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Section: Scientific Foundations

Federating Polyhedral Tools

Participants : Christophe Alias, Fabrice Baray [ Mentor, Former Compsys Post-Doc ] , Alain Darte, Antoine Fraboulet, Paul Feautrier, Tanguy Risset.

Present-day tools for embedded system design have trouble handling loops. This is particularly true for logic synthesis systems, where loops are systematically unrolled (or considered as sequential) before synthesis. An efficient treatment of loops needs the polyhedral model. This is where past results from the automatic parallelization community are useful. The French community is a leader in the field, mainly as one of the long-term results of the C3 cooperative research program.

The polyhedral model is now widely accepted (Inria projects Cosi and A3, now r2d2 and Alchemy, PIPS at École des Mines de Paris, Suif from Stanford University, Compaan at Berkeley and Leiden, PiCo from the hp  l abs, the dtse methodology at Imec, etc.). Most of these groups are research projects, but the increased involvement of industry (Hewlett Packard, Philips) is a favorable factor. Polyhedra are also used in test and certification projects (Verimag, Lande, Vertecs). A very recent development is the interest, shown by several compiler groups, in polyhedral methods (the gcc group, Reservoir Labs in the USA).

Two basic tools that have emerged from this early period are Pip [37] and Polylib [51] . They are currently the only available tools since maintenance has stopped on Omega (Maryland). Their functionalities are parametric integer programming and manipulations of unions of polyhedra. Granting that the showroom effect is important for us (these tools are used in many foreign laboratories), we nevertheless think that maintaining, improving, and extending these tools is a proper research activity. One of our goals must also be the design of new tools for new scheduling/mapping techniques.

In the following, we distinguish between the development of existing tools and the conception and implementation of new tools. These tasks are nevertheless strongly related. We anticipate that most of the new techniques will be evolutions of the present day tools rather than revolutionary developments.

Developing and Distributing the Polyhedral Tools

Recently, we have greatly increased the software quality of Pip and Polylib. Both tools can now use exact arithmetic. A cvs archive has been created for cooperative development. The availability for one year of an odl software engineer has greatly improved the Polylib code. An interface bridge for combined use of the two tools has been created by Cédric Bastoul (former PhD student of Paul Feautrier). These tools have been the core of new code generation tools [33] , [45] widely used in prototyping compilers. Paul Feautrier is the main developer of Pip, while Tanguy Risset has been in charge of coordinating the development of Polylib for several years. Other participants are at Irisa (Rennes) and ICPS (Strasbourg), and also in Lyon and Leiden. In the near future, we contemplate the following actions:

New Models

Industry is now conscious of the need for special programming models for embedded systems. Scholars from the University of Berkeley have proposed new models (process networks, sdl , etc.). This has culminated in the use of Kahn process networks, for which a complete overhaul of parallelization techniques is necessary (see Section  3.3.1 ). Optimizations for memory reduction are also very important. We are developing a tool, based on operations on integral lattices (including Minkowski's successive minima), named Cl@k, that can be used to derive affine mappings with modulo operations for memory reuse (see more details in Section  5.7 ).

Besides, our community has focused its attention on linear programming tools. For embedded systems, the multi-criteria aspect is pervasive, and this might require the use of more sophisticated optimization techniques (non-linear methods, constraint satisfaction techniques, ``pareto-optimal'' solutions).

Here again, our leadership in polyhedral tools will make our contributions in these areas easier. We nevertheless expect that, as sometimes in the past, the methods we need have already been invented in other fields like operational research, combinatorial optimization, or constraint satisfaction programming, and that our contribution will be in the selection and adaptation (and possibly the implementation) of the relevant tools.


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