2023Activity reportProject-TeamKERDATA

Dynamic provisioning of hybrid storage resources.

While for years high-performance computing (HPC) systems were the predominant means of meeting the requirements expressed by large-scale scientific workflows, today some components have moved away from supercomputers to cloud-type infrastructures. This migration has been mainly motivated by the cloud's ability to perform data analysis tasks efficiently.

From an I/O and storage perspective, this means having to deal with two very different worlds: the world of cloud computing, where direct access to resources is extremely limited due to a very high level of abstraction, and the world of on-premise supercomputers offering a low level approach requiring tight user control. The abstraction layer of clouds also allows storage, network and computing resources to have a certain elasticity and to be exclusively allocated.

In this context, we propose to converge these two worlds by exploring ways to provide storage resources distributed across hybrid HPC/cloud infrastructures to complex scientific workflows combining simulation and data analysis.

To do so, we continue our recently started work on scheduling algorithms dedicated to storage resources and implemented in a storage-aware scheduler developed in the team (simulator and scheduler). We also start a new research line focused on the abstraction of storage resources in order to provide a unified interface allowing to query any type of storage on an hybrid infrastructure.

I/O Orchestration over hybrid infrastructures.

On hybrid infrastructures, in the same way as the amount of generated data increases, the need for persistence has stepped up. A broad variety of large-scale scientific applications and workflows in scientific domains such as materials, high energy physics or engineering have massive I/O needs.

On a HPC system, for instance, it is typically estimated that around 10 % to 20 % of the wall time of this class of applications is spent in I/O. In addition, in the case of workflows running on hybrid infrastructures, these I/O are extremely varied and are no longer restricted to a single system but are spread across complex architectures.

To take advantage of the capability of current systems and hope to leverage future ones, improving the I/O performance is decisive. The complexity of both the federation and the different underlying systems implies having a strong knowledge of the workloads' I/O behavior and adopting a topology-aware approach for data movement orchestration.

We focus our effort on two research lines here. We first model the I/O behavior from the application and workflow's point of view. The parameters influencing I/O performance may be as diverse as the data size, the data model (multidimensional arrays, meshes, etc.), the data layout (array of structures, structure of arrays, etc.) or the access frequency.

The impact of each characteristic on I/O performance will be evaluated with benchmarks and real applications on the different systems of an hybrid infrastructure (HPC, cloud and edge later on) and an I/O workload model will be proposed.

Then, while using this I/O characterization, we focus our effort on data aggregation, taking into account the underlying topology, which consists of selecting a subset of intermediate resources to collect data before moving it from/to the destination (a storage system or a data processing system in case of in-transit workflows, for instance). This technique has several advantages: it increases the I/O bandwidth by reading or writing larger chunks of data, it highly reduces the number of concurrent streams to the destination and it minimizes the network contention.

Supporting repeatable, replicable and reproducible automatic deployments across the continuum.

As communities from an increasing number of scientific domains are leveraging the Computing Continuum, a desired feature of any experimental research is that its scientific claims are verifiable by others in order to build upon them. This can be achieved through repeatability, replicability, and reproducibility (3 Rs).

E2Clab is a first step towards enabling these goals and, to the best of our knowledge, it is the first platform to support the complete analysis cycle of an application on the Computing Continuum. We plan to further consolidate E2Clab in order to make it a promising platform for future performance optimization of applications on the Edge-to-Cloud Continuum through reproducible experiments.

Specifically, we plan to focus on three main directions: (1) develop new, finer grained abstractions to model the components of the entire data processing pipeline across the continuum (from data production to permanent storage) and allow researchers to trade between different costs with increased accuracy; (2) enable built-in support for other large-scale experimental testbeds, besides Grid'5000, such as Vagrant and Chameleon and ultimately provide a community driven tool for large scale experimentation; and (3) develop a benchmark for processing frameworks within the Computing Continuum atop E2Clab.

Many exciting research questions could then be explored leveraging such an enhanced deployment and optimization tool, especially in domains like machine and deep learning: how to improve the convergence speed of distributed algorithms (i.e., gradient descent) to reach good accuracy quickly? how to appropriately partition a model based on the capability of different cloud or edge devices?

Continual learning and inference in parallel across the Computing Continuum.

As neural network architectures and their training data are getting more and more complex, so are the infrastructures that are needed to execute them sufficiently fast. Hyperparameter setting and tuning, training, inference, dataset handling are operations that are all putting a growing pressure on the underlying compute infrastructure and call for novel approaches at all levels of the workflow, including the algorithmic level, the middleware and deployment level, and the resource optimization level.

Our goal is to address the following specific research questions: how can the various possible deployment options of complex AI workflows on the available underlying infrastructure impact performance metrics? how can this infrastructure be best leveraged in practice, potentially through seamless integration of supercomputers, clouds, and fog/edge systems?

We will focus on the middleware and the deployment level. Our objective is to investigate various deployment strategies for complex AI workflows (e.g., potentially combining continual training, simulations and inference, all in parallel and in real-time) on hybrid execution infrastructures (e.g., combining supercomputers and cloud/fog/edge systems).

Efficient federated learning in heterogeneous and volatile environments.

The latest technological advances in hardware accelerators like the GPUs enable the execution of machine and deep learning tasks on large volumes of data in a time that has become reasonable. Embedded systems make it possible to deploy some inference tasks as close as possible to the operational context. One of the major challenges of these heterogeneous distributed systems lies in the ability to have relevant data in a given place and at a given time.

One approach is to rely on the recent privacy-preserving Federated Learning paradigm that leverages the edge devices for training. However, such solutions raise some major challenges related to system and statistical heterogeneity, energy footprint and security.

Our goal is to identify and adapt such emerging approaches resulting from the Computing Continuum in order to respond to the problems of distribution of computations and processing, particularly in the case of workflows involving AI. This exploratory topic has concrete application use-cases such as with the smart autonomous vehicles or military and civilian warning systems.

Towards unified data processing techniques for hybrid simulation/analytics workflows executed across potentially hybrid CPU/GPU infrastructures.

In the high-performance computing area (HPC), the need to get fast and relevant insights from massive amounts of data generated by extreme-scale computations led to the emergence of in situ/in transit processing. In the Big Data area, the search for real-time, fast analysis was materialized through a different approach: stream-based processing. A major challenge is the joint use of these techniques in a unified data processing architecture.

Preliminary work already started within the "frameworks" work package of the HPC-Big Data Inria Challenge. It is also a core direction of our team's involvement in the ACROSS H2020 EuroHPC project. A typical scenario considered in ACROSS consists in executing hybrid workflows combining simulations and (potentially learning-based) analytics running concurrently.

The challenge is to integrate both stream and in-situ/in-transit processing tasks in the targeted workflows, leading to a decrease in execution times for data-intensive/deep learning like HPC simulations and modeling workloads. In particular, we will introduce programmatic support for on-demand data analytics on platforms that were traditionally used only for simulations. This new type of workflow (combining simulations with data analytics) could help anticipate the future behavior of the simulated systems.

Analyzing and exploiting stored data jointly with simulated data can provide a richer tool for much deeper interpretation of the targeted systems, enabling more reliable, transparent and innovative decision making. To this purpose, Damaris will be extended to support asynchronously Big Data analytics plugins, to enable in-situ and in transit analysis of simulation data, then to support hybrid (stream-based and batch-based) in transit data analysis. These new, hybrid workflows will allow on one hand to reduce the simulation time (by pre-analyzing some parts of the results locally, in-situ) and on the other hand to use simulations to train proxy models for optimization.

In the EUPEX EuroHPC Project, one goal is to introduce cross-application optimizations for data-driven stream parallel applications. This will rely on Damaris to orchestrate transfers, by leveraging various storage capabilities to provide scalable asynchronous I/O and non-intrusive in situ and in transit data processing on the data nodes. This provides another motivation to adapt Damaris to support workflows and Big Data analytics plugins by enabling in-situ and in-transit analysis of stream data.

Finally, as a piece of software considered for the Inria Exascale Software Task, in collaboration with the CEA, we plan to investigate new types of scenarios for hybrid CPU/GPU machines, where simulations could trigger on-demand analytics potentially run on GPU hardware.

Social impact.

One of our target applications is Early Earthquake Warning. We proposed a solution that enables earthquakes classification with an outstandingly perfect accuracy. By enabling accurate identification of strong earthquakes, it becomes possible to trigger adequate measures and save lifes. For this reason, our work was distinguished with an Outstanding Paper Award — Special Track for Social Impact at AAAI-20, an A* conference in the area of Artificial Intelligence. This result has been highlighted by the Le Monde journal in its edition of December 28, 2020, in a section entitled: Ces découvertes scientifiques que le Covid-19 a masquées en 2020. This collaborative work continued beyond 2020.

Environmental impact.

As presented in Section 4, we are partners with CybeleTech in the framework of the EUPEX EuroHPC project. CybeleTech is a French company specialized in precision agriculture. Within the framework of our collaboration, we propose to focus our efforts on a scale-oriented data management mechanism targeting two CybeleTech use-cases. They address irrigation scheduling for orchards and optimal harvest date for corn, and their models require the acquisition of large volumes of remote data. The overall goal is to improve the accuracy of plant growth models and improve decision making for precision agriculture, which directly aims to contribute to sustainable development.

7.1.1 Damaris

• Keywords:
  Visualization, I/O, HPC, Exascale, High performance computing
• Scientific Description:

  Damaris is a middleware for I/O and data management targeting large-scale, MPI-based HPC simulations. It initially proposed to dedicate cores for asynchronous I/O in multicore nodes of recent HPC platforms, with an emphasis on ease of integration in existing simulations, efficient resource usage (with the use of shared memory) and simplicity of extension through plug-ins.

  Over the years, Damaris has evolved into a more elaborate system, providing the possibility to use dedicated cores or dedicated nodes to in situ data processing and visualization. It proposes a seamless connection to the VisIt visualization framework to enable in situ visualization with minimum impact on run time. Damaris provides an extremely simple API and can be easily integrated into the existing large-scale simulations.

  Damaris was at the core of the PhD thesis of Matthieu Dorier, who received an Accessit to the Gilles Kahn Ph.D. Thesis Award of the SIF and the Academy of Science in 2015. Developed in the framework of our collaboration with the JLESC – Joint Laboratory for Extreme-Scale Computing, Damaris was the first software resulted from this joint lab validated in 2011 for integration to the Blue Waters supercomputer project. It scaled up to 16,000 cores on Oak Ridge’s leadership supercomputer Titan (first in the Top500 supercomputer list in 2013) before being validated on other top supercomputers. Active development is currently continuing within the KerData team at Inria, where it is at the center of several collaborations with industry as well as with national and international academic partners.

  In 2023, in the context of the ACROSS EuroHPC project, we added an interface for Damaris to enable asynchronous analytics, in particular to support Dask (www.dask.org), a Python-based library for scalable analytics. Dask offers a suite of useful distributed analytic methods using familiar Python-like interfaces, similar to NumPy and Pandas. Our proposed Python interface has enabled access to the suite of Python based visualization libraries and Damaris has been successfully tested with new options for in situ visualization.

  Damaris has been selected to be one of the key software pieces of software for the NumPEx PEPR project, which aims to provide the software infrastructure for the future Exascale machine to be hosted in France in 2025 (Jules Vernes project). The capabilities within Damaris will further studied in collaboration with CEA within the NumPEx exploratory PEPR project.

• Functional Description:
  Damaris is a middleware for data management and in-situ visualization targeting large-scale HPC simulations. Damaris enables: - In-situ data analysis by using selected dedicated cores/nodes of the simulation platform. - Asynchronous and fast data transfer from HPC simulations to Damaris. - Semantic-aware dataset processing through Damaris plug-ins, - Writing aggregated data (by hdf5 format) or visualizing them either by VisIt or ParaView.
• Release Contributions:
  v1.8 adds type introspection API and HDF5 select capability to reorder data as it is saved to HDF5 files. v1.9 adds extended support for unstructured mesh data types.
• URL:
  https://­project.­inria.­fr/­damaris/
• Contact:
  Gabriel Antoniu
• Participants:
  Gabriel Antoniu, Lokman Rahmani, Luc Bouge, Matthieu Dorier, Orçun Yildiz, Hadi Salimi, Joshua Bowden
• Partner:
  ENS Rennes

7.1.2 E2Clab

• Name:
  Edge-to-Cloud lab
• Keywords:
  Distributed systems, Cloud, Reproducibility, Experimentation, Computing Continuum, Evaluation, Large scale, Provenance
• Scientific Description:

  E2Clab is a framework that implements a rigorous methodology that provides guidelines to move from real-life application workflows to representative settings of the physical infrastructure underlying this application in order to accurately reproduce its relevant behaviors and therefore understand and optimize end-to-end performance.

  E2Clab allows a rigorous analysis of possible application configurations in a controlled testbed environment to understand their behavior and related performance trade-offs. E2Clab can be generalized to other applications in the Edge-to-Cloud Continuum. E2Clab is currently used by the Pl@ntNet team to understand and optimize the performance of the application. It is also used by our partners from Instituto Politécnico Nacional for automatic experiment deployments in the context of the SmartFastData associate team.

  In 2023, in an effort to enhance the reproducibility capabilities of E2Clab, we extended it to enable efficient provenance date capture across the Edge-to-Cloud Continuum. Specifically, we leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads for collecting such data on the IoT/Edge. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.

• Functional Description:
  E2Clab is a framework that implements a rigorous methodology that provides guidelines to move from real-life application workflows to representative settings of the physical infrastructure underlying this application in order to accurately reproduce its relevant behaviors and therefore understand end-to-end performance. Understanding end-to-end performance means rigorously mapping the scenario characteristics to the experimental environment, identifying and controlling the relevant configuration parameters of applications and system components, and defining the relevant performance metrics.
• Release Contributions:

  Changelog https://gitlab.inria.fr/E2Clab/e2clab/-/blob/master/CHANGELOG.md?ref_type=heads

  Features (release 1.0.0):

  (i) the configuration of the experimental environment, libraries and frameworks, (ii) the mapping between the application parts and machines on the Edge, Fog and Cloud, (iii) the deployment of the application on the infrastructure, (iv) Edge-to-Cloud network emulation, (v) the automated execution and monitoring, (vi) the application optimization, and (vii) the gathering of experiment metrics.

• URL:
  https://­gitlab.­inria.­fr/­E2Clab/­e2clab
• Publications:
  hal-02916032, hal-03310540, hal-03269852, hal-03332524, hal-03270129, hal-03338520, hal-03324177, hal-03259975, hal-03409405, hal-03510012
• Contact:
  Gabriel Antoniu
• Participants:
  Daniel Rosendo, Gabriel Antoniu, Alexandru Costan, Mathieu Simonin

7.1.3 StorAlloc

• Keywords:
  Simulation, HPC, Distributed Storage Systems
• Functional Description:

  StorAlloc is a simulator of a job scheduler dedicated to heterogeneous storage resources. It allows to model storage infrastructures, to simulate their partitioning and allocation, and to evaluate various scheduling algorithms.

  In practice, StorAlloc takes a storage request as input, which represents the presumed storage requirements of a job executed on a HPC system. It then offers to select some fitting storage resources, to be used by the client job. Storage resources are defined by the users, thanks to a YAML format with storage nodes and disks. Their selection happens by mean of an algorithm, also chosen by user (either from predefined algorithms, or user-developed). During simulation, various metrics are stored by StorAlloc all along the processing of storage requests, and eventually written to file when the simulation ends. Components of StorAlloc are independent and communicate through messages. They are easily extensible and new components may also be added.

• URL:
  https://­github.­com/­hephtaicie/­storalloc
• Contact:
  François Tessier
• Participants:
  Julien Monniot, François Tessier, Gabriel Antoniu

7.1.4 Fives

• Keywords:
  Simulation, HPC, Distributed Storage Systems
• Scientific Description:
  Development of Fives began in 2023, given the limitations of our previous StorAlloc simulator. At the end of 2023, Fives is still in active development, while its design and initial results are being submitted to a conference in the field.
• Functional Description:

  Fives is a storage resource scheduling simulator for supercomputers based on WRENCH and SimGrid, two state-of-the-art simulation frameworks. In particular, Fives can model a parallel file system such as Lustre, a computing partition, and simulate a set of jobs performing I/O on the resulting HPC system.

  Fives is based on several components. Firstly, as part of the development of this simulator, an abstraction called "Compound Storage Service" was proposed to represent a distributed storage system, and integrated into WRENCH. Within Fives, a job model was designed to represent a history of jobs and submit them to the scheduler present in WRENCH. Finally, a model of an existing supercomputer, Theta at Argonne National Laboratory, and a reverse-engineered version of its Lustre file system were developed in our simulator.

  Experiments are underway to calibrate and validate Fives.

• Contact:
  François Tessier

8.1.1 Provisioning storage resources for HPC and Cloud systems

Participants: François Tessier, Julien Monniot, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Henri Casanova (University of Manoa, HI, USA)

One of the recent axis we are developing in the context of high-performance data access concerns the provisioning of storage resources. The way these resources are accessed on supercomputers and clouds opposes a complex low-level vision that requires tight user control (on supercomputers) and a very abstract vision that implies uncertainty for performance modeling (on clouds). Nevertheless, taking full advantage of all available resources is critical in a context where storage is central for coupling workflow components. Our goal is then to make heterogeneous storage resources distributed across HPC+Cloud infrastructures allocatable and elastic to meet the needs of I/O-intensive hybrid workloads.

This is the context of Julien Monniot's thesis (thesis started in October 2021). He explores the techniques for scheduling storage resources on large-scale systems though a simulator of a job scheduler developed in KerData. The modeling of storage infrastructures and the evaluation of storage-aware scheduling algorithms are the main contributions of this work.

A first paper published in 2022 introduced StorAlloc, a DES-based simulator, and demonstrated how it can help to size a burst-buffer partition for a top-tier supercomputer  42. A research poster was also presented at SC'22 in Dallas  41 and was selected as a Best Research Poster finalist. An extended and significantly augmented version of this work were published in a journal of the field in 2023 13.

In 2023, the StorAlloc project evolved into Fives (Simulator for Scheduling on Storage Systems at Scale), a new simulator implemented with WRENCH  30, a state-of-the-art simulation framework. Fives not only reproduces the results of StorAlloc, but goes far beyond it. Thanks to this simulator, we were able to model a Lustre parallel file system (both hardware and software, which we reverse-engineered), as well as a supercomputer mimicking Theta, a 11 PFlops HPC system at Argonne National Laboratory. Using a job abstraction layer we devised, Fives enabled us to simulate several weeks of job execution on Theta from an I/O point of view (Darshan traces). The aim of this simulation was to calibrate our simulator with a view to subsequently predicting the Lustre's I/O performance on any other dataset. This work is currently being submitted to an A-rank conference in the field.

8.1.2 Efficient workflow provenance capture on the Edge-to-Cloud Continuum

Participants: Daniel Rosendo, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Marta Mattoso, Federal University of Rio de Janeiro.

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.

To address this challenge, based on a performance analysis of existing systems, we propose ProvLight 19, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.

We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight and E2Clab are available as open-source tools.

8.1.3 Data-centric workflow composition in the Computing Continuum

Participants: Silvina Caino-Lores, Alexandru Costan, Gabriel Antoniu.

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications.

As part of our leadership roles in the Workflows Community Initiative and workflow-specific venues like the WORKS workshop, we have identified the need to support data streams from the edge-to-cloud-to-HPC; orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities 35; scale workflows to exascale computing systems and clouds; and modernize existing workflows to modern workflow management systems, amongst others 14. We are investigating how to model workflow data in a way that allows the interoperability of existing programming models across the computing continuum, with the goal of defining a common data exchange layer serving workflow management building blocks (e.g., task scheduling, task control flow, data staging, provenance services). Specifically, we are exploring how the E2Clab framework can be expanded to support this vision due to its service-oriented nature. We are currently studying relevant application scenarios provided by the SKAO in the context of the ExaDoST project as a specific example of a large scale application in the computing continuum with mixed data and processing requirements.

8.2.1 Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments

Participants: Daniel Rosendo, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Kate Keahey, University of Chicago / Argonne National Laboratory, who supervised the internship of Daniel Rosendo.

Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex scientific workflows to be executed across hybrid systems spanning from IoT Edge devices to Clouds, and sometimes to supercomputers (the Computing Continuum). Understanding the performance trade-offs of large-scale workflows deployed on such complex Edge-to-Cloud Continuum is challenging. To achieve this, one needs to systematically perform experiments, to enable their reproducibility and allow other researchers to replicate the study and the obtained conclusions on different infrastructures. This breaks down to the tedious process of reconciling the numerous experimental requirements and constraints with low-level infrastructure design choices.

To address the limitations of the main state-of-the-art approaches for distributed, collaborative experimentation, such as Google Colab, Kaggle, and Code Ocean, we propose KheOps18, a collaborative environment specifically designed to enable cost-effective reproducibility and replicability of Edge-to-Cloud experiments. KheOps is composed of three core elements: (1) an experiment repository; (2) a notebook environment; and (3) a multi-platform experiment methodology.

We illustrate KheOps with a real-life Edge-to-Cloud application. The evaluations explore the point of view of the authors of an experiment described in an article (who aim to make their experiments reproducible) and the perspective of their readers (who aim to replicate the experiment). The results show how KheOps helps authors to systematically perform repeatable and reproducible experiments on the Grid5000 + FIT IoT LAB testbeds. Furthermore, KheOps helps readers to cost-effectively replicate authors experiments in different infrastructures such as Chameleon Cloud + CHI@Edge testbeds, and obtain the same conclusions with high accuracies (> 88% for all performance metrics).

8.2.2 Formalisation of workflow provenance for trustworthy and explainable AI

Participants: Silvina Caino-Lores, Alexandru Costan.

• Collaboration.
  This work is part of an ongoing collaboration with Renan Souza (Oak Ridge National Laboratory, USA), Rafael Ferreira da Silva (Oak Ridge National Laboratory, USA), Ana Trisovic (Massachusetts Institute of Technology, USA) and Neil Thompson (Massachusetts Institute of Technology, USA).

Artificial Intelligence (AI) is driving scientific discovery and economic growth in all kinds of application domains while impacting from routine daily tasks to societal-level challenges. However, research communities, industry players and social actors are expressing increasing concern about the potential ethical and practical implications of the pervasive presence of AI. Of particular concern are the explainability of AI, or making AI's decision-making process understandable, and transparency of AI, ensuring clarity in AI's design, data and operation. Working towards advancing explainability and transparency of AI is currently a priority, essential for responsible and trustworthy AI applications.

Existing approaches to attain a degree of explainability of AI outputs mainly focus on analyzing the features in the data that lead to a given output or studying the layers and changes in neurons of the network throughout training. We are currently exploring a different approach by investigating the associations between neural network (NN) provenance metadata, model behavior, and outcome. Specifically, we seek to understand the correlation and causation between various architectural and algorithmic characteristics of the NN and their performance. Additionally, we aim to identify the most relevant architectural features serving as the highest predictors for NN behavior and accuracy. Our goal is to investigate to what extent we can leverage these characteristics and relationships as a proxy to improve transparency and explainability of NN models.

To achieve this, we rely heavily on the understanding of provenance metadata captured at runtime during the execution of AI workflows. Provenance refers to the origin or history of data and models, capturing the lineage of their creation, modification, and usage. It provides a detailed record of data sources, processing steps, and model configurations, ensuring transparency and traceability throughout the AI lifecycle. A challenging aspect of working with AI workflows is that today there are no comprehensive formalisms able to capture the complexity and relationships in workflow and model provenance data. Our ongoing work towards the definition of ontologies and taxonomies for AI workflow provenance data aims to fill this gap and serve as a theoretical foundation for developing tailored provenance data management systems tailored for the different stakeholders involved in AI applications.

8.2.3 Selective parameter aggregation for poisoning attack mitigation in federated learning

Participants: Cédric Prigent, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Loïc Cudennec (DGA), who is co-advising the PhD thesis of Cédric Prigent, and with DFKI in the context of the ENGAGE Inria-DFKI project.

Federated Learning (FL) is a distributed Machine Learning paradigm aiming to collaboratively learn a shared model while considering privacy preservation. Clients do the training process locally with their private data while a central server updates the global model by aggregating local models.

Minimizing the attack surface of FL systems is a field of active research. FL turns out to be highly vulnerable to various threats coming from the edge of the network, especially in highly distributed and volatile environment. Current approaches rely on robust aggregation, anomaly detection and generative models for defending against poisoning attacks. Yet, they either have limited defensive capabilities due to their underlying design or are impractical to use as they rely on constraining building blocks.

In 16, we introduce FedGuard, a novel FL framework that utilizes the generative capabilities of Conditional Variational AutoEncoders (CVAE) to effectively defend against poisoning attacks with tuneable overhead in communication and computation. Whilst the idea of hardening a FL system using generative models is not entirely new, FedGuard's original contribution is in its selective parameter aggregation operator with parameter selection being driven by synthetic validation data sampled from the CVAEs trained locally by each participating party. Experimental evaluations in a 100-client setup demonstrates FedGuard to be more effective than previous approaches against several types of attacks (label and sign flipping, additive noise, same value attacks). FedGuard successfully defends in scenarios with up to 50% malicious peers where other strategies fail. In addition, FedGuard does not require auxiliary datasets or centralized (pre-) training. It provides resilience against poisoning attacks from the very first round of federated training.

8.2.4 Efficient Data-Parallel Continual Learning with Asynchronous Distributed Rehearsal Buffers

Participants: Thomas Bouvier, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Bogdan Nicolae (Argonne National Laboratory- ANL, USA), who co-advised the internship of Thomas Bouvier at ANL and serves as a technical advisor for his PhD work.

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input mini-batches (groups of training samples fed to the model) using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably dark experience replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. We run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

8.2.5 Local data compression for resource-constrained clustered federated learning in the Edge-to-Cloud Continuum

Participants: Cédric Prigent, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Loïc Cudennec (DGA), and with DFKI in the context of the ENGAGE Inria-DFKI project.

Although Federated Learning (FL) can be a very effective tool for large scale collaborative training of Machine Learning (ML) models, there exist some fundamental problems which reduce the efficacy of this framework, primarily the problem of clients having vastly varying data distributions. As a result, training a single global model that satisfies data distributions of the entire federation is usually unfeasible.

Clustered FL has been proposed to tackle this problem by grouping clients with similar data distributions and train personalized models in each identified cluster. While providing an efficient framework for training ML models in heterogeneous networks, existing approaches mainly focus on the accuracy of the clustering mechanism, overlooking system constraints. This leads to sustainability problems for resource-constrained devices as encountered in a Edge-to-Cloud Continuum.

We introduce a new method for clustered FL involving privacy-secured low dimensional data projections of user data to derive optimal clusters for personalized FL. We conduct a preliminary study to demonstrate the robustness of our approach against data reconstruction attacks. Afterwards, we conduct extensive experiments on Grid'5000 to evaluate the performance of the proposed approach in various scenarios introducing concept drift and label shift. Our approach achieves the best clustering performance in 3 scenarios, and second best clustering performance in a last scenario while competing with state-of-the-art clustering strategies. Computation and communication overhead analysis shows that with the help of a centralized AutoEncoder pre-training policy, our clustering approach introduces negligible system overhead compared to standard FL. In comparison, IFCA the second best performing clustering approach induces up to 100% communication overhead and 87% training time overhead. This work is currently in submission.

8.2.6 Comparative Analysis of Federated Learning: Simulations Versus Real-World Testbeds

Participants: Mathis Valli, Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Loïc Cudennec (DGA). Mathis Valli PhD is co-supervised by Cédric Tedeschi (Myriads team).

Federated Learning (FL), while a breakthrough in decentralized machine learning, is predominantly validated through simulations. These simulations often fail to capture the complexities and unpredictability of real-world environments. They usually overlook factors like network variability, device heterogeneity, and real-time operational challenges, leading to a gap between theoretical efficiency and practical applicability.

Addressing these limitations, our research shifts focus from simulations to real testbed evaluations. We aim to deploy and assess FL models in environments that mimic more realalistic conditions, considering network instability, varied data distributions, and device capabilities. By comparing these real-testbed outcomes with simulation results, our study intends to highlight the disparities and refine the practical applicability of FL models, bridging the gap between theory and practice.

We deploy FL models across a heterogeneous mix of nodes within Grid'5000, managing and monitoring their performance using e2clab's orchestration tools. Key performance metrics such as model accuracy, training time, network usage, and energy efficiency are measured to evaluate the effectiveness of FL in a real-world environment. We aim to conduct tests to assess the gap between simulated environnements and real deployment.

While our current research emphasizes bridging the theoretical and practical aspects of Federated Learning through real-world testbed evaluations, a key future direction is to investigate the resilience and adaptability of FL models in dynamic environments. This involves understanding how these models react and adjust to changing conditions such as fluctuating network stability, varied data distributions, and shifting device capabilities.

8.3.1 Scalable asynchronous I/O and in-situ processing with Damaris for carbon sequestration

Participants: Joshua Bowden, Alexandru Costan, François Tessier, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Atgeirr Rasmussen (SINTEF) and his team within the framework of the EuroHPC H2020 ACROSS project.

Carbon capture and storage (CCS) is one of the technologies that have a large potential for mitigating CO2 emissions, and can also enable carbon-negative processes. Before one can commit to large-scale carbon storage operations, it is essential to do simulation studies to assess the storage potential and safety of the operation and to optimize the placement and operation of injection wells. Such a simulation is done by computer programs that solve the equations that describe the motion and state of the fluids within the porous rocks. In the ACROSS project, we use OPM Flow, an open-source reservoir simulator program suitable for both industrial uses as well as research.

As large-scale simulations can take a long time to run, and require significant high-performance computing resources, we investigate how asynchronous I/O and in situ processing can help improve the performance, scaling, and efficiency of OPM Flow and of workflows using the program. The ACROSS project is using a co-development method, where software requirements inform the hardware design choice for next generation HPC systems. The OPM Flow software is typical of MPI based simulation software where I/O inhibits the scaling of the simulation to larger machine sizes due to its serialized nature. We started to investigate how the Damaris approach could be leveraged by OPM Flow to provide asynchronous I/O.

We proposed an interface for Damaris to enable asynchronous analytics, in particular to support Dask (www.dask.org), a Python-based library for scalable analytics. Dask offers a suite of useful distributed analytic methods using familiar Python-like interfaces, similar to NumPy and Pandas. Our proposed Python interface has enabled access to the suite of Python based visualization libraries and Damaris has been successfully tested with new options for in situ visualization.

The EuroHPC ACROSS project has supported this work and the results are benefiting the OPM Flow simulation software which integrates Damaris in a public release. Workflows that use the Python and Dask analytics capabilities have been developed and include a demonstration of multi-simulation sensitivity analysis of full simulation field data, and an on-line training of neural network based auto-encoders for reduced order model development. These results have been described in ACROSS deliverable D7.3 and D7.6. The capabilities within Damaris are to be further studied in collaboration with CEA within the future NumPEx exploratory PEPR project.

8.3.2 Using application grouping to improve I/O scheduling

Participants: Guillaume Pallez.

• Collaboration.
  This work has been carried out in the framework of the EuroHPC H2020 Admire project. It started while Guillaume was in the Tadaam team and has been carried out since Guillaume has joined KerData (but is still located in Tadaam).

Previous work has shown that, when multiple applications perform I/O phases at the same time, it is best to grant exclusive access to one of them at a time, which limits interference. That strategy is especially well adapted for a situation where applications have similar periods (they perform I/O phases with a similar frequency). However, when that is not the case, applications with shorter I/O phases present a higher stretch. We have been investigating a strategy where applications are grouped according to their I/O frequency. The idea is that applications from the same group should be executed one at a time, while different groups should share the available bandwidth. We are also working to determine a good priority-assigning policy.

In this topic we have made several contribution:

• In 11, we present the potential of grouping strategies through a scheduling heuristic called SET-10, which is simple and requires only minimal information. Our extensive experimental campaign shows the importance of IO-Sets and the robustness of SET-10 under various workloads. We also provide insights on using our proposal in practice.
• Then we have provided FTIO 27, a tool for characterizing the temporal I/O behavior of an application using frequency techniques such as DFT and autocorrelation. FTIO imposes generate only a modest amount of information and hence imposes minimal overhead. We also proposed metrics that quantify the confidence in the obtained results and further characterize the I/O behavior based on the identified period. The results can be directly included in the algorithmic strategy proposed in 11.

8.3.3 Scheduling distributed I/O resources in HPC systems

Participants: Guillaume Pallez.

Parallel file systems cut files into fixed-size stripes and distribute them across a number of storage targets (OSTs) for parallel access. Moreover, a layer of I/O nodes is often placed between compute nodes and the PFS. In this context, it is important to notice both OST and I/O nodes are potentially shared by running applications, which may lead to contention and low I/O performance.

Contention-mitigation approaches usually see the shared I/O infrastructure as a single resource capable of a certain bandwidth, whereas in practice it is a distributed set of resources from which each application can use a subset. In addition, using X% of the OSTs, for example, does not grant a job X% of the PFS’ peak performance. Indeed, depending on their characteristics, each application will be impacted differently by the number of used I/O resources.

We conducted a comprehensive study of the problem of scheduling shared I/O resources — I/O nodes, OSTs, etc — to HPC applications. We tackled this problem by proposing heuristics to answer two questions: 1) how many resources should we give each application (allocation heuristics), and 2) which resources should be given to each application (placement heuristics). These questions are not independent, as using more resources often means sharing them. Nonetheless, our two-step approach allows for simpler heuristics that would be usable in practice.

In addition to overhead, an important aspect that impacts how “implementable” algorithms are is their input regarding applications’ characteristics, since this information is often not available or at least imprecise. Therefore, we proposed heuristics that use different input and studied their robustness to inaccurate information.

8.3.4 Enabling Agile Analysis of I/O Performance Data with PyDarshan

Participants: Jakob Luettgau.

Modern scientific applications utilize numerous software and hardware layers to efficiently access data. This approach poses a challenge for I/O optimization because of the need to instrument and correlate information across those layers. The Darshan I/O characterization tool seeks to address this challenge by providing efficient, transparent, and compact runtime instrumentation of many common I/O interfaces. It also includes command-line tools to generate actionable insights and summary reports. However, the extreme diversity of today’s scientific applications means that not all applications are well served by one-size-fits-all analysis tools. We have designed and implemented PyDarshan, a Python-based library that enables agile analysis of I/O performance data. PyDarshan caters to both novice and advanced users by offering ready-to-use HTML reports as well as a rich collection of APIs to facilitate custom analyses. In 17 we present the design of PyDarshan and demonstrate its effectiveness in four diverse real-world analysis use cases.

8.3.5 Analyzing Qualitatively Optimization Objectives in the Design of HPC Resource Manager

Participants: Guillaume Pallez, Robin Boezennec.

A correct evaluation of scheduling algorithms and a good understanding of their optimization criterias are key components of resource management in HPC. In 15, 25 we discuss bias and limitations of the most frequent optimization metrics from the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling. We experimentally demonstrate these limitations by focusing on two use-cases: a study on the impact of runtime estimates on scheduling performance, and the reproduction of a recent highimpact work that designed an HPC batch scheduler based on a network trained with reinforcement learning. We demonstrate that focusing on quantitative optimization criterion ("our work improve the literature by X%") may hide extremely important caveat, to the point that the results obtained are opposed to the actual goals of the authors. Key findings show that mean bounded slowdown and mean response time are irrelevant objectives in the context of HPC. Despite some limitations, mean utilization appears to be a good objective. We propose to complement it with its standard deviation in some pathologic cases. Finally, we argue for a larger use of area-weighted response time, that we find to be a very relevant objective.

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

9.2.1 Visits of international scientists

9.2.2 Visits to international teams

In September 2023, Julien Monniot, François Tessier and Gabriel Antoniu visited the University of Manoa, HI, USA, to collaborate with Henri Casanova on simulation of distributed storage systems. The outcome of this visit and the following work is going to be submitted in a rank-A conference.

9.3.1 H2020 projects

9.3.2 Collaborations with Major European Organizations

Participants: Gabriel Antoniu, Alexandru Costan.

Appointments by Inria in relation to European bodies

• Big Data Value Association (BDVA) and ETP4HPC:
  Since 2018, Gabriel Antoniu and Alexandru Costan have been serving as Inria representatives in the working groups dedicated to HPC-Big Data convergence.

Community service at European level in response to external invitations

• ETP4HPC:
  Since 2019: Gabriel Antoniu has served as a co-leader of the working group on Programming Environments and co-leader of two research clusters, contributing to two successive versions of the Strategic Research Agenda of ETP4HPC, the latest one being published in 2022 14. Alexandru Costan served as a member of these working groups.
• Transcontinuum Initiative (TCI):
  As a follow-up action to the publication of its Strategic Research Agenda, ETP4HPC initiated a collaborative initiative called TCI (Transcontinuum Initiative). It gathers major European associations in the areas of HPC, Big Data, AI, 5G, Cybersecurity, including ETP4HPC, BDVA, CLAIRE, HIPEAC, 5G IA, ECO). It aims to strengthen research and industry in Europe to support the Digital Continuum - infrastructure (including HPC systems, clouds, edge infrastructures) by helping to define a set of research focus areas/topics requiring interdisciplinary action. The expected outcome of this effort is the co-editing of multidisciplinary calls for projects to be funded by the European Commission. Gabriel Antoniu is in charge of ensuring the BDVA-ETP4HPC coordination and of co-animating the working group dedicated to the definition of representative application use cases.
• Big Data Value Association:
  Gabriel Antoniu was asked by BDVA to coordinate Diva's contribution to the TCI initiative recently started (see above).

Exa-DoST

Participants: Gabriel Antoniu, François Tessier, Julien Monniot, Joshua Bowden, Silvina Caino Lores, Guilaume Pallez.

Exa-DoST project of the NumPEx PEPR program

• Title:
  Data-oriented Software and Tools for the Exascale
• Duration:
  From January 1, 2023 to April 1, 2030
• Partners:
  • Inria
  • CEA
  • CNRS
  • University of Bordeaux
  • Observatoire de Paris
  • Observatoire de la Côte d'Azure
  • Data Direct Networks France (DDN)
• Coordinator:
  Gabriel Antoniu (KerData Team, Inria)
• Summary:

  The advent of future Exascale supercomputers raises multiple data-related challenges. To enable applications to fully leverage the upcoming infrastructures, a major challenge concerns the scalability of techniques used for data storage, transfer, processing and analytics. Additional key challenges emerge from the need to adequately exploit emerging technologies for storage and processing, leading to new, more complex storage hierarchies. Finally, it now becomes necessary to support more and more complex hybrid workflows involving at the same time simulation, analytics and learning, running at extreme scales across supercomputers interconnected to clouds and edgebased systems. The Exa-DoST project will address most of these challenges, organized in 3 areas:

  • Scalable storage and I/O;
  • Scalable in situ processing;
  • Scalable smart analytics.

  As part of the NumPEx program, Exa-DoST targets a much higher technology readiness level than previous national projects concerning the HPC software stack. It will address the major data challenges by proposing operational solutions co-designed and validated in French and European applications. This will allow filling the gap left by previous international projects to ensure that French and European needs are taken into account in the roadmaps for building the data-oriented Exascale software stack.

STEEL

Participants: Gabriel Antoniu, Alexandru Costan, Jakob Luettgau, François Tessier, Mathis Valli.

• Title:
  Secure and efficient daTa storagE and procEssing on cLoud-based infrastructures
• Duration:
  From June 1, 2023 to 31 August 2030
• Partners:
  • Inria
  • CNRS
  • Institut Mines Télécom (IMT)
  • University of Bordeaux
  • University of Rennes
  • INSA Rennes
  • INSA Lyon
• Coordinator:
  Gabriel Antoniu (KerData Team, Inria)
• Summary:
  The strong development of cloud computing since its emergence in 2007 and its massive adoption for the storage of unprecedented volumes of data in a growing number of domains has brought to light major technological challenges. In this project we will address several of these challenges, organized in three research directions. The first direction concerns the exploitation of emerging technologies for efficient storage on cloud infrastructures. We will address this challenge through NVRAM-based distributed performance storage solutions, as close as possible to data production and consumption locations (disaggregation principle) and develop strategies to optimize the trade-off between data consistency and access performance. The second direction concerns the efficient storage and processing of data on hybrid, heterogeneous infrastructures within the digital edge-cloud-supercomputer continuum. In many domains (autonomous cars, predictive maintenance, intelligent buildings, etc.) we are witnessing the emergence of hybrid workflows combining simulations, analysis of sensor data flows and machine learning. Their execution requires storage resources ranging from the edge to cloud infrastructures, and even to supercomputers, which poses challenges for unified data storage and processing. The third research direction is dedicated to confidential storage, in connection with the need to store and analyze large volumes of data of strategic interest or of a personal nature. For all of these directions, the project will take into account the need to propose and validate interoperable approaches with a potential for transfer to major French or European industrial players in cloud computing.

Grid'5000

We are members of Grid'5000 community and run experiments on the Grid'5000 platform on a daily basis.

Inria Exploratory program: Repas

Participants: Guillaume Pallez.

• Project Acronym:
  REPAS
• Title:
  New Portrayal of HPC Applications
• Coordinator:
  Guillaume Pallez
• Collaboration:
  This is done in collaboration with the team DATAMOVE (Inria Grenoble)
• Duration:
  2022-2025

What is the right way to represent an application in order to run it on a highly parallel (typically exascale) machine? The idea of project is to completely review the models used in the development scheduling algorithms and software solutions to take into account the real needs of new users of HPC platforms.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• Gabriel Antoniu
  :

  • Keynote Speaker at the ISPDC 2023 conference in Bucharest, Romania.
• François Tessier:
  • Talk at the Per3S workshop on High-Performance Storage in Paris, France.
• Silvina Caino-Lores:
  • Panel "Runtimes and Workflow Systems for Extreme Heterogeneity: Challenges and Opportunities" at SC 23 (Denver, CO, USA).

10.1.5 Leadership within the scientific community

• Gabriel Antoniu:
  • Large-wingspan National project management:
    Coordinator of ExaDoST, one of the 5 targeted projects of the NumPEx PEPR project (started in 2023, budget: 6.2 M€). Coordinator of STEEL, one of the 7 high-priority projects of the CLOUD PEPR project (started in 2023, budget: 2.8 M€).
  • ETP4HPC:
    Since 2019, co-leader of the working group on Programming Environments, lead co-author of the corresponding chapter of the Strategic Research Agenda of ETP4HPC (latest edition published in 2022, next edition to be published in 2024).
  • TCI:
    Since 2020, co-leader of the Use-Case Analysis Working Group. TCI (The Transcontinuum Initiative) emerged as a collaborative initiative of ETP4HPC, BDVA, CLAIRE and other peer organizations, aiming to identify joint research challenges for leveraging the HPC-Cloud-Edge computing continuum and make recommendations to the European Commission about topics to be funded in upcoming calls for projects.
  • International lab management:
    Vice Executive Director of JLESC for Inria. JLESC is the Joint Inria-Illinois-ANL-BSC-JSC-RIKEN/AICS Laboratory for Extreme-Scale Computing. Within JLESC, he also serves as a Topic Leader for Data storage, I/O and in situ processing for Inria.
  • Bilateral Inria-DFKI project management:
    French coordinator of the ENGAGE project (2022-2024).
  • Team management:
    Head of the KerData Project-Team (INRIA-ENS Rennes-INSA Rennes).
  • International Associate Team management:
    Leader of the UNIFY Associate Team with Argonne National Lab (2019–2022).
• Alexandru Costan:
  • Work package co-leader with René Schubotz(DFKI) within the ENGAGE Inria-DFKI project.
  • Work package leader witin the PEPR CLOUD STEEL project.
• François Tessier:
  • Work package co-leader with Francieli Zanon-Boito (Associate Professor, University of Bordeaux) within the NumPEX ExaDoST project.

10.2.1 Teaching

• Alexandru Costan:
  • Bachelor: Software Engineering and Java Programming, 28 hours (lab sessions), L3, INSA Rennes.
  • Bachelor: Databases, 68 hours (lectures and lab sessions), L2, INSA Rennes.
  • Bachelor: Practical case studies, 24 hours (project), L3, INSA Rennes.
  • Master: Big Data Storage and Processing, 28h hours (lectures, lab sessions), M1, INSA Rennes.
  • Master: Algorithms for Big Data, 28 hours (lectures, lab sessions), M2, INSA Rennes.
  • Master: Big Data Project, 28 hours (project), M2, INSA Rennes.
• Gabriel Antoniu:
  • Master (Engineering Degree, 5th year): NoSQL and Cloud technologies, 20 hours (lectures), M2 level, ENSAI (École nationale supérieure de la statistique et de l'analyse de l'information), Bruz.

  • Master: Scalable Distributed Systems, 10 hours (lectures), M1 level, SDS Module, EIT ICT Labs Master School.

    M2 level, IBD Module, SIF Master Program, University of Rennes.

  • Master: Cloud Computing and Big Data, 14 hours (lectures), M2 level, Cloud Module, MIAGE Master Program, University of Rennes.
• François Tessier:
  • Bachelor: Computer science discovery, 15 hours (lab sessions), L1 level, DIE Module, ISTIC, University of Rennes.
  • Master: Cloud Computing and Big Data, 15 hours (lectures), M2 level, Cloud Module, MIAGE Master Program, University of Rennes.
  • Master (Engineering Degree, 4th year): Storage on Clouds, 5 hours (lecture and lab session), M2 level, IMT Atlantique, Rennes.
• Silvina Caino-Lores:
  • Master: Processing Artificial Intelligence and Machine Learning Workloads at Scale, 9 hours (lectures), M2 level, Big Data Storage and Processing Infrastructures Module, Cloud and Network Infrastructures Master Program, EIT Digital School, ISTIC, University of Rennes.
• Thomas Bouvier:
  • Master: Stream Processing, 12 hours (lectures and lab sessions), M2 level, INSA Rennes.
  • Master: Database optimizations, 30 hours (lab sessions), M1 level, ISTIC, University of Rennes.
• Cédric Prigent:
  • Master: Cloud Computing and Big Data, 36 hours (lab sessions), M2 level, Cloud Module, MIAGE Master Program, University of Rennes.

10.2.2 Supervision

• PhD:
  • Daniel Rosendo Prigent, "Methodologies for Reproducible Analysis of Workflows on the Edge-to-Cloud Continuum", thesis defended in June 2023, co-advised by Gabriel Antoniu, Alexandru Costan and Patrick Valduriez.
• PhD in progress:
  • Thomas Bouvier, "Supporting Continual Learning across the Computing Continuum", thesis started in January 2021, co-advised by Alexandru Costan and Gabriel Antoniu.
  • Julien Monniot, "Dynamic provisioning of intermediate storage resources across hybrid HPC/Cloud infrastructures", thesis started in November 2021, co-advised by François Tessier and Gabriel Antoniu.
  • Cédric Prigent, "Supporting Online Learning and Inference in Parallel across the Digital Continuum", thesis started in November 2021, co-advised by Alexandru Costan, Gabriel Antoniu and Loïc Cudennec (DGA).
  • Mathis Valli, "Comparative Analysis of Federated Learning: Simulations Versus Real-World Testbeds in dynamic settings", thesis started in April 2023, co-advised by Alexandru Costan, Cédric Tedeschi (Myriads) and Loïc Cudennec (DGA).
  • Robin Boezennec, "Towards a New Representation of HPC Workloads", thesis started in 2022, co-advised by Guillaume Pallez and Fanny Dufossé (DataMove).
• Internships:
  • Malvin Chevallier, "Study of regularization techniques applied to rehearsal-based continual learning", two-and-a-half-month internship started in June 2023, co-advised by Thomas Bouvier and Alexandru Costan.

10.2.3 Juries

• Alexandru Costan:
  • Reviewer for the PhD thesis of Quentin Guilloteau (Université Grenoble Alpes)
  • GDR RSD - Member of the juries for the PhD award and Young Researcher award.

10.3.1 Internal or external Inria responsibilities

• François Tessier:
  • Member of the Commission on Health, Safety and Working Conditions (now called FSS) within the Inria center of Rennes
  • Member of the COURSE committee in charge of gathering user needs for the future SLICES platform
• Alexandru Costan:
  • In charge of internships at the Computer Science Department of INSA Rennes.
  • In charge of the management of the KerData team access to Grid'5000. for the future SLICES platform
• Gabriel Antoniu:
  Inria representative in the BDVA working group dedicated to HPC-Big Data convergence.
• Guillaume Pallez:
  Member of the Inria Evaluation Committee. As part of this role:

  • Copywriter of the “Recommendations on "Grey-Zone Publishers from the Inria Evaluation Committee” 23, 24
  • Bilan de la mandature 2019-2023 de la Commission d'Évaluation Inria 26

International journals

• 11 articleF.Francieli Boito, G.Guillaume Pallez, L.Luan Teylo and N.Nicolas Vidal. IO-SETS: Simple and efficient approaches for I/O bandwidth management.IEEE Transactions on Parallel and Distributed Systems3410August 2023, 2783 - 2796HALDOIback to textback to text
• 12 articleE.Emmanuel Jeannot, G.Guillaume Pallez and N.Nicolas Vidal. IO-aware Job-Scheduling: Exploiting the Impacts of Workload Characterizations to select the Mapping Strategy.International Journal of High Performance Computing Applications2023, 1-13HALDOI
• 13 articleJ.Julien Monniot, F.François Tessier, M.Matthieu Robert and G.Gabriel Antoniu. Supporting Dynamic Allocation of Heterogeneous Storage Resources on HPC Systems.Concurrency and Computation: Practice and Experience3528August 2023, 1-16HALDOIback to text

International peer-reviewed conferences

• 14 inproceedingsJ.Jonathan Bader, J.James Belak, M.Matthew Bement, M.Matthew Berry, R.Robert Carson, D.Daniela Cassol, S.Stephen Chan, J.John Coleman, K.Kastan Day, A.Alejandro Duque, K.Kjiersten Fagnan, J.Jeff Froula, S.Shantenu Jha, D. S.Daniel S Katz, P.Piotr Kica, V. V.Volodymyr V Kindratenko, E.Edward Kirton, R.Ramani Kothadia, D.Daniel Laney, F.Fabian Lehmann, U.Ulf Leser, S.Sabina Lichołai, M.Maciej Malawski, M.Mario Melara, E.Elais Player, M.Matthew Rolchigo, S.Setareh Sarrafan, S.-J.Seung-Jin Sul, A.Abdullah Syed, L.Lauritz Thamsen, M.Mikhail Titov, M.Matteo Turilli, S.Silvina Caino-Lores and A.Anirban Mandal. Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments.Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisSC-W 2023 - Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisDenver (CO), United StatesACMNovember 2023, 2097-2108HALDOIback to text
• 15 inproceedingsR.Robin Boëzennec, F.Fanny Dufossé and G.Guillaume Pallez. Optimization Metrics for the Evaluation of Batch Schedulers in HPC.JSSPP 2023 - 26th edition of the workshop on Job Scheduling Strategies for Parallel ProcessingSt. Petersburg, Florida, United StatesMarch 2023, 1-19HALback to text
• 16 inproceedingsM.Melvin Chelli, C.Cédric Prigent, R.René Schubotz, A.Alexandru Costan, G.Gabriel Antoniu, L.Loïc Cudennec and P.Philipp Slusallek. FedGuard: Selective Parameter Aggregation for Poisoning Attack Mitigation in Federated Learning.Cluster 2023 - IEEE International Conference on Cluster ComputingSanta Fe, New Mexico, United StatesIEEE2023, 1-10HALback to text
• 17 inproceedingsJ.Jakob Luettgau, S.Shane Snyder, T.Tyler Reddy, N.Nikolaus Awtrey, K.Kevin Harms, J. L.Jean Luca Bez, R.Rui Wang, R.Rob Latham and P.Philip Carns. Enabling Agile Analysis of I/O Performance Data with PyDarshan.SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisDenver CO USA, FranceACMNovember 2023, 1380-1391HALDOIback to text
• 18 inproceedingsD.Daniel Rosendo, K.Kate Keahey, A.Alexandru Costan, M.Matthieu Simonin, P.Patrick Valduriez and G.Gabriel Antoniu. KheOps: Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments.ACM REP '23: Proceedings of the 2023 ACM Conference on Reproducibility and ReplicabilityREP 2023 - ACM Conference on Reproducibility and ReplicabilitySanta Cruz, CA, United StatesACMJune 2023, 62-73HALDOIback to text
• 19 inproceedingsD.Daniel Rosendo, M.Marta Mattoso, A.Alexandru Costan, R.Renan Souza, D.Débora Pina, P.Patrick Valduriez and G.Gabriel Antoniu. ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum.IEEE Cluster 2023 - IEEE International Conference on Cluster ComputingSanta Fe, New Mexico, United StatesIEEE2023, 1-13HALback to text

Doctoral dissertations and habilitation theses

• 20 thesisG.Guillaume Pallez. Model Design and Accuracy for Resource Management in HPC.Université de BordeauxJuly 2023HAL
• 21 thesisD.Daniel Rosendo. Methodologies for Reproducible Analysis of Workflows on the Edge-to-Cloud Continuum.INSA RENNESJune 2023HAL

Reports & preprints

• 22 miscA.Alexis Bandet, F.Francieli Boito and G.Guillaume Pallez. Scheduling distributed I/O resources in HPC systems.January 2024HAL
• 23 reportF.Frédéric Blanqui, A.Anne Canteaut, H.Hidde de Jong, S.Sébastien Imperiale, N.Nathalie Mitton, G.Guillaume Pallez, X.Xavier Pennec, X.Xavier Rival and B.Bertrand Thirion. Recommandations sur les « éditeurs de la zone grise ».InriaJanuary 2023, 1-3HALback to text
• 24 reportF.Frédéric Blanqui, A.Anne Canteaut, H. D.Hidde De Jong, S.Sébastien Imperiale, N.Nathalie Mitton, G.Guillaume Pallez, X.Xavier Pennec, X.Xavier Rival and B.Bertrand Thirion. Recommendations on "Grey-Zone Publishers": Recommendations from the Inria Evaluation Committee, translated from https://hal.inria.fr/hal-04001505.InriaJanuary 2023, 1-3HALback to text
• 25 miscR.Robin Boëzennec, F.Fanny Dufossé and G.Guillaume Pallez. Analyzing Qualitatively Optimization Objectives in the Design of HPC Resource Manager.August 2023HALback to text
• 26 reportA.Anne Canteaut, M.Manuel Serrano, C.Céline Grandmont, G.Guillaume Pallez, V.Vincent Perrier, X.Xavier Rival and E.Emmanuel Thomé. Bilan de la mandature 2019-2023 de la Commission d'Évaluation Inria.InriaAugust 2023HALback to text
• 27 miscA.Ahmad Tarraf, A.Alexis Bandet, F.Francieli Zanon Boito, G.Guillaume Pallez and F.Felix Wolf. FTIO: Detecting I/O Periodicity Using Frequency Techniques.June 2023HALback to text