Reusable semantics for implementation of Python optimizing compilers

Le langage de programmation Python est aujourd'hui parmi les plus populaires au monde grâce à son accessibilité ainsi que l'existence d'un grand nombre de librairies standards. Paradoxalement, Python est également reconnu pour ses performances médiocres lors de l'exécution de nombreuses tâches. Ainsi, l'écriture d’implémentations efficaces du langage est nécessaire. Elle est toutefois freinée par la sémantique complexe de Python, ainsi que par l’absence de sémantique formelle officielle. Pour régler ce problème, nous présentons une sémantique formelle pour Python axée sur l’implémentation de compilateurs optimisants. Cette sémantique est écrite de manière à pouvoir être intégrée et analysée aisément par des compilateurs déjà existants. Nous introduisons également semPy, un évaluateur partiel de notre sémantique formelle. Celui-ci permet d'identifier et de retirer automatiquement certaines opérations redondantes dans la sémantique de Python. Ce faisant, semPy génère une sémantique naturellement plus performante lorsqu'exécutée. Nous terminons en présentant Zipi, un compilateur optimisant pour le langage Python développé avec l'assistance de semPy. Sur certaines tâches, Zipi offre des performances compétitionnant avec celle de PyPy, un compilateur Python reconnu pour ses bonnes performances. Ces résultats ouvrent la porte à des optimisations basées sur une évaluation partielle générant une implémentation spécialisée pour les cas d'usage fréquent du langage.Python is among the most popular programming language in the world due to its accessibility and extensive standard library. Paradoxically, Python is also known for its poor performance on many tasks. Hence, more efficient implementations of the language are required. The development of such optimized implementations is nevertheless hampered by the complex semantics of Python and the lack of an official formal semantics. We address this issue by presenting a formal semantics for Python focussed on the development of optimizing compilers. This semantics is written as to be easily reusable by existing compilers. We also introduce semPy, a partial evaluator of our formal semantics. This tool allows to automatically target and remove redundant operations from the semantics of Python. As such, semPy generates a semantics which naturally executes more efficiently. Finally, we present Zipi, a Python optimizing compiler developped with the aid of semPy. On some tasks, Zipi displays performance competing with those of PyPy, a Python compiler known for its good performance. These results open the door to optimizations based on a partial evaluation technique which generates specialized implementations for frequent use cases

A Scalable Workflow for a Configurable Neuromorphic Platform

This thesis establishes a scalable multi-user workflow for the operation of a highly configurable, large-scale neuromorphic hardware platform. The resulting software framework provides unified low-level as well as parallel high-level access. The latter is realized by an efficient abstract neural network description library, an automated translation of networks into hardware specific configurations and an experiment server infrastructure responsible for scheduling and executing experiments. Scalability, manual guidance and a broad support for handling hardware imper- fections render the model translation process suitable for large networks as well as large-scale neuromorphic systems. Networks with local connectivity, random networks and cortical column models are explored to study the topological aptitude of the neuromorphic platform and to benchmark the workflow. Depending on the model, performance improvements of more than two orders of magnitude have been achieved over a previous implementation. Additionally, an automated defect assessment for hardware synapses is introduced, indicating that most synapses are available for model emulation. In a second study, a tempotron-based hardware liquid state machine has been developed and applied to different tasks, including a memory challenge and digit recognition. The trained tempotron inherently compensates for fixed pattern variations making the setup suitable for analog neuromorphic hardware. The achieved performance is comparable to reference software simulations