About the position
About the research centre or Inria department The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022. It brings together approximately 410 people in 20 research teams and research support services. Its staff are distributed in Villeurbanne, Lyon Gerland, and Saint-Etienne. The Lyon centre is active in the fields of software, distributed and high-performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology. Context 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. Assignment 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. Main activities Main activities (5 maximum) : Research Software development Experimentation Report & article writing Skills Technical skills and level required : Notions in compilers, parallelism, parallel architectures, parallel runtimes Experience with C++ Languages : English, French Excellent relational skills.
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