FPGA Accelerated Near-Storage Data Analytics

Computational storage [15, 16] is a promising technology to improve the efficiency and performance of various workloads, for example in scientific computing, machine learning (ML), and artificial intelligence (AI). As the volume of generated data continues to grow exponentially [5,9] conventional compute and storage architectures are increasingly constrained by large data movements between storage/memory and compute resources. Computational storage can eliminate many of these data movements by co-locating compute capabilities along with storage locations allowing to offload the typically much smaller (sub)programs [1, 12, 14]. While the concept is well established in literature, computational storage devices are not widely commercially available or deployed in data centers today. A key challenge is that computational storage encourages domain-specialization for highest efficiency while economic factors encourage commoditization of products catering to broad markets. A second challenge is that both for legacy applications as well as emerging applications such as ML/AI it remains an open research question how to program and orchestrate across distributed platforms with computational storage capabilities.

Recent advancements in programming models and software portability on the one hand, and reconfigurable hardware and domain-specific hardware design on the other [5, 7, 11], suggest that a modular approach that identifies common building blocks across domain boundaries might hold the key to both aforementioned challenges. Computational storage research exists on accelerating specific workloads or applications [5, 11, 14] as well as on emulating computational storage devices but a systematic study focusing on scientific computing workloads and modelling of suitable architectures and data distribution strategies is missing.

Research Objectives

This project aims to advance the research on computational storage for scientific computing and artificial intelligence applications. It will investigate mechanisms to formalize, capture, model and evaluate computational storage in distributed environments. The project is structured into three primary objectives:

• Objective A: realize a survey and define ontologies and taxonomies for distributed computational storage systems from multiple angles across multiple domains: the application perspective (programming paradigm, data flows), the middleware perspective (workflow orchestration, resource management and data placement), and the system perspective (e.g., hardware, storage, compute)
• Objective B: establish the technical foundations to empirically assess hardware performance and decompose existing workflows and task characteristics on the one hand, as well as implement suitable computational storage testbeds (e.g. simulated [2,3,4], emulated [7, 10], and on realistic software stacks and physical hardware [6, 8, 9, 11])
• Objective C: develop a methodology to faithfully model performance extrapolations to large-scale deployment scenarios as a means to identify and validate common computational storage building blocks

• An excellent academic record in computer science courses
• Knowledge on distributed systems and data management systems
• Strong programming skills (Python, C/C++)
• Familiarity with containerized environments (e.g., Docker, Podman, Apptainer, Kubernetes)
• Ability and motivation to conduct high-quality research, including publishing the results in relevant venues
• Very good communication skills in oral and written English
• Open-mindedness, strong integration skills and team spirit

Appreciated:

• Knowledge on machine learning and data analysis methods
• Professional experience in the areas of HPC and Big Data management

• 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 (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

The Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes., The thesis will be hosted by the KerData team at the Inria research center of Rennes. Rennes is the capital city of Britanny, 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 include collaborations with international partners from Germany, thus research visits to and from the collaborator’s teams are expected.

The KerData team in a nutshell for candidates

• KerData is a human-sized team currently comprising 5 permanent researchers, 2 engineers and 6 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: https://jlesc.github.io. Our team has active collaboration with high-profile academic institutions all around the world (including the USA, Spain, Germany or Japan) and with 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 that are critical in the process of becoming successful researchers.
• Check our website for more about the KerData team here: https://team.inria.fr/kerdata, To explore how computational storage can aid workloads in scientific computing and artificial intelligence, we will build upon previous work and active research of the members of the supervisory team in Germany and France.

For objective A, the research methodology centers on analysing real-world scientific computing use cases in close exchange with domain scientists to identify computational storage opportunities. This work will establish ontologies and taxonomies for distributed computational storage systems from multiple angles across multiple domains. This work will be complementary to collaborations with the German Climate Computing Center (DKRZ) and the Parallel Computing and I/O group at Otto von Guericke University Magdeburg. Several surveys have taken snapshots of the state of the art of computational storage [12, 14] and its precursors or related concepts (e.g., active storage, near-data processing, processing in memory). The outcome of this aim will be a state of the art survey focusing on the applicability for scientific computing in the HPC, to cloud and edge computing continuum.

Objective B is to establish suitable testbeds and modeling environments to study computational storage at scale. The work will build upon existing research of the KerData team to leverage system simulation to model large scale distributed systems [13] in addition to empirical platforms leveraging hardware emulation [7, 10], as well as realistic software stacks and physical hardware within Grid5000, Slices-FR or Chameleon Cloud and domain-specific experimental platforms together with collaborators from the different domain sciences. The targeted outcome of Objective B are proof-of-concept environments to run real-world computational workflows leveraging real operating system, middleware and device APIs.

Objective C is to develop a methodology to faithfully model performance extrapolation to large-scale deployment scenarios will evaluate and validate the impact achievable through common computational storage building blocks. The methodology aims to study, for example, task and data placement strategies based on the needs of real-world computational science use cases but extrapolated to system scales not deployed in state of the art data centers today. The outcome will be a methodological framework to support middleware and domain-specific computational storage device development.