a sequence of instructions and whose arrows represent all possible control jumps that can occur at runtime.
 abstract interpretation Model that describes program static analysis as a special sort of execution, in which all branches of control switches are taken concurrently, and where computed values are replaced by abstract values from a given semantic domain. Each particular analysis gives birth to a specific semantic domain.
 data flow analysis Program analysis that studies how a given property of variables evolves with execution of the program. Data Flow analysis is static, therefore studying all possible runtime behaviors and making conservative approximations. A typical dataflow analysis is to detect, at any location in the source program, whether a variable is initialized or not.
The most obvious example of a program transformation tool is certainly a compiler. Other examples are program translators, that go from one language or formalism to another, or optimizers, that transform a program to make it run better. AD is just one such transformation. These tools share the technological basis that lets them implement the sophisticated analyses 15 required. In particular there are common mathematical models to specify these analyses and analyze their properties.
An important principle is abstraction: the core of a compiler should not bother about syntactic details of the compiled program. The optimization and code generation phases must be independent from the particular input programming language. This is generally achieved using languagespecific frontends, languageindependent middleends, and targetspecific backends. In the middleend, analysis can concentrate on the semantics of a reduced set of constructs. This analysis operates on an abstract representation of programs made of one call graph, whose nodes are themselves flow graphs whose nodes (basic blocks) contain abstract syntax trees for the individual atomic instructions. To each level are attached symbol tables, nested to capture scoping.
Static program analysis can be defined on this internal representation, which is largely language independent. The simplest analyses on trees can be specified with inference rules 18, 25, 16. But many dataflow analyses are more complex, and better defined on graphs than on trees. Since both call graphs and flow graphs may be cyclic, these global analyses will be solved iteratively. Abstract Interpretation 19 is a theoretical framework to study complexity and termination of these analyses.
Data flow analyses must be carefully designed to avoid or control combinatorial explosion. At the call graph level, they can run bottomup or topdown, and they yield more accurate results when they take into account the different call sites of each procedure, which is called context sensitivity. At the flow graph level, they can run forwards or backwards, and yield more accurate results when they take into account only the possible execution flows resulting from possible control, which is called flow sensitivity.
Even then, data flow analyses are limited, because they are static and thus have very little knowledge of actual runtime values. Far before reaching the very theoretical limit of undecidability, one reaches practical limitations to how much information one can infer from programs that use arrays 32, 20 or pointers. Therefore, conservative overapproximations must be made, leading to derivative code less efficient than ideal.
3.3 Algorithmic Differentiation and Scientific Computing
Participants: Alain Dervieux, Laurent Hascoët, Bruno Koobus, Matthieu Gschwend, Stephen Wornom.
 linearization In Scientific Computing, the mathematical model often consists of Partial Differential Equations, that are discretized and then solved by a computer program. Linearization of these equations, or alternatively linearization of the computer program, predict the behavior of the model when small perturbations are applied. This is useful when the perturbations are effectively small, as in acoustics, or when one wants the sensitivity of the system with respect to one parameter, as in optimization.
 adjoint state Consider a system of Partial Differential Equations that define some characteristics of a system with respect to some parameters. Consider one particular scalar characteristic. Its sensitivity (or gradient) with respect to the parameters can be defined by means of adjoint equations, deduced from the original equations through linearization and transposition. The solution of the adjoint equations is known as the adjoint state.
Scientific Computing provides reliable simulations of complex systems. For example it is possible to simulate the steady or unsteady 3D air flow around a plane that captures the physical phenomena of shocks and turbulence. Next comes optimization, one degree higher in complexity because it repeatedly simulates and applies gradientbased optimization steps until an optimum is reached. The next sophistication is robustness, that detects undesirable solutions which, although maybe optimal, are very sensitive to uncertainty on design parameters or on manufacturing tolerances. This makes second derivatives come into play. Similarly Uncertainty Quantification can use second derivatives to evaluate how uncertainty on the simulation inputs imply uncertainty on its outputs.
To obtain this gradient and possibly higher derivatives, we advocate adjoint AD (cf3.1) of the program that discretizes and solves the direct system. This gives the exact gradient of the discrete function computed by the program, which is quicker and more sound than differentiating the original mathematical equations 22. Theoretical results 21 guarantee convergence of these derivatives when the direct program converges. This approach is highly mechanizable. However, it requires careful study and special developments of the AD model 26, 30 to master possibly heavy memory usage. Among these additional developments, we promote in particular specialized AD models for FixedPoint iterations 23, 17, efficient adjoints for linear algebra operators such as solvers, or exploitation of parallel properties of the adjoint code.
4 Application domains
4.1 Algorithmic Differentiation
Algorithmic Differentiation of programs gives sensitivities or gradients, useful for instance for :
 optimum shape design under constraints, multidisciplinary optimization, and more generally any algorithm based on local linearization,
 inverse problems, such as parameter estimation and in particular 4Dvar data assimilation in climate sciences (meteorology, oceanography),
 firstorder linearization of complex systems, or higherorder simulations, yielding reduced models for simulation of complex systems around a given state,
 adaptation of parameters for classification tools such as Machine Learning systems, in which Adjoint Differentiation is also known as backpropagation.
 mesh adaptation and mesh optimization with gradients or adjoints,
 equation solving with the Newton method,
 sensitivity analysis, propagation of truncation errors.
4.2 Multidisciplinary optimization
A CFD program computes the flow around a shape, starting from a number of inputs that define the shape and other parameters. On this flow one can define optimization criteria e.g. the lift of an aircraft. To optimize a criterion by a gradient descent, one needs the gradient of the criterion with respect to all inputs, and possibly additional gradients when there are constraints. Adjoint AD is the most efficient way to compute these gradients.
4.3 Inverse problems and Data Assimilation
Inverse problems aim at estimating the value of hidden parameters from other measurable values, that depend on the hidden parameters through a system of equations. For example, the hidden parameter might be the shape of the ocean floor, and the measurable values of the altitude and velocities of the surface. Figure 1 shows an example of an inverse problem using the glaciology code ALIF (a pure C version of ISSM 27) and its ADadjoint produced by Tapenade.
One particular case of inverse problems is data assimilation 28 in weather forecasting or in oceanography. The quality of the initial state of the simulation conditions the quality of the prediction. But this initial state is not well known. Only some measurements at arbitrary places and times are available. A good initial state is found by solving a least squares problem between the measurements and a guessed initial state which itself must verify the equations of meteorology. This boils down to solving an adjoint problem, which can be done though AD 31. The special case of 4Dvar data assimilation is particularly challenging. The 4th dimension in “4D” is time, as available measurements are distributed over a given assimilation period. Therefore the least squares mechanism must be applied to a simulation over time that follows the time evolution model. This process gives a much better estimation of the initial state, because both position and time of measurements are taken into account. On the other hand, the adjoint problem involved is more complex, because it must run (backwards) over many time steps. This demanding application of AD justifies our efforts in reducing the runtime and memory costs of AD adjoint codes.
4.4 Linearization
Simulating a complex system often requires solving a system of Partial Differential Equations. This can be too expensive, in particular for realtime simulations. When one wants to simulate the reaction of this complex system to small perturbations around a fixed set of parameters, there is an efficient approximation: just suppose that the system is linear in a small neighborhood of the current set of parameters. The reaction of the system is thus approximated by a simple product of the variation of the parameters with the Jacobian matrix of the system. This Jacobian matrix can be obtained by AD. This is especially cheap when the Jacobian matrix is sparse. The simulation can be improved further by introducing higherorder derivatives, such as Taylor expansions, which can also be computed through AD. The result is often called a reduced model.
4.5 Mesh adaptation
Some approximation errors can be expressed by an adjoint state. Mesh adaptation can benefit from this. The classical optimization step can give an optimization direction not only for the control parameters, but also for the approximation parameters, and in particular the mesh geometry. The ultimate goal is to obtain optimal control parameters up to a precision prescribed in advance.
5 Social and environmental responsibility
5.1 Impact of research results
Our research has an impact on environmental research: in Earth sciences, our gradients are used in inverse problems, to determine key properties in oceanography, glaciology, or climate models. For instance they determine basal friction coefficients of glaciers that are necessary to simulate their future evolution. Another example is to locate sources and sinks of CO2 by coupling atmospheric models and remote measurements.
6 New software and platforms
Here we describe new or upgraded software.
6.1 New software
6.1.1 AIRONUM

Keywords:
Computational Fluid Dynamics, Turbulence

Functional Description:
Aironum is an experimental software that solves the unsteady compressible NavierStokes equations with kepsilon, LESVMS and hybrid turbulence modelling on parallel platforms, using MPI. The mesh model is unstructured tetrahedrization, with possible mesh motion.
 URL:

Contact:
Alain Dervieux

Participant:
Alain Dervieux
6.1.2 TAPENADE

Name:
Tapenade Automatic Differentiation Engine

Keywords:
Static analysis, Optimization, Compilation, Gradients

Scientific Description:
Tapenade implements the results of our research about models and static analyses for AD. Tapenade can be downloaded and installed on most architectures. Alternatively, it can be used as a web server. Higherorder derivatives can be obtained through repeated application.
Tapenade performs sophisticated dataflow analysis, flowsensitive and contextsensitive, on the complete source program to produce an efficient differentiated code. Analyses include TypeChecking, ReadWrite analysis, and Pointer analysis. ADspecific analyses include the socalled Activity analysis, Adjoint Liveness analysis, and TBR analysis.

Functional Description:
Tapenade is an Algorithmic Differentiation tool that transforms an original program into a new program that computes derivatives of the original program. Algorithmic Differentiation produces analytical derivatives, that are exact up to machine precision. Adjointmode AD can compute gradients at a cost which is independent from the number of input variables. Tapenade accepts source programs written in Fortran77, Fortran90, or C. It provides differentiation in the following modes: tangent, vector tangent, adjoint, and vector adjoint.

News of the Year:
Extension to CUDA (in C): at this stage only for tangent AD. Various extensions requested by endusers: C++ "&" reference notation, Fortran2003 object constructs. Extension to OpenMP: support for experimental proof of absence of incrementincrement conflicts. Continued refactoring and bug fixes. Draft developer's documentation.
 URL:

Contact:
Laurent Hascoët

Participants:
Laurent Hascoët, Valerie Pascual
7 New results
7.1 Algorithmic Differentiation of OpenMP
Participants: Laurent Hascoët [Argonne National Lab.], Jan Hueckelheim [Argonne National Lab.].
For applications that are parallelized for multicore CPUs or GPUs using OpenMP, it is desirable to also compute the gradients in parallel. We extended the AD model of Tapenade (source transformation, association by address, storage on tape of intermediate values) towards correct and efficient differentiation of OpenMP parallel worksharing loops, one of the most commonly used OpenMP features, in tangentlinear and adjoint mode. This work was published in ACM TOMS 12.
The major issue raised by the adjoint mode is the transformation of variable reads into adjoint variable overwrites, more accurately into increments. While there is no parallel conflict between two reads, two concurrent increments can cause a data race, unless they are both atomic. Classical automated detection of independence is as always limited. We propose to gather information about the memory access patterns of the original code, which is assumed correct and therefore free of data races, and to reuse this information into a theorem prover with which we check the safety of the shared memory accesses of the adjoint code.
A poster was accepted for presentation at PPoPP'22. An article is in preparation.
7.2 Algorithmic Differentiation of CUDA
Participants: Laurent Hascoët [ONERA], Sébastien Bourasseau [ONERA], Cedric Content [ONERA], Bruno Maugars [ONERA].
In collaboration with ONERA, we study extension of Tapenade to the parallel constructs of CUDA. The industrial objective is to include Adjoint AD natively into the successor of ONERA's “Elsa” CFD platform. Our research objective is to extend our adjoint AD model to CUDA code. While this extension bears similarity with our work on OpenMP, several specific aspects of CUDA deserve a specific treatment. For instance the stack storage mechanism central to our adjoint AD model must be redesigned to take into accout the memory limitations of GPU code sections.
This year, we delivered to ONERA a version of Tapenade that can parse and regenerate CUDA C source, and that can differentiate in tangent mode a few of the test cases provided by ONERA.
A joint article is in preparation about the general architecture of ONERA's new CFD solver, that includes a section on integrating AD into the compilation chain and on the needed adaptions of Tapenade.
7.3 Adjoint Differentiation and Garbage Collection
Participants: Laurent Hascoët.
DataFlow reversal is at the heart of our model of SourceTransformation Adjoint Algorithmic Differentiation (Adjoint STAD). The presence of addresses, pointers, and pointer arithmetics in the target language poses challenges to DataFlow reversal, which we can deal with in languages such as C. However, when the target language uses Garbage Collection (GC), for instance Java or Python, the notion of address that is used by DataFlow reversal disappears. Moreover, GC is asynchronous and does not appear explicitly in the source. We studied an extension to the model of Adjoint STAD suitable for a language with GC. We validated this approach on a Java implementation of a simple NavierStokes solver. We compared performance with alternative models of Adjoint AD, such as Overloadingbased AD (e.g. ADOLC) which by design could handle GC more easily.
An article has been written and is currently under review with ACM TOMS. A research report has been published 14.
7.4 Application to large industrial codes
Participants: Laurent Hascoët [U. of Texas, Austin, USA], Shreyas Gaikwad [U. of Texas, Austin, USA], Sri Hari Krishna Narayanan [Argonne National Lab. (Illinois, USA)], Hervé Guillard [CASTOR, INRIA SophiaAntipolis].
We support users with their first experiments of Algorithmic Differentiation of large codes. This concerned two codes this year.
One application was done by Shreyas Gaikwad, University of Texas at Austin, PhD student supervised by Patrick Heimbach. His goal is to produce the adjoint of glaciology codes such as SICOPOLIS and the glaciology component of the MIT GCM. Both are Fortran 90 codes that have been previously differentiated with OpenAD, the former AD tool developed by Argonne National Lab. Krishna Narayanan provided crucial help and expertise, as he had been in charge of the previous differentiation with OpenAD. Indeed, differentiation with Tapenade did not encounter bugs in the tool itself. It rather underlined interface and documentation difficulties to apply special recommended strategies e.g. for adjoint AD of linear solvers.
The other code this year is CTFEM, an element of the code suite developed by the CASTOR team of INRIA and University of Nice for the ITER project. CTFEM is a plasma simulation code written in Fortran90. Together with Hervé Guillard, we applied Tapenade to produce the adjoint code of CTFEM. In addition to several Tapenade bugs, now fixed, we encountered two more interesting issues. One is that CTFEM introduces memory aliasing at a few locations. Classically, memory aliasing is adverse to adjoint AD and should be avoided. In general, the AD tool emits a warning message when potential aliasing is detected. It unfortunately failed to do so in a few particular cases. The other issue is about array notation: Fortran90 actual parameters of calls that are arrays are in principle passed by reference, allowing the called procedure to modify the actual parameter. This is also true for array sections, i.e. actual parameters of the form T(0:10:2). On the other hand, an array section that uses an indirection such as T(ind(0:10:2)) appears to be passed by value, although we found no literature on the subject. Tapenade now points out this adverse situation, which can be solved by a simple local code rewrite.
7.5 Aeroacoustics
Participants: Alain Dervieux [IMAG, U. of Montpellier], Matthieu Gschwend [IMAG, U. of Montpellier], Bastien Sauvage [IMAG, U. of Montpellier], Bruno Koobus [IMAG, U. of Montpellier], Florian Miralles [IMAG, U. of Montpellier], Stephen Wornom [IMAG, U. of Montpellier], Tanya Kozubskaya [CAALAB, Moscow].
The progress in highly accurate schemes for compressible flows on unstructured meshes (together with advances in massive parallelization of these schemes) allows to solve problems previously out of reach. The fouryears programme Norma, associating:
 IMAG of Montpellier University (B. Koobus, coordinator),
 Computational AeroAcoustics Laboratory (CAALAB) of Keldysh Institute of Moscow (T. Kozubskaya, head), and
 Ecuador of INRIA SophiaAntipolis
is supported by the French ANR and by the Russian Science Foundation. Norma is a cooperation on the subject of the extension of Computational AeroAcoustics methods to the simulation the noise emission by rotating machines (helicopters, future aerial taxis, unmanned aerial vehicles, wind turbines...). The tasks of INRIA in this RussianFrench cooperation program are:
 the advancement of two numerical techniques, namely:
 a higherorder approximation scheme for compressible NavierStokes equations, and
 mesh adaptation methods for the flows under study (DES modeled flows able to model noise generation).
 a contribution to the Norma test cases, in particular the quadrotor drone.
Among this year's results:
 We delivered a review on HighOrder methods for compressible CFD.
 We defined CENO3D, a 3D finitevolume scheme relying on cellbased reconstruction and fourthorder accurate, and we developed and tested it in a CFD software.
 We installed an anisotropic metricbased mesh adaptation for rotating machines and we validated it on a first test case, a mixing device rotating in a cylinder. The study of the next test case, the CaradonnaTung helix (Norma test case 1.1.) is under progress.
 We started the design of a new mesh adaptation based on HP principle (simultaneous adaptation of the mesh and of the scheme accuracy), in order to combine the higherorder scheme CENO3D with our anisotropic metricbased mesh adaptation technology.
7.6 Turbulence models
Participants: Alain Dervieux [IMAG, U. of Montpellier], Bruno Koobus [IMAG, U. of Montpellier], Florian Miralles [IMAG, U. of Montpellier], Stephen Wornom [IMAG, U. of Montpellier], Tanya Kozubskaya [CAALAB, Moscow].
Modeling turbulence is an essential aspect of CFD. The purpose of our work in hybrid RANS/LES (Reynolds Averaged NavierStokes / Large Eddy Simulation) is to develop new approaches for industrial applications of LESbased analyses. In the applications targeted (aeronautics, hydraulics), the Reynolds number can be as high as several tens of millions, far too high for pure LES models. However, certain regions in the flow can be predicted better with LES than with usual statistical RANS (Reynolds averaged NavierStokes) models. These are mainly vortical separated regions as assumed in one of the most popular hybrid models, the hybrid Detached Eddy Simulation (DES) model. Here, “hybrid” means that a blending is applied between LES and RANS. An important difference between a real life flow and a wind tunnel or basin is that the turbulence of the flow upstream of each body is not well known.
The development of hybrid models, in particular DES in the litterature, has raised the question of the domain of validity of these models. According to theory, these models should not be applied to flow involving laminar boundary layers (BL). But industrial flows are complex flows and often present regions of laminar BL, regions of fully developed turbulent BL and regions of nonequilibrium vortical BL. It is then mandatory for industrial use that the new hybrid models give a reasonable prediction for all these types of flow. We concentrated on evaluating the behavior of hybrid models for laminar BL and for vortical wakes. While less predictive than pure LES on laminar BL, some hybrid models still give reasonable predictions for rather low Reynolds numbers.
During the first phase of Norma, Montpellier and Moscow are computing a series of initial test cases in order to control the consistancy of the results produced by the two platforms of CFD, namely Noisette for Moscow, and Aironum for Montpellier.
A communication in seminar was presented by Florian Miralles 29
7.7 Rotating machines
Participants: Alain Dervieux [Lemma, SophiaAntipolis], Didier Chargy [Lemma, SophiaAntipolis], Bastien Sauvage [IMAG, u. of Montpellier], Bruno Koobus [IMAG, u. of Montpellier], Florian Miralles [IMAG, u. of Montpellier], Tanya Kozubskaya [CAALAB, Moscow].
The physical problem addressed by Norma involves a computational domain made of at least two components having different rotative motions. The numerical problem of their combination gave birth to many specialized schemes, such as the socalled sliding method, chimera method, immersed boundary method (IBM). In concertation with Moscow, Montpellier is introducing a novel IBM in the CFD code Aironum. The Ecuador team is studying in cooperation with Lemma engineering (Sophia Antipolis) a novel sliding/chimera method.
7.8 High order approximations
Participants: Alain Dervieux, Matthieu Gschwend, Bastien Sauvage.
After many decades of research which we suumarized in 11, approximation of unstructured meshes have become the common practice in CFD. High order approximations for compressible flows on unstructured meshes are facing many constraints that increase their complexity i.e. their computational cost. This is clear for the largest class of approximation, the class of $k$exact schemes, which rely on a local polynomial representation of degree $k$. We are investigating schemes which would solve as efficiently as possible the dilemma of choosing between an approximation with a representation inside macroelements which finally constrains the mesh, and a representation around each individual cell, as in vertex formulations. For this purpose, we extend the Central Essentially Non Oscillating (CENO) family of schemes. We have developed a fourthorder accurate threedimensional CENO. This work is documented in a research report 13.
7.9 Control of approximation errors
Participants: Alain Dervieux [Gamma3 team, INRIASaclay], Bastien Sauvage [Gamma3 team, INRIASaclay], Adrien Loseille [Gamma3 team, INRIASaclay], Frédéric Alauzet [Gamma3 team, INRIASaclay].
Reducing approximation errors as much as possible by changing the mesh is a particular kind of optimal control problem. We formulate it exactly this way when we look for the optimal metric of the mesh, which minimizes a userspecified functional (goaloriented mesh adaptation). In that case, the usual methods of optimal control apply, using adjoint states that can be produced by Algorithmic Differentiation.
This year, we extended mesh adaptation methods to the simulation of rotating machines is a special unsteady periodic flow. We also continued our new analysis for hp anisotropic mesh adaptation.
8 Bilateral contracts and grants with industry
8.1 Bilateral contracts with industry
Participants: Laurent Hascoët [ONERA], Sébastien Bourasseau [ONERA], Cedric Content [ONERA], Bruno Maugars [ONERA].
The team has a contract with ONERA to assist in the Algorithmic Differentiation of ONERA's new CFD platform “SoNICS”. The main objective is adjoint AD of the CUDA parts of the SoNICS source. This contract will finance the work of a development engineer for two years. The contract also includes support to the ONERA development team on advanced use of AD, e.g., fixedpoint adjoints and binomial checkpointing.
9 Partnerships and cooperations
9.1 International initiatives
9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program
Participants: Laurent Hascoët [Argonne National Lab.], Paul Hovland [Argonne National Lab.], Jan Hueckelheim [Argonne National Lab.], Sri Hari Krishna Narayanan [Argonne National Lab.].
Ecuador participates in the Joint Laboratory for Exascale Computing (JLESC) together with colleagues at Argonne National Laboratory.
We gave a short presentation at the last JLESC meeting, about research on Adjoint AD of OpenMP. This is a joint work with Jan Hueckelheim, Argonne National Lab.
10 Dissemination
Participants: Laurent Hascoët.
10.1 Promoting scientific activities
10.1.1 Scientific events: organisation
Together with the MCS division of Argonne National Lab, the Ecuador team coorganized a 2days tutorial “Automatic Differentiation as a Tool for Computational Science” at the SIAM CSE conference, on March 15. Laurent Hascoët gave three presentations there.
Laurent Hascoët is on the organizing commitee of the EuroAD Workshops on Algorithmic Differentiation (www.autodiff.org), taking place twice a year, with exceptions. Organization rotates between RWTH Aachen, University of Jena, Humboldt University Berlin, INRIA SophiaAntipolis, and Argonne National Lab. The 24th EuroAD workshop was organized by the Ecuador team, remotely, on December 1416.
10.1.2 Scientific events: selection
Laurent Hascoët was on the program committee of the “Differentiable Programming Workshop” at NeurIPS2021, December 614.
11 Scientific production
11.1 Major publications
 1 articleReverse automatic differentiation for optimum design: from adjoint state assembly to gradient computation.Optimization Methods and Software1852003, 615627
 2 inproceedingsThe DataFlow Equations of Checkpointing in reverse Automatic Differentiation.International Conference on Computational Science, ICCS 2006, Reading, UK2006
 3 articleAn optimized treatment for algorithmic differentiation of an important glaciological fixedpoint problem.Geoscientific Model Development952016, 27
 4 incollectionAdjoints by Automatic Differentiation.Advanced data assimilation for geosciencesOxford University Press2014, URL: https://hal.inria.fr/hal01109881
 5 article``To Be Recorded'' Analysis in ReverseMode Automatic Differentiation.Future Generation Computer Systems2182004
 6 articleThe Tapenade Automatic Differentiation tool: Principles, Model, and Specification.ACM Transactions On Mathematical Software3932013, URL: http://dx.doi.org/10.1145/2450153.2450158
 7 articleCheaper Adjoints by Reversing Address Computations.Scientific Programming1612008, 8192
 8 articleProgramming language features, usage patterns, and the efficiency of generated adjoint code.Optimization Methods and Software312016, 885903
 9 articleA Framework for Adjointbased Shape Design and Error Control.Computational Fluid Dynamics Journal1642008, 454464
 10 articleAlgorithmic differentiation of code with multiple contextspecific activities.ACM Transactions on Mathematical Software2016
11.2 Publications of the year
International journals
 11 articleTo be structured, or unstructured, fifty years of slings and arrows.Comptes Rendus de l'Académie des sciences Série 2  Mécaniquephysique, Chimie, Sciences de l'univers, Sciences de la Terre2022
 12 articleSourcetoSource Automatic Differentiation of OpenMP Parallel Loops.ACM Transactions on Mathematical Software2021
Reports & preprints
 13 reportA 3D vertex centered CENO scheme for advection.RR9415Inria Sophia Antipolis  MéditerranéeJuly 2021, 37
 14 reportDataFlow reversal and Garbage Collection.RR9416Inria Sophia Antipolis  MéditerranéeJuly 2021, 18
11.3 Cited publications
 15 bookCompilers: Principles, Techniques and Tools.AddisonWesley1986
 16 techreportA language and an integrated environment for program transformations.3313INRIA1997, URL: http://hal.inria.fr/inria00073376
 17 articleReverse accumulation and implicit functions.Optimization Methods and Software941998, 307322
 18 articleNatural semantics on the computer.Proceedings, FranceJapan AI and CS Symposium, ICOTAlso, Information Processing Society of Japan, Technical Memorandum PL866. Also INRIA research report # 4161986, 4989URL: http://hal.inria.fr/inria00076140
 19 articleAbstract Interpretation.ACM Computing Surveys2811996, 324328
 20 articleInterprocedural Array Region Analyses.International Journal of Parallel Programming2461996, 513546
 21 articleAutomatic differentiation and iterative processes.Optimization Methods and Software11992, 1321
 22 inproceedingsAdjoint methods for aeronautical design.Proceedings of the ECCOMAS CFD ConferenceSwansea, U.K.2001
 23 articleReduced Gradients and Hessians from Fixed Point Iteration for State Equations.Numerical Algorithms30(2)2002, 113139
 24 bookEvaluating Derivatives: Principles and Techniques of Algorithmic Differentiation.SIAM, Other Titles in Applied Mathematics2008
 25 phdthesisTransformations automatiques de spécifications sémantiques: application: Un vérificateur de types incremental.Université de Nice SophiaAntipolis1987
 26 techreportAutomatic Differentiation of NavierStokes computations.MCSP6870997Argonne National Laboratory1997
 27 articleInferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model).Cryosphere862014, 23352351URL: http://www.thecryosphere.net/8/2335/2014/
 28 articleVariational algorithms for analysis and assimilation of meteorological observations: theoretical aspects.Tellus38A1986, 97110
 29 inproceedingsHybrid 3D simulations on circular cylinder at Re=1M. Comparison between DDES, RANS/DVMS, DDES/DVMS.33rd Nordic Seminar on Computational Mechanics, Jönköping, 2526 november2021
 30 articlePractical application to fluid flows of automatic differentiation for design problems.Von Karman Lecture Series1997
 31 phdthesisDifférentiation Automatique: application à un problème d'optimisation en météorologie.université de Nice SophiaAntipolis1993
 32 inproceedingsSymbolic Bounds Analysis of Pointers, Array Indices, and Accessed Memory Regions.Proceedings of the ACM SIGPLAN'00 Conference on Programming Language Design and ImplementationACM2000