It appears that if all the projects of the LRDE involve various application areas, a common underlying problem is how to reconcile two aspects that are usually contradictory: performance and genericity.
This issue has led the lab's teams to develop a long-term software engineering based on technical C++ with many templates. This approach has its drawbacks. Disadvantages include the fact that C++ is a heavy language with an extremely complex and ambiguous syntax, that the template system is actually a completely different language from standard C++, and finally that it is a static language. This last point has significant implications on the application, insofar as it imposes a strict chain of Compilation => Development => Run => Debug, making for example rapid prototyping or human-machine interfacing activities difficult. It becomes therefore essential to equip the involved projects with a third language infrastructure that is rather based on scripting languages.
Facing these problems, another team of the laboratory has chosen to focus on the persistent question of performance and genericity with a transversal approach, that is to say focused on alternative languages rather than on a particular application domain. The purpose of this research is therefore to examine the solutions offered by languages other than C++, especially dynamic languages, and we chose Common Lisp.
At first glance, it seems paradoxical indeed to use, for performance issues, a language which is legitimately expected to deteriorate them. Almost by definition, a dynamic language does less at compile-time and more at run-time (eg, type checking). Most modern dynamic languages like Python and Ruby match these preconceived notions. Common Lisp, however, has this ability to let you static type pieces of the code you write, and let the rest be dynamically typed. A compiler that is properly informed about such types is then free to perform any possible optimization, even to produce a weakly typed executable as a C compiler would do.
Therefore, one goal of this research focuses on comparing the performance obtained by some Common Lisp compilers with those of equivalent programs written in C or C++. This work involves a substantial set of performance measurements on micro-benchmarks to evaluate the cost of some basic operations (arithmetic, object instantiation, access to their members, dynamic dispatch, etc.).
Once the issue of performance is resolved, we still have to demonstrate the benefits of a dynamic language for genericity. The basis of this approach lies in a fact that is this time widely recognized: dynamic languages are more expressive. By eliminating especially the static typing, and consequently, by expanding the scope of polymorphism, dynamic languages provide paradigms that are impossible or difficult to obtain in static languages. If we take simply the paradigm of object-oriented programming, Common Lisp goes further than other (static or dynamic) languages if that the object layer is based on a so-called meta-object protocol: a reflexive architecture that implements the object layer in itself, and that also allows to modify or extend it. Thus, far from the classical OO approach of traditional languages, the object-oriented layer of Common Lisp includes natively multimethods, and allows the use (and mix) of traditional classes-based, prototype-based, context-oriented approaches, etc.
The second line of research in this work consists in the study of all these new programming paradigms in terms of what they bring to the notion of genericity. To do this, we have chosen to clone one of the oldest projects of the laboratory (Olena) and to express the same issues following an axis of dynamic genericity.
Finally, a longer-term perspective will be to reconcile these aspects of dynamic genericity with the performances of a dedicated code. This step should also be made significantly easier than the manipulation of C++ templates because of the structural reflexivity of Lisp (code and data are represented in the same way). The system of macros in Common Lisp is unique because it can perform any function written in Lisp itself during the compilation, these functions themselves having access to the code to compile. This allows in particular to rewrite the code on the fly and provides for native JIT-compilation.
|newton.19.els||Finite Automata Theory Based Optimization of Conditional Variable Binding||Jim Newton, Didier Verna||European Lisp Symposium||14 January 2019|
|newton.18.meta.pdf||Recognizing heterogeneous sequences by rational type expression||Jim Newton, Didier Verna||Proceedings of the Meta'18: Workshop on Meta-Programming Techniques and Reflection||14 September 2018|
|newton.18.els||Approaches in Typecase Optimization||Didier Verna, Jim Newton||European Lisp Symposium||5 April 2018|
|verna.18.els||Method Combinators||Didier Verna||11th European Lisp Symposium||25 March 2018|
|newton.17.els||Programmatic Manipulation of Common Lisp Type Specifiers||Jim Newton, Didier Verna, Maximilien Colange||European Lisp Symposium||6 February 2017|
|newton.16.els||Type-Checking of Heterogeneous Sequences in Common Lisp||Jim Newton, Akim Demaille, Didier Verna||European Lisp Symposium||25 March 2016|
|verna.15.cop||Context-Oriented Image Processing||Didier Verna, François Ripault||Context-Oriented Programming Workshop||1 January 2015|
|senta.12.els||Generic Image Processing with Climb||Laurent Senta, Christopher Chedeau, Didier Verna||European Lisp Symposium||1 May 2012|
|verna.08.els||Binary Methods Programming: the CLOS Perspective||Didier Verna||Proceedings of the First European Lisp Symposium||31 March 2009|
|verna.09.ilc||CLOS Efficiency: Instantiation||Didier Verna||Proceedings of the International Lisp Conference||13 January 2009|
|verna.09.accu||Revisiting the Visitor: the Just Do It Pattern||Didier Verna||Proceedings of the ACCU Conference 2009||29 December 2008|
|verna.06.imecs||How to make Lisp go faster than C||Didier Verna||Proceedings of the International MultiConference of Engineers and Computer Scientists||11 June 2007|
|verna.07.imecs||CLOS solutions to binary methods||Didier Verna||Proceedings of the International MultiConference of Engineers and Computer Scientists||22 January 2007|
|verna.06.ecoop||Beating C in Scientific Computing Applications||Didier Verna||Third European Lisp Workshop at ECOOP||11 July 2006|
|newton.18.tocl||A Theoretical and Numerical Analysis of the Worst-Case Size of Reduced Ordered Binary Decision Diagrams||Jim Newton, Didier Verna||ACM Transactions on Computational Logic||28 August 2018|
|verna.10.jucs||Revisiting the Visitor: the Just Do It Pattern||Didier Verna||Journal of Universal Computer Science||1 January 2010|
|verna.08.jucs||Binary Methods Programming: the CLOS Perspective (extended version)||Didier Verna||Journal of Universal Computer Science||1 January 2008|
|verna.06.ijcs||How to make Lisp go faster than C||Didier Verna||IAENG International Journal of Computer Science||1 December 2006|
|newton.17.dtd.report||Analysis of Algorithms Calculating the Maximal Disjoint Decomposition of a Set||Jim Newton||2 February 2017|
|newton.16.monad.report||Monads in Common Lisp||Jim Newton||18 November 2016|
|newton.16.dag.report||Finding maximal common joins in a DAG||Jim Newton||17 November 2016|
|newton.16.rte.report||Efficient dynamic type checking of heterogeneous sequences||Jim Newton||22 February 2016|
|verna.12.dsl||Extensible languages: blurring the distinction between DSLs and GPLs||Didier Verna||Formal and Practical Aspects of Domain-Specific Languages: Recent Developments||1 September 2012|
PHD Theses and Habilitations
|newton.18.phd||Representing and Computing with Types in Dynamically Typed Languages||Jim Newton||1 November 2018|