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PhD Position F/M Foundation of an HPC Composition Model



Job description

Contexte et atouts du poste

Numerical simulation is a key technology for many application domains.

Thanks to the democratization of high performance computers (HPC), complex physics and more generally complex systems can now be simulated routinely.

Numerical simulation is considered as the third pillar of sciences (with experiment and theory) and is critical to gain competitive position.


Though significant efforts have been devoted to the implementation and optimization of several crucial parts of a typical HPC software stack, most HPC experts agree that exascale supercomputers will raise new challenges, mostly because the trend in exascale compute-node hardware is toward heterogeneity and scalability: Compute nodes of future systems will have a combination of regular CPUs and accelerators (typically GPUs), along with a diversity of GPU architectures.


Meeting the needs of complex parallel applications and the requirements of exascale architectures raises numerous challenges which are still left unaddressed.

As a result, several parts of the software stack must evolve to better support these architectures.

More importantly, the links between these parts must be strengthened to form a coherent, tightly integrated software suite.


The

Mission confiée

The particular issue that this PhD shall tackle is to provide a model to composose code develop accordingly to various HPC programming paradigms.

Software composition is an old [NATO] but still key technique [SZY] to develop complex applications by being able to divde them into manageable code.

Though it is common in traditionnal sequential programming, composition parallel codes is still challenging.

Previous work demonstrated that composition within a parallel programming is possible and do not impact performance [CCA, HLCM, Comet].

However, we are lacking a model to compose distinct paradigms without mentioning the specific problem of runtime cohabitation in an HPC context.


A particular target of this work could be the Multi-Level Intermediate Representation (MLIR) framework [MLIR], aiming to harness MLIR not only as a unifying compilation target for heterogeneous input Domain Specific Languages (DSLs) but also as a semantic backbone for expressing data flow, and reasoning about scheduling.


 Bibliography:

  • [NATO] Mcllroy, M.D., Mass Produced Software Components.

    Proceedings of NATO Software Engineering Conference, Garmisch, Germany, October 1968, 138-155.


  • [SZY] Clemens Szyperski.

    Component Software : Beyond Object-Oriented Programming.

    Addison-Wesley Longman Publishing Co., Inc., 2002.

  • [CCA] B.

    A.

    Allan, R.

    Armstrong, D.

    E.

    Bernholdt, and al.

    A component architecture for high-performance scientific computing.

    International Journal of High PerformanceComputing Applications, 20 :163–202, 2006.

  • [HLCM] Julien Bigot.

    Du support générique d’opérateurs de composition dans les modèles de composants logiciels, application au calcul scientifique.

    PhD thesis, INSA de Rennes (France), 2010.

  • [COMET] Jérôme Richard.

    Conception d’un modèle de composants logiciels avec ordonnancement de tâches pour les architectures parallèles multi-coeurs, application au code Gysela.

    PhD Thesis, Université de Lyon, December 2017.

  • [MLIR] C.

    Lattner et al., MLIR: Scaling Compiler Infrastructure for Domain Specific Computation, 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), Seoul, Korea (South), 2021, pp.

    2-14, doi: 10.1109/CGO51591.2021.9370308.
  • Principales activités

  • Study the state-of-the-art of parallel paradigms, parallel programming models, and composition models

  • Analyse challenges for incomming parallel applications

  • Propose a model that supports composition of of selected programming principles

  • Implement a proof of concept (POC)

  • Conduct evaluation of POC on selected use cases
  • Compétences

    Technical skills and level required :

  • knowledge of HPC programming models

  • knowledge in (meta-)modeling will be appreciated

  • very good programming skills (in particular in C++ but not only)
  • Avantages

  • Subsidized meals

  • Partial reimbursement of public transport costs

  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)

  • Possibility of teleworking (90 days / year) and flexible organization of working hours Social, cultural and sports events and activities

  • Access to vocational training

  • Social security coverage under conditions
  • Rémunération

    1st and 2nd year: 2100 euros gross salary /month
     
     3rd year: 2200 euros gross salary / month


    Required Skill Profession

    Computer Occupations



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