PhD Position F/M Modeling and Simulation of Exascale Storage Systems

TN France; Rennes; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Join a dynamic team at the forefront of research in exascale storage systems! This PhD position offers a unique opportunity to work on groundbreaking projects in high-performance computing and data management. Collaborate with leading researchers and contribute to innovative solutions that push the boundaries of technology. Enjoy a supportive environment that values work-life balance, with flexible hours and opportunities for teleworking. If you are passionate about computer science and eager to make a significant impact in the field, this is the perfect opportunity for you!

Prestations

Partial reimbursement of public transport costs

Possibility of teleworking (90 days/year)

Flexible hours

Partial insurance costs coverage

Qualifications

• Excellent Master degree in computer science or equivalent.
• Course in high-performance or distributed computing is advantageous.
• Good communication skills in English.

Responsabilités

• Model and simulate heterogeneous storage systems to study their behavior.
• Develop innovative algorithms for better resource utilization.
• Collaborate with international partners and conduct internships abroad.

Connaissances

C/C++

Python

High-performance computing

Distributed computing

Communication skills

Formation

Master's degree in Computer Science

Outils

WRENCH-based simulator

Robinhood

Description du poste

Social network you want to login/join with:

PhD Position F/M Modeling and Simulation of Exascale Storage Systems, Rennes

col-narrow-left

Client:

INRIA

Location:

Job Category:

Other

-

EU work permit required:

Yes

col-narrow-right

Job Reference:

1e70e68a4f25

Job Views:

1

Posted:

25.04.2025

Expiry Date:

09.06.2025

col-wide

Job Description:

Context

This thesis is placed in the context of NumPEx (a key national project whose goal is to co-design the software stack for the exascale era and prepare applications accordingly). This thesis will be co-supervised by Inria and CEA, respectively the Inria center at the University of Rennes and the CEA center at Bruyères-Le-Châtel, near Paris. Beyond the supervision, collaborations within NumPEx with the different partners of the consortium are to be expected.

Location and Mobility

The thesis, co-supervised by Inria and CEA, will be hosted by the KerData team at the Inria research center of Rennes and will include regular visits at the CEA Center of Bruyères-le-Châtel. Rennes is the capital city of Brittany, in the western part of France. It is easy to reach thanks to the high-speed train line to Paris. Rennes is a dynamic, lively city and a major center for higher education and research: 25% of its population are students.

This thesis will also include collaborations with international partners, especially from the US.

The KerData team in a nutshell for candidates

KerData is a human-sized team currently comprising 5 permanent researchers, 2 contract researchers, 1 engineer, and 5 PhD students. You will work in a caring environment, offering a good work-life balance.

KerData is leading multiple projects in top-level national and international collaborative environments such as within the Joint-Laboratory on Extreme-Scale Computing: Our team has active collaboration with high-profile academic institutions worldwide (including the USA, Spain, Germany, or Japan) and industry.

Our team strongly favors experimental research, validated by implementation and experimentation of software prototypes with real-world applications on real-world platforms, including some of the most powerful supercomputers worldwide.

The KerData team is committed to personalized advising and coaching, to help PhD candidates train and grow in all directions critical for becoming successful researchers.

Research Context

Modern scientific fields like radio-astronomy or weather forecasting require computing power beyond current machines. Supercomputers reaching exascale are necessary, but their efficient use presents new challenges, especially in data management.

Despite increasing power, HPC systems have seen a decline in I/O bandwidth, leading to congestion and variability. New storage levels are added to mitigate this, but their efficient utilization remains a challenge.

Thesis Proposal

This PhD aims to model and simulate heterogeneous storage systems to study their behavior, predict performance, and develop innovative algorithms for better resource utilization.

The initial focus will be on studying, modeling, and simulating storage systems like Lustre and DAOS using the existing WRENCH-based simulator, StorAlloc. The project will propose advanced resource allocation algorithms to overcome current limitations, validated through tools like Robinhood and outcomes from the IO-SEA European Project. Collaboration with international partners and opportunities for internships abroad will be integral to the work.

References

[1] GK. Lockwood et al., Storage 2020 report, 2017.

[2] O. Yildiz et al., IEEE IPDPS, 2016.

[3] F. Tessier et al., Reproducibility study, 2017.

[4] F. Tessier et al., IPDPSW 2020.

[5] J. Monniot et al., HeteroPar 2022.

[6] H. Casanova et al., Future Generation Computer Systems, 2020.

[7] [Details incomplete]

Candidate Profile

• Excellent Master degree in computer science or equivalent
• Course in high-performance or distributed computing is advantageous
• Programming skills in C/C++ and Python
• Good communication skills in English
• Open-mindedness, team spirit, and strong integration skills

Advantages

• Partial reimbursement of public transport costs
• Possibility of teleworking (90 days/year) and flexible hours
• Partial insurance costs coverage

Gross monthly salary: €2051 (years 1-2), €2158 (year 3)