Phd Position F/m Modelization of Hpc Jobs and Resources to Minimize Energy Waste

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Niveau de diplôme exigé**: Bac + 5 ou équivalent

**Fonction**: Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 24 research teams and 9 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area.

The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

**Contexte et atouts du poste**:
co-advised by Raphaël Bleuse and Eric Rutten (Ctrl-A), LIG/INRIA, and

Franck Corset (LJK, ASAR)

Within the framework of the Taranis project in the PEPR Cloud.

**Mission confiée**:
Soberness—in terms of electrical power—of Data Centers and High-Performance Computing (HPC) systems is becoming an important design issue, as the global energy consumption of Information Technologies is rising at consid
- tructures. This automatic management can be done by periodically monitoring the state of the system, and updating the configuration to activate relevant mechanisms.

This work takes root in the field of autonomic computing [6], and aims at designing efficient feedback loops to automatically manage the resources of a HPC (high performance computing) infrastructure. The use of feedback loops is widespread in various fields of engineering, but recent in the computer science field.

**Principales activités**:
**Resource harvesting**

The Resource and Job Management System (_RJMS_) is a key component to operate a HPC system [5, 7]. Users submit _jobs_: a description of their computation, data, and resource requirements. With respect to the resources status reported by the resource manager, the scheduler decides which resources to allocate to a job and assigns a time slot for the job’s execution.

The RJMS is however unable to fully exploit the resources in a HPC cluster: the presence of unused resources results from the limits of the scheduling of jobs. The allocation of resources to jobs, while respecting all constraints, leaves resources idle. Such inefficiency is sometimes referred as _fragmentation_. The loss of computing power resulting from the fragmentation represents an exploitable pool of resource.

CiGri [1] is a lightweight, scalable and fault-tolerant grid system that plugs into the RJMS. CiGri aims at minimizing the waste of computing resources to leverage the pool of unused resources. Yet, some computations (best-effort computations) can still lead to wasted resources if they are stopped before completion. In particular, by integrating information from the RJMS one could avoid to start best-effort computations if there is not enough time to execute them.

**Modeling jobs and resources**

In this work, we want to model the availability of the platform in order to insert the best-effort jobs in a frugal way. The design of models will have to consider that the allocation decisions are taken online, with partial information unveiling during the platform life-cycle.

**References**

[2] Dror G. Feitelson, Dan Tsafrir, and David Krakov. “Experience with using the Parallel Workloads Archive.” In: _J. Parallel Distributed Comput. _74.10 (Oct. 2014), pp. 2967-2982. doi: 10.1016/J.JPDC.2014.06.013.

[3] Sylvia Fruhwirth-Schnatter, Gilles Celeux, and Christian P Robert. _Handbook of mixture analysis_. CRC press, 2019.

[4] Ana Gainaru, Hongyang Sun, Guillaume Aupy, Yuankai Huo, Bennett A. Landman, and Padma Raghavan. “On-the-fly scheduling versus reservation-based scheduling for unpredictable workflows.” In: _Int. J. High Perform. Comput. Appl. _33.6 (2019). doi: 10.1177/1094342019841681.

[6] Jeffrey O. Kephart and David M. Chess. “The Vision of Autonomic Computing.” In: _IEEE Computer _36.1 (Jan. 2003), pp. 41-50. doi: 10.1109/MC.2003.1160055.

[7] Albert Reuther et al. “Scalable system scheduling for HPC and big data.” In: _Journal of Parallel and Distributed Computing _111 (Jan. 2018), pp. 76-92. doi: 10.1016/j.jpdc.2017.06.009.

[8] Christian P Robert et al. _The Bayesian choice: from decision-theoretic foundations to computational implemen
- tation_. Vol. 2. Springer.

**Compétences**:

- a MSc degree in Computer science or Statistics.
- skills in programming languages, software engineering
- Knowledge in the domains of HPC

**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