Luiz Angelo Steffenel
email: luiz-angelo.steffenel@univ-nancy2.fr

Maxime Martinasso
email: maxime.martinasso@imag.fr

Denis Trystram
email: denis.trystram@imag.fr

Assessing Contention Effects on MPI_Alltoall Communications

Keywords: Network Contention, MPI, Collective Communications, Performance Modeling

One of the most important collective communication patterns used in scientific applications is the complete exchange, also called All-to-All. Although efficient algorithms have been studied for specific networks, general solutions like those available in well-known MPI distributions (e.g. the MPI_Alltoall operation) are strongly influenced by the congestion of network resources. In this paper we present an integrated approach to model the performance of the All-to-All collective operation, which consists in identifying a contention signature that characterizes a given network environment, using it to augment a contention-free communication model. This approach, assessed by experimental results, allows an accurate prediction of the performance of the All-to-All operation over different network architectures with a small overhead.

Introduction

One of the most important collective communication patterns for scientific applications is the total exchange [START_REF] Christara | An efficient transposition algorithm for distributed memory computers[END_REF] (also called All-to-All), in which each process holds n different data items that should be distributed among the n processes, including itself. An important example of this communication pattern is the All-to-All operation, where all messages have the same size m.

Although efficient All-to-All algorithms have been studied for specific networks structures like meshes, hypercubes, tori and circuit-switched butterflies, general solutions like those found in well-known MPI distributions rely on direct point-to-point communications among the processes. Because all communications are started simultaneously, architecture independent algorithms are strongly influenced by the saturation of network resources and subsequent loss of packets -the network contention.

In this paper we present a new approach to model the performance of the All-to-All collective operation. Our strategy consists in identifying a contention signature that characterizes a given network environment. Using such contention signature, we are able to accurately predict the performance of the All-to-All operation, with an arbitrary number of processes and message sizes. To demonstrate our approach, we present experimental results obtained with different network architectures (Fast Ethernet, Gigabit Ethernet and Myrinet). We believe that this model can be extremely helpful on the development of application performance prediction frameworks such as PEMPIs [START_REF] Midorikawa | PEMPIs: A new metodology for modeling and prediction of MPI programs performance[END_REF], but also in the optimization of grid-aware collective communications (e.g.: LaPIe [START_REF] Barchet-Steffenel | Scheduling heuristics for efficient broadcast operations on grid environments[END_REF] and MagPIe [START_REF] Kielmann | Network performance-aware collective communication for clustered wide area systems[END_REF]).

This paper is organized as follows: Section 2 presents a survey of performance modeling under communication contention. Section 3 presents the network models used in this paper, and in section 4 we formalize the total exchange problem, as well as some performance lower bounds. In Section 5 we propose a strategy to characterize the contention signature of a given network and for instance, to predict the performance of the All-to-All operation. Section 6 validates our model against experimental data obtained on different network architectures (Fast Ethernet, Gigabit Ethernet and Myrinet). In Section 7 we provide a study case for predicting the performance of a grid-aware All-to-All algorithm. Finally, Section 8 presents some conclusions and the future directions of our work.

Related Works

In the All-to-All operation, every process holds m × n data items that should be equally distributed among the n processes, including itself. Because general implementations of the All-to-All collective communication rely on direct pointto-point communications among the processes the network can easily become saturated, and by consequence, degrade the communication performance. Indeed, Chun and Wang [START_REF] Chun | Realistic communication model for parallel computing on cluster[END_REF][6] demonstrated that the overall execution time of intensive exchange collective communications are strongly dominated by the network contention and congestive packet loss, two aspects that are not easy to quantify. As a result, a major challenge on modeling the communication performance of the All-to-All operation is to represent the impact of network contention.

Unfortunately, most communication models like those presented by Christara et al. [START_REF] Christara | An efficient transposition algorithm for distributed memory computers[END_REF] and Pjesivac-Grbovic et al. [START_REF] Pjesivac-Grbovic | Performance analysis of MPI collective operations[END_REF] do not take into account the potential impacts of network contention. Indeed, these works usually represent the Allto-All operation as parallel executions of the personalized one-to-many pattern [START_REF] Johnssonn | Optimum broadcasting and personalized communication in hypercubes[END_REF], as presented by the linear point-to-point model below, where where α is the start-up time (the latency between the processes), 1 β is the bandwidth of the link, m represents the message size in bytes and n corresponds to the number of processes involved in the operation:

T = (n -1) × (α + βm) (1)
The development of contention-aware communication models is relatively recent, as shown by Grove [START_REF] Grove | Performance Modelling of Message-Passing Parallel Programs[END_REF]. For instance, Adve [START_REF] Adve | Analysing the Behavior and Performance of Parallel Programs[END_REF] presented one of the first models to take into account the effects of resource contention. This model considers that the total execution time of parallel programs is the sum of four components, namely:

T = t computation + t communication + t resource-contention + t synchronization (2)
While conceptually simple, this model was non-trivial in practice because of the non-deterministic nature of resource contention, and because of the difficulty to estimate average synchronization delays.

While the non-deterministic behavior of the network contention is a major obstacle to modeling communication performance, some authors suggested a few techniques to adapt the existing models. As consequence, Bruck [START_REF] Bruck | Efficient algorithms for all-to-all communications in multiport message-passing systems[END_REF] suggested the use of a slowdown factor to correct the performance predictions. Similarly, Clement et al. [START_REF] Clement | Network performance modelling for PM clusters[END_REF] introduced a technique that suggested a way to account contention in shared networks such as non-switched Ethernet, consisting in a contention factor γ that extends the linear communication model T:

T = α + β × m × γ ( 3
)
where γ is equal to the number of processes. A restriction on this model is that it assumes that all processes communicate simultaneously, which is only true for a few collective communication patterns. Anyway, in the cases where this assumption holds, they found that this simple contention model enhanced the accuracy of their predictions for essentially zero extra effort.

The use of a contention factor was supported by the work of Labarta et al. [START_REF] Labarta | DiP: A parallel program development environment[END_REF], that intent to approximate the behavior of the network contention by considering that if there are m messages ready to be transmitted, and only b available buses, then the messages are serialized in m b communication waves. Also, König et al. [START_REF] König | Analysis of gossiping algorithms with restricted buffers[END_REF] have shown indeed that some All-to-All algorithms that are optimal with unlimited buffers become less efficient when communications depend on restricted buffers size.

A similar approach was followed by Jeannot et al. [START_REF] Jeannot | Two fast and efficient message scheduling algorithms for data redistribution through a backbone[END_REF], who designed scheduling algorithms for data redistribution through a backbone. In their work, they suppose that at most k communications can be performed at the same time without causing network contention (the value of k depending on the characteristics of the platform). Using the knowledge of the application transfer pattern, they proposed two algorithms to schedule the messages transfer, performing an application-level congestion control that in most cases outperforms the TCP contention control mechanism.

Most recently, some works aimed to design contention-aware performance models. For instance, LoGPC [START_REF] Moritz | LoGPC: Modeling network contention in messagepassing programs[END_REF] presents an extension of the LogP model that tries to determine the impact of network contention through the analysis of kary n-cubes. Unfortunately, the complexity of this analysis makes too hard the application of such model in practical situations.

Another approach to include contention-specific parameters in the performance models was introduced by Chun [START_REF] Chun | Performance Studies of High-Speed Communication on Commodity Cluster[END_REF]. In his work, the contention is considered as a component of the communication latency, and by consequence, the model uses different latency values depending on the message size. Although easier to use than LoGPC, this model does not take into account the number of messages passing in the network nor the link capacity, which are clearly related to the occurrence of network contention.

In this work we assume that the network is fully connected, which corresponds to most current parallel machines with distributed memory.

Communication Model: The links between pairs of processes are bidirectional, and each process can transmit data on at most one link and receive data on at most one link at any given time.

Transmission Model: We use Hockney's notation [START_REF] Hockney | The communication challenge for MPP: Intel paragon and meiko cs-2[END_REF] to describe our transmission model. Therefore, the time to send a message of size w i,j from a process p i to another process p j , is α + w i,j β, where α is the start-up time (the communication latency between the processes) and 1 β is the bandwidth of the link. As in this paper we assume that all links have the same latency and bandwidth, and because we only investigate the regular version of the MPI_Alltoall operation where all messages have the same size m, ∀i, ∀j, w i,j = m, and therefore the time to send a message from a process p i to a process p j is α + mβ.

Synchronization Model: We assume an asynchronous communication model, where transmissions from different processes do not have to start at the same time. However, all processes start the algorithm simultaneously. This synchronization model corresponds to the execution of the MPI_Alltoall operation, used as reference in this work.

Problem Definition

In the total exchange problem, n different processes hold each one n data items that should be evenly distributed among the n processes, including itself. Because each data item has potentially different contents and sizes according to their destinations, all processes engage a total exchange communication pattern. Therefore, a total exchange operation will be complete only after all processes have sent their messages to their counterparts, and received their respective messages.

Formally, the total exchange problem can be described using a weighted digraph dG(V, E) of order n with V = {p 0 , ..., p n-1 }. This digraph is called a message exchange digraph or MED for short. In a MED, the vertices represent the process nodes, and the arcs represent the messages to be transmitted. An integer w(e) is associated with each arc e = (p i , p j ), representing the size of the message to be sent from process p i to process p j . Note that there is not necessarily any relationship between a MED and the topology of the interconnection network.

The port capacity of a process for transmission is the number of other processes to which it can transmit simultaneously. Similarly, the port capacity for reception is the number of other processes from which it can receive simultaneously. We will concentrate on the performance modeling problem with all port capacities restricted to one for both transmitting and receiving. This restriction is well-known in the literature as 1-port full-duplex.

Notation and lower bounds

In this section, we present theoretical bounds on the minimum number of communications and on the bandwidth for the general message exchange problem. The number of communications determines the number of start-ups, and the bandwidth depends on the message weights.

Given a MED dG(V ; E), we denote the in-degree of each vertex p i ∈ V by ∆ r (p i ), and the out-degree by ∆ s (p i ). Let ∆ r = max pi∈V {∆ r (p i )} and ∆ s = max pi∈V {∆ s (p i )}. Therefore, we obtain the following straightforward bound on the number of start-ups.

Claim 1. The number of start-ups needed to solve a message exchange problem on a digraph dG(V ; E) without message forwarding is at least max(∆ s , ∆ r ).

Given a MED dG(V, E), the bandwidth bounds are determined by two obvious bottlenecks for each vertex -the time for it to send its messages and the time for it to receive its messages. Each vertex p i has to send messages with sizes {w i,j | j = 0 . . . n -1}. The time for all vertices to send their messages is at least t s = max i n-1 j=0 w i,j β. Similarly, the time for all vertices to receive their messages is at least t r = max j n-1 i=0 w i,j β. Claim 2. The time to complete a personalized exchange is at least max{t s , t r }. We can combine the claims about the number of start-ups and the bandwidth when message forwarding is not allowed.

Claim 3. If message forwarding is not allowed, and either the model is synchronous or both maxima are due to the same process, the time to complete a personalized exchange is at least max(∆ s , ∆ r ) × α + max{t s , t r }.

Because in this paper we do not assume messages forwarding, the fan-in and fan-out of a process must be (n -1). Further, as we consider messages to be the same size and the network to be homogeneous, we can simplify Claim 3 so that the following bound holds.

Proposition 1. If message forwarding is not allowed, and all messages have size m, and both bandwidth and latency are identical to any connection between two different processes p i and p j , the time to complete a total exchange is at least (n -1) × α + (n -1) × βm.

Proof. The proof is trivial, as the time to complete a total exchange is at least the time a single process needs to send one message to each other process.

Contention Signature Approach

To cope with this problem and to model the contention impact on the performance of the All-to-All operation, we adopt an approach similar to Clement et al. [START_REF] Clement | Network performance modelling for PM clusters[END_REF], which considers the contention sufficiently linear to be modeled. Our approach, however, tries to identify the behavior of the All-to-All operation with regard to the theoretical lower bound (Proposition 1) on the 1-port communication model. In our hypothesis, the network contention depends mostly on the physical characteristics of the network (network cards, links, switches), and consequently, the ratio between the theoretical lower bound and the real performance represents a "contention signature" of the network. Once identified the signature of a network, it can be used in further experiments to predict the communication performance, provided that the network infrastructure does not change.

Initially, we consider communication in a contention-free environment. In this case, a process that sends messages of size m to n -1 processes needs at least (n -1) × α + (n -1) × mβ time units. Further, by the properties of the 1-port communication model, the total communication time of the All-to-All operation must be at least (n -1) × α + (n -1) × mβ time units if all processes start communicating simultaneously, as stated by Proposition 1.

In the case of the All-to-All operation, however, the intensive communication pattern tends to saturate the network, causing message delays and packet loss that strongly impact on the communication performance of this collective communication. In this network congestion situation, traditional models such as those presented by Christara [START_REF] Christara | An efficient transposition algorithm for distributed memory computers[END_REF] do not hold anymore, even if the communication pattern has not changed.

Therefore, our approach to model the performance of the MPI_Alltoall operation despite network contention consists on determining a contention ratio γ that express the relationship between the theoretical performance (lower bound) and the real completion time. For simplicity, we consider that this contention ratio γ is constant and depends exclusively on the network characteristics. Therefore, the simplest way to integrate this contention ratio γ in our performance model would be as follows:

T = (n -1) × (α + mβ × γ) (4)

Non-linear aspects

Although the performance model augmented by use of the contention ratio γ improves the accuracy of the predictions, we observe nonetheless that some network architectures are still subject to performance variations according to the message size. To illustrate this problem, we present in Fig. 1, a detailed mapping of the communication time of the MPI_Alltoall operation in a Gigabit Ethernet network. We observe that the communication time does not increase linearly with the message size, but instead, present a non-linear behavior that prevents our model to accurately predict the performance when dealing with small messages.

To cope with this non-linearity, we propose an extension of the contention ratio model to better represent this phenomenon when messages are sufficiently large. Hence, we augment the model with a new parameter δ, which depends on the number of processes but also on a given message size M . As a consequence, the association of different equations helps to define a more realistic performance model for the MPI_Alltoall operation, as follows:

T = (n -1) × (α + mβ × γ) if m < M (n -1) × (α + mβ × γ + δ) if m ≥ M (5)

Validation

To validate the approach proposed in this paper, this section presents our experiments to model the performance of MPI_Alltoall operation using three network architectures, Fast Ethernet, Gigabit Ethernet and Myrinet. As previously explained, our approach consists on comparing the expected and real performance of the MPI_Alltoall operation using a sample experiment with n ′ nodes; the relationship between these two measures allows us to define the γ and δ parameters that characterize the "network contention signature".

To obtain these parameters, we compare the sample data obtained from both theoretical lower bound and experimental measure, when varying the message size. Indeed, the lower bound comes from Proposition 1, with parameters α and β obtained from a simple point-to-point measure. The parameters γ and δ are obtained through a linear regression with the Generalized Least Squares method, comparing at least four measurement points in order to better fit the performance curve.

The different experiments presented in this paper represent the average of 100 measures for each set of parameters (message size, number of processes), and were conducted over two clusters of the Grid'50001 :

The icluster2 cluster, located at INRIA-Rhone-Alpes, composed of 104 dual Itanium2 nodes at 900 MHz, used for the experiments with the Fast Ethernet network (5 Fast Ethernet switches -20 nodes per switch -interconnected by 1 Gigabit Ethernet switch) and the Myrinet 2000 network (one 128 ports M3-E128 Myrinet switch). All machines run Red Hat Enterprise Linux AS release 3, with kernel version 2.4.21.

The GdX (GriD'eXplorer) cluster, operated by INRIA-Futurs. This cluster includes 216 nodes with dual AMD Opteron processors at 2 GHz running Debian Linux kernel 2.6.8 and a Broadcom Gigabit Ethernet network.

Fast Ethernet

Taking as basis the measured performance for a 24 machines network, we were able to approximate the performance of the Fast Ethernet network with a con-tention ratio γ = 1.0195. Indeed, this relatively small difference must be considered in the light of the retransmission policy: although the communication latency (and therefore the timeouts) is relatively small (around 60 µs), the reduced bandwidth of the links minimizes the impact of the retransmission of a lost packet. More important, we observe that the experimental measure behave like an affine equation, showing a start-up cost usually not considered by the traditional performance model which corresponds to the δ parameter proposed in our model. Therefore, we determined δ = 8.23 ms for messages larger than M = 2 kB, which means that each simultaneous communication induces an overload of 8.23 ms to the completion time of the All-to-All operation. Applying both γ and δ parameters we were able to approximate our predictions from the performance of the MPI_Alltoall operation with an arbitrary number of processes, as demonstrate in Fig. 2a. We observe indeed that our error rate is usually smaller than 10% when there are enough processes to saturate the network, as presented in Fig. 2b.

Gigabit Ethernet

To compute the contention ratio γ and a start-up cost δ, we use sample data for an arbitrary number of processes. Indeed, we chose in this example the results for an execution of the All-to-All operation with 40 processes (one by machine). Using linear regression on these data we obtain γ = 4.3628 and δ = 4.93 ms (to be used only for messages larger than M = 8 kB). As a result, the performance predictions from our model correspond to the curve presented on Fig. 3a. As in the case of the Fast Ethernet network, the error rate is quite small when the network becomes saturate, even when we consider different message sizes (Fig. 3b).

Myrinet

Although the two previous experiments give important proofs on the validity of our modeling method, they share many similarities on both network architecture and transport protocol (TCP/IP). To ensure that our method is not bounded to a specific infrastructure, we chose to validate our performance model also in a Myrinet network, using the gm transport protocol. Because of the Myrinet+gm stack differs considerably from the Ethernet+TCP/IP stack, any systematic behavior introduced into our sampling data by these architectures should be exposed.

Indeed, the Myrinet network differs from Ethernet-based architectures due to an start-up cost almost inexistent (one of the main characteristics of the Myrinet+gm stack). Indeed, we were able to fit the performance of a 24-processes All-to-All operation using only the contention ratio γ = 2, 49754 (as the linear regression pointed a start-up cost δ smaller than 1 microsecond).

Nevertheless, when applying this factor to an arbitrary number of machines, as presented in Fig. 4a, we observe that our predictions do not follow the experimental data as observed before with Fast Ethernet and Gigabit Ethernet. Actually, a close look at the error rate (Fig. 4b) indicates that network saturation occurs only when there are more then 40 communicating processes (evidenced by the constant error rate from that point). These results demonstrate the limitations of our approach: while a contention ratio may provide precise performance predictions, it depends on the data used to define the network signature. By using reference data from a partially saturated network we are subjected to inaccurate approximations (even if they are better than the contention unaware predictions). Actually, most of the complexity of the All-to-All problem in grid environments resides on the need to exchange different messages through different networks (local and distant). The traditional implementation of the MPI_Alltoall operation cannot differentiate these networks, leading to poor performances. However, if we assume that communications between clusters are slower than intra-clusters ones, it might be useful to collect data in the local level before sending it through the backbone, in a single transmission. Indeed, in [START_REF] Jeannot | Fast and efficient total exchange on two clusters[END_REF] we propose a grid-aware solution which performs on two phases. In the first phase only local communications are performed. During this phase the total exchange is performed on local nodes on both cluster and extra buffers are prepared for the second (intercluster) phase. During the second phase data are exchanged between the clusters. Buffers that have been prepared during the first phase are sent directly to the corresponding nodes in order to complete the total exchange.

Direct Exchange Prediction

More precisely, our algorithm works as follow. Without loss of generality, let us assume that cluster C 1 has less nodes than C 2 (n 1 ≤ n 2 ). Nodes are numbered from 0 to n 1 + n 2 -1, with nodes from 0 to n 1 -1 being on C 1 and nodes from n 1 to n 1 + n 2 -1 being on cluster C 2 . We call M i,j the message (data) that has to be send form node i to node j. For instance, the algorithm proceeds in two phases:

First phase During the first phase, we perform the local exchange: Process i sends M i,j to process j, if i and j are on the same cluster. Then it prepares the buffers for the remote communications. On C 1 data that have to be send to node j on C 2 is first stored to node j mod n 1 . Data to be sent from node i on C 2 to node j on C 1 is stored on node ⌊i/n 1 ⌋ × n 1 + j.

Second phase

During the second phase only n 2 inter-cluster communications occurs. This phase is decomposed in ⌈n 2 /n 1 ⌉ steps with at most n 1 communications each. Steps are numbered from 1 to ⌈n 2 /n 1 ⌉ During step s node i of C 1 exchange data stored in its local buffer with node j

= i + n 1 × s on C 2 (if j < n 1 + n 2 ). More precisely i sends M k,j to j where k ∈ [0, n 1 ] and j sends M k,i to i where k ∈ [n 1 × s, n 1 × s + n 1 -1].
As our algorithm minimizes the number of inter-cluster communications between the clusters, we need only 2 × max(n 1 , n 2 ) messages in both directions (against 2 × n 1 × n 2 messages in the traditional algorithm). For instance, the exchange of data between two clusters with the same number of process will proceed in one single communication step of the second phase. Our algorithm is also wide-area optimal since it ensures that a data segment is transferred only once between two clusters separate by a wide-area link.

Performance prediction in a grid environment

As shown above, the algorithm we propose to optimize All-to-All communications in a grid environment rely on the relative performances of both local and remote networks. Indeed, we extend the total exchange among nodes in the same cluster in order to reduce transmissions through the backbone.

This approach has therefore two consequences for performance prediction: First, it prevents contention in the wide-area links, which are hard to model. Second, transmission of messages packed together is easy to be predicted in a wide-area network (large messages are less subjected to network interferences). For instance, we can design a wide-area performance model by composing localarea predictions obtained with our performance model and wide-area predictions that can be easily obtained from traditional methods. Hence, an approximate model for the communication between two clusters would be similar to:

T = max(T C1 , T C2 ) + ⌈n 2 /n 1 ⌉ × (α w + β w × m × n) (6)
Although not in the scope of this work, preliminary experiments indicate that this model holds. We expect to develop this subject in a future work.

Conclusions and Future Works

In this paper we address the problem of modeling the performance of Total Exchange communication operations, usually subject to important variations caused by network contention. Because traditional performance models are unable to predict the real completion time of an All-to-All operation, we try to cope with this problem by identifying the contention signature of a given network. In our approach, two parameters γ and δ are used to augment a linear performance model in order to fit the performance of the MPI_Alltoall operation. Because these parameters characterize the network contention and are independent of the number of communicating processes, they can be used to accurately predict the communication performance when communications tend to saturate the network. Indeed, we demonstrate our approach through experiments conducted on popular network architectures, Fast Ethernet, Gigabit Ethernet and Myrinet.

We intend to pursue this research by validating our model under other network architectures like Infiniband. Indeed, we expect to extend our models to other collective communication operations, which are especially affected by contention when scaling up to a grid level. We are also investigating different strategies to model collective communications in grid environments.

Fig. 1 .

 1 Fig. 1. Non-linearity of communication cost with small messages

Fig. 2 .

 2 Performance prediction on a Fast Ethernet network

Fig. 3 .

 3 Performance prediction on a Gigabit Ethernet network

Fig. 4 .

 4 Performance prediction on a Myrinet network 7 Applications to Grid-Aware Communications

http://www.grid5000.org/

Acknowledgments

We are grateful to the anonymous referees for many comments and helpful suggestions which helped us improve the focus of the paper.


I C Leão

P De Laverny

D Mékarnia
email: mekarnia]@obs-nice.fr

J R De Medeiros

B Vandame

The circumstellar envelope of IRC+10216 from milli-arcsecond to arcmin scales ⋆

Keywords: AGB and post-AGB -stars, variables, general -stars, individual, IRC+10216 -stars, mass-loss -stars, circumstellar matter -techniques, high angular resolution

Aims. Analysis of the innermost regions of the carbon-rich star IRC+10216 and of the outer layers of its circumstellar envelope have been performed in order to constrain its mass-loss history. Methods. High dynamic range near infrared adaptive optics and high angular resolution deep V-band images of its circumstellar envelope collected with VLT/NACO and VLT/FORS1 instruments have been analyzed. Results. Maps of the sub-arcsecond structures, or clumps, in the innermost regions are derived from the near-infrared observations. The morphology of these clumps is found to strongly vary from J-to L-band. Their relative motion appears to be more complex than proposed in earlier works: they can be weakly accelerated, have a constant velocity, or even be motionless with respect to one another. From V-band imaging, a high spatial resolution map of the shell distribution in the outer layers of IRC+10216 is presented. Shells are well resolved up to a distance of about 90 ′′ to the core of the nebula and most of them appear to be composed of thinner elongated shells. Finally, by combining the NACO and FORS1 images, a global view, showing both the extended layers and the bipolar core of the nebula together with the real size of the inner clumps is presented. Conclusions. This study confirms the rather complex nature of the IRC+10216 circumstellar environment. In particular, the coexistence at different spatial scales of structures with very different morphologies (clumps, bipolarity and almost spherical external layers) is very puzzling. This confirms that the formation of AGB winds is far more complex than usually assumed in current models.

Introduction

Low-and intermediate-mass stars lose a large amount of their initial mass when they evolve along the Asymptotic Giant Branch (AGB) and beyond. During these mass-loss events, a huge circumstellar envelope (CSE) is formed. IRC+10216 is the best-known example of such evolved stars with an optically thick CSE. Indeed, its envelope almost completely absorbs the central stellar photons in visible light and at shorter wavelengths. This circumstellar environment has therefore been mostly studied in the infrared and millimeter domains, spectral regions where the envelope radiates itself and scatters the stellar light.

At very small scales (arcsec and below), a detailed picture of the IRC+10216 central regions has already been presented by several groups (see e.g. [START_REF] Guélin | IAU Symp[END_REF]Weigelt et al. 1998Weigelt et al. , 2002;;Tuthill et al. 2000Tuthill et al. , 2005)). The central core appears to be composed of a series of clumps whose positions and luminosities vary on time-scales of a few years. The complexity of the structures detected has led to several hypotheses regarding the precise location of the central star.

At much larger scales (up to arcmin), Mauron & Huggins (1999, 2000, MH99-00 hereafter) have shown that the IRC+10216 CSE can also be studied in visible wavelength if enough deep images are collected. The nebula brightness then results from galactic ambient light scattered by its dust particles. It is detected up to very large distances to the central star (up to about 6 000 stellar radii) and thus carries information about the mass-loss history during the last few thousand years. MH99-00 have also shown that this fairly round circumstellar envelope is consistent with an isotropic galactic radiation field and a spherically symmetric dust shell (see also Mauron et al. 2003). However, on a better spatial resolution (∼ 1 arcsec), the envelope consists of a series of discrete and nested multi-ple shells (or arclets) whose origin is still debated. Although IRC+10216 is the only known AGB with such shells, similar morphology has already been detected around a dozen of planetary nebulae (PN) and about six proto-planetary nebulae (PPN). However, all these PN and PPN are bipolar, contrary to what we observe for their progenitor (assuming that IRC+10216 CSE properties are common for AGB stars). The cause and occurrence of the transition from a spherical multiple-shell CSE to a bipolar one is crucial for the understanding of the mass-loss phenomenon on the AGB and the evolution of the material ejected into the interstellar environment.

To date, no global view of the morphology of the IRC+10216 CSE at different scales exists. The aim of this work is to provide such a global description by combining new high dynamic and high spatial resolution images of its innermost regions collected with adaptive optics techniques together with new deep images of its most external layers. These observations are presented in Sect. 2. We analyze in Sect. 3 the morphology of the innermost regions and their temporal variations. Sect. 4 is devoted to the analysis of the numerous shells found in this envelope and to some of their properties. We then discuss, in Sect. 5, the coexistence of the different morphologies found in the CSE of IRC+10216. Finally, a conclusion is presented in Sect. 6.

Observations and reductions

NACO observations

Infrared images of IRC+10216 were recovered from ESO Science Archive Facility. They were obtained in November 2002 and March 2003, using the adaptive optics system NACO at the ESO/VLT Yepun telescope. NACO is an association of the adaptive optics system NAOS (Rousset 2000) and the spectro-imager CONICA (Lenzen 2003).

We have recovered observations of IRC+10216 obtained with the narrow-band filters NB 1.24 (centered at λ c = 1.237 µm, ∆λ = 0.015 µm), NB 1.26 (λ c = 1.257 µm, ∆λ = 0.014 µm), NB 1.64 (λ c = 1.644 µm, ∆λ = 0.018 µm), NB 1.75 (λ c = 1.748 µm, ∆λ = 0.026 µm), NB 2.17 (λ c = 2.166 µm, ∆λ = 0.023 µm) and the broad-band filter L ′ (λ c = 3.80 µm, ∆λ = 0.62 µm). The pixel scale on CONICA was respectively 13.27 mas in the narrow-band filters and 27.15 mas in the L ′ filter. Observation conditions, as well as total on-source integration time for each filter, are summarized in Tab. 1. Calibration files (flat fields and dark exposures) and observations of the PSF reference star HR 3550 were also recovered. The Jitter technique was used in all observations. The box size of the L ′ broad-band image was about 7 ′′ × 7 ′′ , and the box sizes in the narrow-band images varied between about 4 ′′ × 4 ′′ and 7 ′′ × 7 ′′ . As shown in Tab. 1, the seeing conditions were variable, ranging between about 0.5 ′′ and 0.8 ′′ . The best dynamic ranges of the IRC+10216 final images (not deconvolved) were around 7 000, 40 000, 6 000 and 90 000 AFU (Arbitrary Flux Units) for the J, H, K and L bands, respectively. The noise level was found to be smaller than 30 AFU in all images. In the PSF observations, the seeing varied between 0.5 ′′ and 0.6 ′′ and the air-masses between about 1.3 and 1.4. The estimated FWHM of the PSF star was around 70 mas in the J and H bands, 80 mas in the K-band and 120 mas in the L-band. Standard reduction procedures were applied using selfdeveloped routines. The raw images were sky subtracted, then divided by the flat-field and corrected from hot pixels. In each filter, the images were cross-correlated and aligned by subpixel shifting, and then combined to produce the final images, eliminating cosmic rays hits. Finally, they were deconvolved with the PSF reference star. We used the Richardson-Lucy algorithm (Richardson 1972;Lucy 1974). Since no PSF data for IRC+10216 were found in the November 22, 2002 observations, we have developed for that night pseudo-PSF images, by analyzing and comparing the other IRC+10216 observations with their corresponding PSF data. Constancy of prominent features present in deconvolved images showed that the PSF selection and the number of iterations (25 typically) for the deconvolution process was performed carefully and conservatively. We have then summed the deconvolved images in each band (see Fig. 1). The highest dynamic range J-band image was obtained from the 1.24µm and 1.26µm images, which leads to about 14 000 AFU. For the H-band, we have summed both 1.64µm and 1.75µm narrow-band images, obtaining a dynamic range of about 78 000 AFU. The three 2.17µm narrowband images were combined to build a K-band image (∼ 16 000 AFU). Finally, the L-band image has about 92 000 AFU. These images have thus the best dynamical range ever published (see e.g. Tuthill et al. 2005). We note that, over the interval of about 4 months between the first and last observations studied here, no clear variations of the positions of the structures were found.

FORS1 observations

The observations were collected with the VLT-Antu telescope, equipped with the FORS1 focal reducer. The detector is a 2048×2048 thinned 24 µm pixel Tektronix chip. The field of view of individual images is 6.8 ′ × 6.8 ′ with a pixel size of 0.2 arcsec (see Appenzeller et al. 1998). All the exposures were acquired in standard FORS1 service mode using a classical Bessel V-band filter. The available data consists of eight 10min exposure and two 20-min exposure frames collected from 10 to 11 January, 2000, leading to a total observing time of 2 hours. The selected exposures were taken in dark time under very good seeing conditions and photometric sky. A few other 10-min frames were indeed rejected due to their moderate seeing conditions. The mean airmass was 1.3. The telescope was shifted by a few arcseconds between each individual image.

It was found that individual images reduced by the standard ESO reduction pipeline (which includes standard corrections such as bias subtraction, flat fielding, etc., see [START_REF] Hanuschik | [END_REF] were of rather poor quality. We suspect this was due to the use of a corrupted flat field. Therefore, a new reduction procedure was performed for all individual exposures (removal of cosmic and aberrant pixels, flat-fielding with a specific mean sky flat for each night, etc.). All exposures taken on the same night were then shifted and stacked. The final reduced image was built by adding the summed exposures collected during the same night, taking into account of their respective total exposure time. It consists of 1900 × 1900 pixels corresponding to a total field of view of 6.3 ′ × 6.3 ′ . The resulting mean seeing, measured from the brightness profile of individual stars, is found to be around 0.65 ′′ . The central core of IRC+10216 is measured with a S/N ratio larger than 100 per pixel and the S/N of the envelope at 20 ′′ from the center is around 5-6 per pixel. For a more detailed description of this reduction procedure see Vandame (2002), and a preliminary presentation of this image can be found in de [START_REF] De Laverny | Mass-losing pulsating stars and their circumstellar matter[END_REF]. Due to the wide field of this image, we have estimated a PSF reference by using the median average of a set of suitable point-like stars, that have been, firstly, background-subtracted, centered with sub-pixel accuracy and normalized. The final FORS1 image (see Sect. 4) has been deconvolved using a Richardson-Lucy algorithm (Richardson 1972;Lucy 1974). This procedure has slightly improved the spatial resolution (to less than about 0.6 ′′ ) and the S/N ratio (to about 7-8 per pixel at 20 ′′ from the center).

The CSE innermost regions

The JHKL diffraction-limited images of IRC+10216 are displayed in Fig. 1 with a log-scale for the brightness, so that details of the morphology at all flux levels can be seen. The labels A to D shown in the H-band image indicate the features identified and labeled by [START_REF] Guélin | IAU Symp[END_REF]. The images have been centered at the central star location estimated by Murakawa et al. (2005). These authors have performed a polarimetric study of IRC+10216 in H-band which has independently provided a possible central star position, after series of contradictory hypothesis (see Weigelt et al. 2002 andTuthill et al. 2005). Following Murakawa et al. (2005), we have used clump A as reference to identify the central star position. We note that their observations were made at the same epoch as the images presented here.

Morphology at different wavelengths

The images exhibit a bright and inhomogeneous structure which roughly looks like a ring (with a diameter of approximately 0.5 ′′ ) composed of clumps (including clumps A to D) around an approximately circular depression. The depression is located at about (0.15, 0.15) ′′ from the image center and has about 6% of the intensity peak. Clumps A and C are the brightest features. Clump B appears as an elongated feature, at about the NE direction from clump C, and clump D seems to be a more spread out feature. In addition, there is a faint and almost spherical extended envelope (from ∼ 0.5 ′′ to more than 1 ′′ from the image center), which also appears to have its center in the ring depression. This depression could thus correspond to an apparent center of the images. Regarding the central star, it is located in the fainter SE region of the mentioned ring. Its position also coincides with a particular elongation in the ring brightness distribution, well seen in the H-band image. Finally, we have verified that the faintest structures seen in the J-band are ghosts, probably due to the light reflected in the NACO optics.

On another hand, a clear difference between the JHKL images concerns the brightness variations of clumps A to D with respect to the images peaks. Clump A remains close to the intensity peak in all bands whereas clumps B and D are brighter at larger wavelengths (from about 10% and 20%, respectively, of the intensity peak in J to about 100% in L). The brightness of clump C increases more slightly and is always brighter than 80%. We also note that the brightness difference between the four clumps strongly decreases with increasing wavelength. The SE region of the ring, close to the assumed location of the central star, remains faint, varying from about 8% to 20% of the intensity peak from J to L. Finally, the extent of the external envelope seems to decrease with increasing wavelength. Considering its limits at 0.2% of the image peak brightness, we have calculated its mean extent as being about 4.8 ′′ , 3.5 ′′ , 2.7 ′′ and 2.0 ′′ in the J-, H-, K-and L-band, respectively.

The clump brightness variations as well as the extent of the envelope at different wavelengths may reveal that we mostly detect, in K & L, the emission of the heated dust, whereas at shorter wavelengths the scattered stellar emission becomes more important. This is in agreement with models of the spectral energy distribution computed for IRC+10216 (see e.g. Mauron et al. 2003). In the K & L bands, the dust emission is indeed ∼ 100 times larger than the scattered stellar light which becomes dominant below ∼ 1µm. Therefore, we can deduce that most of the clumps seen close to the star in the L-band have approximately the same temperature. On the contrary, in the J-band optical depth effects could explain the different brightness of the clumps.

Temporal variations

Temporal changes of the IRC+10216 innermost regions have already been reported by Tuthill et al. (2000), Weigelt et al. (2002) and references therein. Weigelt et al. (2002) have estimated approximately linear displacements between clumps A-C and A-D, and a possible acceleration of 5 mas yr -2 for the separation A-B. Tuthill et al. (2000Tuthill et al. ( , 2005) ) have identified two sub-components in clump B: NE1 and NE2 close and far from clump C, hereafter referred as B 1 and B 2 , respectively. They have proposed that clumps B 1 , B 2 and D move away from A, apparently with an uniform acceleration of 3.4 mas yr -2 . We have also applied on the H-band 1 image a Fourier filtering procedure in order to remove the structures of lower spatial frequencies (see Fig. 2). We have then identified new features, 1 We have selected the H-band image (instead of the K-band most commonly analyzed) because of its better spatial resolution and considerably better dynamic range. We have verified that the detected clumps and their estimated positions are similar to those found in the K-band.

in particular, the sub-features B ′ 0 , B ′ 1 and B ′ 2 , in clump B. We found a feature, not identified in previous works, close to the assumed star position (labeled H). We note that the star position assumed in this work could still be discussed and its connection with clump H is very unclear. This clump could be, for instance, a dust cloud just passing between the star and the observer. Future observations are needed to study the evolution of this clump with respect to the central star position. Fig. 2 shows estimated apparent trajectories for the previously detected clumps B 1 , B 2 and D, with respect to A. These estimations were made by assuming that the clumps move away from A, as proposed by Tuthill et al. (2000), and by taking into account of their spatial separations. The solid arrows represent the displacement of these clumps during the interval time of their observations, i.e. from 1997 January to 1999 April. The dashed arrows show a prediction for the clumps displacements up to 2003 March, by assuming the averaged velocity of Tuthill et al. (2000). The dotted arrows represent an alternative prediction by assuming the acceleration law proposed by Tuthill et al. (2000). The error margins are about 10 mas for the dashed arrows and 30 mas for the dotted ones. We can see that the previous clumps B 1 and B 2 are most probably the current sub-features B ′ 1 and B ′ 2 , respectively. They are currently separated by 258 ±20, 394 ±20 and 261 ±20 mas from A. B 1 and B 2 thus appear to be less accelerated than expected. At the same time, clump D appears to have moved with a constant velocity. ¿From the clumps separations given by Weigelt et al. (2002), we have also verified that clump C (currently located at 131 ±20 mas from A) appears to be approximately motionless. Note that choosing clump A as reference could give the illusion that the clumps escape from it. The clumps motions are therefore not compatible with the uniform acceleration law proposed by previous studies, although some accelerations may exist for clumps B 1 and B 2 . New high angular resolution observations are needed to disentangle the three-dimensional morphology of the innermost environment of IRC+10216 and to study the temporal variations of these clumps.

External layers of IRC+10216

Fig. 3 shows the deconvolved wide-field V-band image of IRC+10216. As already shown by MH99-00, we see an extended halo composed of thin and irregular multiple-shells. They appear to be non-concentric and azimuthally incomplete. The CSE is seen due to external illumination by the ambient Galactic light, scattered by the dust. Since these photons can easily penetrate into the CSE (their optical depth from the outside towards the center being very low), the incomplete shells do reveal lower densities in some parts of the CSE. The shell discontinuities can obviously not be caused by some shadowing effects due to more external material.

Structure of the external layers

To emphasize the shell morphology, we have removed the central extended halo by applying the same Fourier filtering procedure as for the NACO images. We have also removed several sources (stars or galaxies) by selecting those with observed intensities larger than a prefixed threshold. The source pixels were replaced by averaged values taking into account the local background and the noise level. The resulting image is shown in Fig. 4. We have then applied an azimuthal smoothing of 20 o around the center. Although this decreases the spatial resolution in the azimuthal direction, the resulting map (Fig. 5) shows a clear visualization of the shells, and gives a more realistic and complete pattern than that presented in previous works.

The shell distribution seen in Figs. 4 & 5 are similar to those reported before. For instance, the three faint and apparently thick shells at North, located at about 30-40 ′′ , 50-60 ′′ and 70-80 ′′ from the center, can also be seen in the CFHT image (MH99-00). However, these shells, being better resolved in the FORS1 images, appear to be composed of a complex sub-distribution of thinner ones. Similar thin shells located very close each other are well seen everywhere in the CSE. For instance, the shells labeled e and f by MH00 (located at distances to the center of about 55 ′′ and 58 ′′ , between 347-20 o and 23-53 o , respectively) are clearly mergers of complex thin shell distributions. Another prominent shell located to the S, at about 15 ′′ from the center, joins series of slightly less prominent thinner ones distributed toward SE, between 10-30 ′′ from center. Moreover, even in the more internal regions, several thin shells seem to merge in thicker ones between about 4 ′′ and 20 ′′ . The whole CSE thus appears to be composed of a complex of several thin irregular shells that could be identified as thicker ones in less resolved images. Finally, we note that the separation between apparently thick shells varies a lot with respect to the radial direction.

On another hand, a smooth azimuthally radial profile derived by computing the mean of all the pixels found in annuli 0.9 ′′ thick (see MH99-00 for more details) confirms that the dust is detected up to about 200 ′′ (about 5 800 stellar radii). That corresponds to material ejected about 8 000 years ago (assuming an escape velocity of 14 km s -1 and a distance of 120 pc), i.e. an important fraction of an interpulse on the AGB. Actually, we do not see any edge for the dusty envelope and we are limited by the detector size.

Thickness of the shells

MH00 have proposed that the shells thickness increases with increasing the distance to the center, in agreement with the natural expansion of the envelope. To analyze the shell profiles and to verify their finding, we propose here a new and more accurate method, by taking into account the non-concentric nature of the shells and the possibility that an apparently thick shell may be resolved into several thinner ones.

Fig. 6 shows the CSE morphology in a map of narrow2 radial strips. From this diagram, we clearly see the nonconcentric nature of the shells as well as the complexity of their spatial distribution. For instance, the long and thin shell located at distances to the center of about 15-20 ′′ , between 220 o and 340 o from North, has an inclination of about 4 o in this diagram with respect to the vertical axis. The shell located at distances to the center of about 30-35 ′′ , between 300 o and 360 o from North, has an inclination of about 17 o . At the same time, there are shells with opposite orientations, as those found at distances to the center of about 25-30 ′′ and 45 ′′ , from 210 o to 260 o , and from 200 o to 230 o , respectively, which make angles of about -16 o and -2 o with respect to the vertical axis.

Regarding the profile of the thickest shells, we have carefully analyzed the shell labeled d in MH00. Fig. 7(a) shows its profile by applying the same method as those authors. Fig. 7(b) shows the profile of the same shell estimated from the more restricted region located at a distance to the center of about 39 ′′ , between 70-90 o , where it appears more regular. We have then integrated profiles perpendicular to its direction and subtracted the extended halo contribution. The estimated FWHM of the profile (a) is about 3.0 ′′ (as in MH00), whereas it is about 2.6 ′′ for the profile (b). The error margins are around 0.4 ′′ . Although both estimates are in agreement within the error bars, a deeper analysis of this shell reveals that even our profile (b) could be widened due to a merging of two thinner ones. Indeed, the profiles shown in Fig. 7 have two close peaks at offsets of about ±0.5 ′′ , which leads us to suspect that there are two thin shells close together in this region and, hence, not well spatially resolved. We note that this shell was carefully analyzed, this pair of peaks being identified in every derived profiles. If we decompose the profile (b) in two close shells, their estimated FWHM are about 1.8 ′′ ± 0.4 ′′ . In consequence, we have derived several shell profiles by identifying, as above, well resolved thin shells at different distances to the center, (see Fig. 8). The profiles (a) to (d) have good S/N ratios. The profile (e) having a worse S/N ratio is actually a thin feature composing an apparently thicker shell which was also detected by MH00. The FWHM of the two features in the profile (a) and the other four features in the profiles (b) to (e) are, respectively, about: 1.2 ′′ , 1.6 ′′ , 1.5 ′′ , 1.7 ′′ , 1.6 ′′ and 1.4 ′′ . The error margins are around 0.4 ′′ . In a more general way, we have estimated the FWHM of 23 shells at radial distances from 4 ′′ to 80 ′′ (see Fig. 9). We found a mean FWHM value of 1.6 ′′ , with a standard deviation of 0. and the distance to the center such as that proposed by MH00. However, the shells found rather close from center could be resolved into even thinner ones. We therefore could have overestimated their thickness. In consequence, either the slope of the thickness variation with distance proposed by MH00 could still be valid but with very thin shells close to the star, or the shell thickness increases much less than that estimated by these authors.

Global view of IRC+10216

In order to better understand the possible links existing between the almost spherical shells and the inner clumps, we describe here the morphology of the inner CSE from the FORS1 image together with the NACO data.

Fig. 10 (left panel) shows a closer view of Fig. 4. MH99-00 have detected three structures suspected to be shells in regions within about 3.1 ′′ from the center, whereas no such shells are found in our data, possibly because the HST data have a better spatial resolution, despite their lower S/N ratio. The closest identifiable structures are located between ∼3 ′′ and 16 ′′ from center. Regarding the core of the nebula, it appears clearly asymmetric. Two dominant lobes much brighter than the rest of the envelope lie around the center, making together a direction of about 22 o ±2 o with respect to North. The southern lobe being 40% brighter than the northern one. These features likely result from scattered stellar photons in contrast to the reflected galactic light seen elsewhere in the envelope. Such bipolar morphology could be an indication that scattering is more efficient in the polar direction. It could be roughly reproduced by a simple model of scattering dust grains in a non-spherical dusty envelope, with evacuated polar regions, around the star, the system being tilted away from the observer (see e.g. Men'shchikov et al. 2001). We however note that the main shape of the bipolar nebula slightly differs from the one reported by MH00 from their HST image, possibly due to the different spatial resolution.

Fig. 10 (right panel) gives a representation of the IRC+10216 core, by superposing the NACO and FORS images. The two images were arbitrarily positioned by coinciding their intensity peaks. We are conscious that this assumption may be crude since the V and IR images result from very different physical processes. However, this composite image represents for the first time both the extended layers and the bipolar core together with the real size of the inner clumps, and puts forward the difficulty of finding a link between such small and large scale morphologies. Firstly, evidence of clumps far from the center was not found by Huggins & Mauron (2002) in a previous analysis of the same FORS1 image. Secondly, shells cannot be identified in the NACO images. We note that, although the region composed of clumps A to D roughly looks like a ring, its center (the depression feature) is not compatible with the star position estimated by Murakawa et al. (2005). Finally, the bipolar structure detected in V-band is also not clearly identified in the near-IR.

Conclusion

We have described in this work very high quality images of the CSE of IRC+10216, from its most inner regions to the most external ones. In the central arcsec scale of the JHKL images, sub-arcsec structures (or clumps) identified by other authors have been recovered about 4 years later. We have also derived a map of the brightest clumps found close to the core of IRC+10216. The morphology of these clumps varies strongly with increasing wavelengths and we propose that the closest structures have about the same temperature. Furthermore, by analyzing their apparent relative motion, we cannot confirm the uniform outflow acceleration previously proposed since only two bright clumps appear to be accelerated (but at a smaller rate than that already estimated), whereas others clumps could have a constant velocity or even no relative motion. At much larger spatial scales (up to a few arcmin), we present a new map of the non-spherical incomplete shells characterizing the CSE of IRC+10216. Owing to the high spatial resolution of our image, most of the thicker shells actually appear to be composed of thinner elongated ones. Their thicknesses appear rather uniformly distributed between about 1 ′′ and 2 ′′ , regardless of the distance to the center. Finally, we have combined the NACO and FORS images in order to provide a more global view of this CSE and to compare the typical size of the clumps found very close to the center with the bipolar nebula and with the much more external shells.

This study has confirmed the very complex nature of the IRC+10216 envelope with asymmetries already present on the AGB. Neither the morphology at different spatial scales nor the motions detected very close to the center can be satisfactory explained by current models on the mass-loss mechanisms in AGB stars and their typical time-scales. For instance, Sandin & Höfner (2004 and references therein) have predicted shell density distributions not compatible with those observed around IRC+10216 (see also on the same topic Meijerink et al., 2003). Another scenario for the formation of the shells in a spherically symmetric stellar wind has been explored by Soker (2000Soker ( , 2002)). He proposed that these shells could be connected to cool magnetic spots on the stellar surface. If these spots are more concentrated near the equator, the mass-loss geometry could deviate from sphericity and thus favor the formation of shelllike features and/or clumps.

Moreover, it is interesting to note that the very complex structures found around IRC+10216 may affect the chemical composition of its envelope. For instance, the clumps detected very close to the central core may favor, by their thermodynamical properties, the formation of the graphite observed in presolar dust grains (Bernatowicz et al. 2005). Furthermore, the presence of high density shells in the photochemically active regions could change the molecular distribution in the envelope by blocking external UV photons (see e.g. MH00; Brown & Millar 2003). Then, high contrast shells of complex molecules may be formed more easily, as confirmed by some millimeter observations (see for instance, HCO + , C 2 H, C 4 H and HC 5 N maps by [START_REF] Guélin | IAU Symp[END_REF]CO maps by Fong et al., 2003).

Finally, future high spatial resolution images of this CSE are still mandatory in order to better understand the motions of its clumps (and in particular the clump H superposed on the assumed central star), their formation/fading, the central star position and the possible evolution of the external shells as their three-dimensional morphology.

⋆

  Based on observations collected with the VLT/Antu and Yepun telescopes (Paranal Observatory, ESO, Chile) using the FORS1 and NACO instruments (programs ID 63.I-0177A, 70.C-0565A, 70.C-0271B, 70.D-0271B).

Fig. 1 .

 1 Fig. 1. NACO JHKL images of IRC+10216. Contour levels are 80, 60, 40, 20, 10, 8, 6, 4, 2, 1, 0.5, and 0.2% of the peak surface brightness. North is up and East is left. The white cross at each image center represents the assumed central star position and its size is proportional to the error of 0.03" as given by Murakawa et al. 2005. Clumps A to D of the H-band follow the Haniff & Buscher (1998) clump nomenclatures. The resolution is about 70 mas in the J and H bands, 80 mas in K and 120 mas in L (represented by the circles at each image corner).

  Fig. 2. H-band map of IRC+10216, where only the highest spatial frequencies of the brightness in the Fourier space have been kept. The contour levels are 80, 50, 20, 10, 8, 5, 2, 1 and 0.5% (this minimum level being the estimated noise). The dotted circle is the assumed star position, as in Fig. 1. Main clumps are indicated by the labels A to D, and some sub-features by the smaller labels. The positions of these clumps are shown by the white crosses. Estimated trajectories (from the results of Tuthill et al. 2000) for some clumps with respect to clump A are also shown (see text for details).

Fig. 3 .

 3 Fig. 3. FORS1 deconvolved V-band image of IRC+10216. North is up and East is left.

Fig. 4 .

 4 Fig. 4. Deconvolved V-band image after subtraction of the halo of the CSE and removing of most stars and galaxies.

Fig. 5 .

 5 Fig. 5. Schematic map of the shells surrounding IRC+10216 (see text for details).

Fig. 6 .

 6 Fig. 6. Map of the shells transformed from polar coordinates to a Cartesian representation. Each horizontal section of these maps represents a narrow radial strip of the FORS1 images, the angles of the strips being with respect to North. Maps to the left, center and right were derived from Figs. 3, 4 & 5, respectively.

  3 ′′ . The minimum FWHM value of 1.2 ′′ is found for the innermost analyzed shell, located at about 4 ′′ from center between 40-100 o , and also for two shells located at about 9 ′′ and 11 ′′ from the center, between 150-180 o and 205-255 o , respectively. Comparison of the profile of the shell d estimated as in MH00 (top panel) with its profile derived by our more complex method (bottom panel, see text for details). The distances to the center, d, of the shells and the ranges of their azimuthal angles from north, ang, are given. The relative intensity is with respect to the central peak brightness of the original image.maximum FWHM value of 2.0 ′′ is found for the shells located at about 24 ′′ and 25 ′′ , between 105-135 o and 60-85 o , respectively. The furthest analyzed shell, located at about 80 ′′ from center, between 30-45 o , has a FWHM ≃ 1.9 ′′ . The error bars vary between 0.3 ′′ and 0.6 ′′ . Thin shells are thus detected in the whole envelope and even far from center. We therefore cannot derive a clear increasing relation between the shell thickness ang Profiles of some shells considering their non-concentric nature (see text for details). There are two shells in the panel (a) and one in the others. The given parameters follow the same definitions as in Fig.7. The ranges in the shell distances to the center, d, are due to their non-concentric nature.

Fig. 9 .

 9 Fig. 9. Relation between the thickness of the shells and their distance to the center.

Fig. 10 .

 10 Fig. 10. View of the IRC+10216 CSE inner morphology. Left panel shows the closest structures around the center detected in V-band. Right panel shows the V-band image core, on which has been superposed the NACO H-band image. The overlapping has been done by assuming that the NACO and FORS peak brightness are found at the same location.

Table 1 .

 1 NACO observations log of IRC+10216.

	Date	Filter	On-source Seeing	Air-	Dyn.
			exp. time		mass	range
	(UT)		(sec)	( ′′ )		(AFU)
	22 Nov 02 NB 1.64	128	0.6	1.5	28 700
		NB 2.17	120	0.6	1.6	6 200
	16 Mar 03 NB 1.26	120	< 0.5	1.3	6 900
		NB 1.64	70	< 0.5	1.3	8 300
		NB 2.17	60	< 0.5	1.3	4 800
	18 Mar 03 NB 1.24	210	0.6	1.3	7 200
		NB 1.75	103	0.7	1.3	41 500
		NB 2.17	200	0.8	1.3	5 200
		L ′	183	0.6	1.3	92 500

Radial sections with thickness of 1 pixel and a rotation step of 0.2 o . Interpolations between the original image pixels were applied for each step.

Acknowledgements. We thank N. Mauron for fruitful and stimulating discussions over all these years and his comments on the manuscript, and D. O'Brien for proofreading it. The Brazilian agencies CAPES and CNPq are thanked for financial support. P. de Laverny acknowledges support from the CNRS/INSU (Actions Thématiques Innovantes) and MESR (Jeunes Chercheurs).

List of Objects

'IRC+10216' on page 1


Paul Picavet

Sibrecht Reniere

Veerle Cnudde

Wim De Clercq

Roland Dreesen

Gilles Fronteau

Eric Goemaere

Else Hartoch

THE MACQUENOISE SANDSTONE : A suitable Lochkovian raw material for ancient millstones : quarries, properties, manufacture and distribution (Belgium-France)

published or not. The documents may come

L'archive ouverte

THE MACQUENOISE SANDSTONE :

A suitable Lochkovian raw material for ancient millstones : quarries, properties, manufacture and distribution (Belgium-France) A sandstone formation, formally defi ned as the "Arkose d'Haybes" is closely linked to the former Lochkovian conglomerates (called also the "Poudingue de Fépin"). Th e «Formation de Fépin» crops out around the eponymous localities : Haybes, Fépin and Hargnies (Ardennes, France), and displays a partly recrystallized, well-sorted grey to greenish sandstone (turbidite facies) with wine-red coloured joints.

Another important area displaying old quarries in the same formation is located between Hirson (Aisne, France) and Macquenoise (Hainaut, Belgium): here, a more homogenous grey coarse sandstone facies occurs, with a better consistency and small amounts of dark green tourmaline crystals.

...to archaeological investigation

Th ese detrital formations were locally quarried, not only for building stones but also for the manufacturing of querns and millstones. Th e huge Proto-historical and Roman millstone quarries in Lochkovian conglomerates and sandstones of Macquenoise and Milourd areas are known and identifi ed since the end of the 19th century.

Even if the lack of recent archeological excavations do not allow yet to precise the exact age of their quarrying, the dating of the various products derived from these quarries allows to consider the duration of the active extraction and how far the millstones from Macquenoise area were transported into the northern Gallo-Roman provinces.

Th ese products consist both of small querns operated in domestic hand mills, and of large millstones used in hydraulic geared mills. Th ey are massively found on Roman archaeological sites of the North-western Ardennes and the Western Flemish Basin, 100 km from the Macquenoise area (regional scale). Th ese heavy goods are still but sparsely unearthed in a 200 km radius, especially in big Roman cities, well served by the main commercial roads. Th is distant distribution however requires more than one day of transport by inland waterway and more than fi ve days by road.

Since a few years a French-Belgian team of archeologists and geologists works on the characterization of ancient querns and millstones (mainly from the Antique period) in relation to the determination of their stratigraphical and geographical provenances.

Th rough the identifi cation of the rocks, their origin and their distribution, this collaboration aims at reconstructing the ancient commercial roads and the economic organization of territories during that period.

  Main Roman roads (after Deru 2010) Limits of massive distribution Limits of maximum distribution Near the Ardenne border between France and Belgium, the earliest Devonian (Lochkovian) sediments deposited on the Caledonian substrate are conglomerates, which are interpreted as continental alluvial fans.

  . Lille, CNRS, Ministère de la Culture et de la Communication, UMR 8164 -HALMA -Histoire Archéologie Littérature des Mondes Anciens, F-59000 Lille, France. 2, 4: Ghent University -Faculty of Arts and Philosophy, Historical Archaeology Research Group (HARG), Sint-Pietersnieuwstraat 35-UFO B-9000, Ghent, BELGIUM 3: Ghent University -Faculty of Sciences, Department of Geology and Soil Science, Krijgslaan 281, S8, B-9000, Ghent, BELGIUM 5: Geological Survey of Belgium, OD Earth and History of Life, Royal Belgian Institute of Natural Sciences & Gallo-Roman Museum, Tongeren, BELGIUM 6: GEGENAA (EA3795) Université de Reims Champagne-Ardenne, 2 Esplanade Roland Garros, F-51100 Reims, FRANCE 7: Geological Survey of Belgium, OD Earth and History of Life, Royal Belgian Institute of Natural Sciences, 13, Rue Jenner, B-1000 Bruxelles, BELGIUM 8: Gallo-Roman Museum, Kielenstraat 15, B -3700 Tongeren, BELGIUM 9 : Groupe Meule : https://groupemeule.hypotheses.org HARTOCH (éd.) et al. 2015 : Moudre au Pays des Tungri, Tongeren, Publications of the Gallo-Roman Museum, 416 p. (Atuatuca 7) MEILLIEZ 1984 : « La Formation de Fépin (Gedinnien de l'Ardenne) : un marqueur régional lithostratigraphique et structural », Ann. Soc. géol. Nord, 103, 37-53. PICAVET et al. 2016 (in press) : « Les productions de meules en grès dévonien dit "arkose" d'Haybes/Macquenoise de la fi n de l'Âge du Fer à l'Antiquité tardive dans le nord de la Gaule. Caractérisation typologique, chronologie et diffusion », in BUCHSENSCHUTZ et al. (éd.), Les meules à grain du Néolithique à l'Époque Médiévale : technique, culture, diffusion, actes du colloque de Reims 15-17 mai 2014, Dijon. (Revue Archéologique de l'Est, supplément) BIBLIOGRAPHY Querns and millstones made of Lochkovian sandstone found on Gallo-Roman sites Plane polarized light microphotograph © Paul Picavet

th International Geologica Belgica meeting, 26-29 january 2016, Mons, Belgium : «Mother Earth» --Geoarchaeology, Geoheritage, Geoeductation --


Nicole Revel

The World of Orality

If the senses give us access to the world and if the world seems to profile itself spatially, then we tend to grant primacy to visual perception and descriptions of the real. However 1 would also like to show that a complex and subtle web uniting the subject and the world involves al1 the senses and more particularly that of hearing and aura1 sensitivity.