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
- 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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Post-Doctoral Research Visit F/M Dynamic Parallelization of Sparse Codes for High-Performance Computing and Machine Learning in Lyon employeur : Inria Lyon
1000scholars est un employeur exceptionnel, offrant un environnement de recherche dynamique au sein du Laboratoire de l'Informatique du Parallélisme à l'ENS Lyon. Avec un fort accent sur l'innovation et la collaboration, les chercheurs bénéficient d'opportunités de croissance professionnelle, d'un accès à des ressources de pointe et d'une culture de travail stimulante qui valorise la créativité et l'excellence. Rejoindre notre équipe, c'est participer à des projets de pointe dans le domaine de l'informatique haute performance et de l'apprentissage automatique, tout en évoluant dans une ville riche en culture et en savoir-faire scientifique.
