Post-Doctoral Research Visit – Dynamic Parallelization of Sparse Codes for High-Performance Computing and Machine Learning

Type de contrat : CDD Contexte et atouts du poste Within the framework of the French prioritary research program for Exascale computing in France (PEPR NumPEx), we are hiring a postdoctoral fellow to address runtime code optimization for sparse computing.  This research targets both High-Performance Computing and Machine Learning. This postdoctoral fellowship will be held at Laboratoire de l'Informatique du Parallélisme at Ecole Normale Supérieure de Lyon, France. Mission confiée Most kernels of interest in machine learning and high-performance computing manipulate sparse tensors.  Sparse codes are highly irregular and make use of array indirections and dynamic control which jeopardize static automatic parallelization algorithms.  The overall objective of this postdoctoral fellowship is to investigate compiler and runtime algorithms to delay the specialization of the dense code at runtime when the sparse structure is known. From a dense specification, we seek to compile a code able to specialize itself on the sparse input data. The specialization will involve a set of sub-computations, which are expected to be achievable by standard linear algebra routines (e.g. gemm), using state-of-the art linear algebra libraries. Several issues must be investigated: • How to specialize the code? In particular, how propagate efficiently the sparsity along the computation flow?  • How to detect library kernels on the specialized code? • How to enforce a proper scheduling for the parallel runtime? Points 1 and 2 have been partially addressed by a PhD student. The postdoctoral fellow will: • Propose code optimizations and data structures for scaling sparse propagation (point 1) • Address runtime scheduling (point 3) by relying on existing parallel runtimes • Validate the complete approach (points 1, 2 and 3) on scientific benchmarks by using sparse tensors from the Florida sparse matrix collection as well as machine learning applications. Principales activités Main activities (5 maximum) : • Research • Software development • Experimentation • Report & article writing Compétences Technical skills and level required :  • Notions in compilers, parallelism, parallel architectures, parallel runtimes • Experience with C++ Languages : English, French Excellent relational skills. Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l’État pour les stratégies nationales de recherche et d’innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d’innovation avec ses 3500 scientifiques, ingénieurs et personnels d’appui, en partenariat avec les universités et l’écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l’informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l’impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.