4.1.1 Algorithms

The most common batch scheduling policy is to consider the jobs according to the First Come First Served order (FCFS) with backfilling (BF). BF is the most widely used policy due to its easy and robust implementation and known benefits such as high system utilization. It is well-known that this strategy does not optimize any sophisticated function, but it is simple to implement and it guarantees that there is no starvation (i.e. every job will be scheduled at some moment).

More advanced algorithms are seldom used on production platforms due to both the gap between theoretical models and practical systems and speed constraints. When looking at theoretical scheduling problems, the generally accepted goal is to provide polynomial algorithms (in the number of submitted jobs and the number of involved computing units). However, with millions of processing cores where every process and data transfer have to be individually scheduled, polynomial algorithms are prohibitive as soon as the polynomial degree is too large. The model of parallel tasks simplifies this problem by bundling many threads and communications into single boxes, either rigid, rectangular or malleable. Especially malleable tasks capture the dynamicity of the execution. Yet these models are ill-adapted to heterogeneous platforms, as the running time depends on more than simply the number of allotted resources, and some of the common underlying assumptions on the speed-up functions (such as monotony or concavity) are most often only partially verified.

In practice, the job execution times depend on their allocation (due to communication interferences and heterogeneity in both computation and communication), while theoretical models of parallel jobs usually consider jobs as black boxes with a fixed (maximum) execution time. Though interesting and powerful, the classical models (namely, synchronous PRAM model, delay, LogP) and their variants (such as hierarchical delay), are not well-suited to large scale parallelism on platforms where the cost of moving data is significant, non uniform and may change over time. Recent studies are still refining such models in order to take into account communication contentions more accurately while remaining tractable enough to provide a useful tool for algorithm design.

Today, all algorithms in use in production systems are oblivious to communications. One of our main goals is to design a new generation of scheduling algorithms fitting more closely job schedules according to platform topologies.

4.1.2 Locality Aware Allocations

Recently, we developed modifications of the standard back-filling algorithm taking into account platform topologies. The proposed algorithms take into account locality and contiguity in order to hide communication patterns within parallel tasks. The main result here is to establish good lower bounds and small approximation ratios for policies respecting the locality constraints. The algorithms work in an online fashion, improving the global behavior of the system while still keeping a low running time. These improvements rely mainly on our past experience in designing approximation algorithms. Instead of relying on complex networking models and communication patterns for estimating execution times, the communications are disconnected from the execution time. Then, the scheduling problem leads to a trade-off: optimizing locality of communications on one side and a performance objective (like the makespan or stretch) on the other side.

In the perspective of taking care of locality, other ongoing works include the study of schedulers for platforms whose interconnection network is a static structured topology (like the 3D-torus of the BlueWaters platform we work on in collaboration with the Argonne National Laboratory). One main characteristic of this 3D-torus platform is to provide I/O nodes at specific locations in the topology. Applications generate and access specific data and are thus bounded to specific I/O nodes. Resource allocations are constrained in a strong and unusual way. This problem is close for actual hierarchical platforms. The scheduler needs to compute a schedule such that I/O nodes requirements are filled for each application while at the same time avoiding communication interferences. Moreover, extra constraints can arise for applications requiring accelerators that are gathered on the nodes at the edge of the network topology.

While current results are encouraging, they are however limited in performance by the low amount of information available to the scheduler. We look forward to extend ongoing work by progressively increasing application and network knowledge (by technical mechanisms like profiling or monitoring or by more sophisticated methods like learning). It is also important to anticipate on application resource usage in terms of compute units, memory as well as network and I/Os to efficiently schedule a mix of applications with different profiles. For instance, a simple solution is to partition the jobs as "communication intensive" or "low communications". Such a tag could be achieved by the users them selves or obtained by learning techniques. We could then schedule low communications jobs using leftover spaces while taking care of high communication jobs. More sophisticated options are possible, for instance those that use more detailed communication patterns and networking models. Such options would leverage the work proposed in Section 4.2 for gathering application traces.

4.1.3 Data-Centric Processing

Exascale computing is shifting away from the traditional compute-centric models to a more data-centric one. This is driven by the evolving nature of large scale distributed computing, no longer dominated by pure computations but also by the need to handle and analyze large volumes of data. These data can be large databases of results, data streamed from a running application or another scientific instrument (collider for instance). These new workloads call for specific resource allocation strategies.

Data movements and storage are expected to be a major energy and performance bottleneck on next generation platforms. Storage architectures are also evolving, the standard centralized parallel file system being complemented with local persistent storage (Burst Buffers, NVRAM). Thus, one data producer can stage data on some nodes' local storage, requiring to schedule close by the associated analytics tasks to limit data movements. This kind of configuration, often referred as in-situ analytics, is expected to become common as it enables to switch from the traditional I/O intensive workflow (batch-processing followed by post mortem analysis and visualization) to a more storage conscious approach where data are processed as closely as possible to where and when they are produced (in-situ processing is addressed in details in section 4.3). By reducing data movements and scheduling the extra processing on resources not fully exploited yet, in-situ processing is expected to have also a significant positive energetic impact. Analytics codes can be executed in the same nodes than the application, often on dedicated cores commonly called helper cores, or on dedicated nodes called stagging nodes. The results are either forwarded to the users for visualization or saved to disk through I/O nodes. In-situ analytics can also take benefit of node local disks or burst buffers to reduce data movements. Future job scheduling strategies should take into account in-situ processes in addition to the job allocation to optimize both energy consumption and execution time. On the one hand, this problem can be reduced to an allocation problem of extra asynchronous tasks to idle computing units. But on the other hand, embedding analytics in applications brings extra difficulties by making the application more heterogeneous and imposing more constraints (data affinity) on the required resources. Thus, the main point here is to develop efficient algorithms for dealing with heterogeneity without increasing the global computational cost.

4.1.4 Learning

Another important issue is to adapt the job management system to deal with the bad effects of uncertainties, which may be catastrophic in large scale heterogeneous HPC platforms (jobs delayed arbitrarly far or jobs killed). A natural question is then: is it possible to have a good estimation of the job and platform parameters in order to be able to obtain a better scheduling ? Many important parameters (like the number or type of required resources or the estimated running time of the jobs) are asked to the users when they submit their jobs. However, some of these values are not accurate and in many cases, they are not even provided by the end-users. In DataMove, we propose to study new methods for a better prediction of the characteristics of the jobs and their execution in order to improve the optimization process. In particular, the methods well-studied in the field of big data (in supervised Machine Learning, like classical regression methods, Support Vector Methods, random forests, learning to rank techniques or deep learning) could and must be used to improve job scheduling in large scale HPC platforms. This topic received a great attention recently in the field of parallel and distributed processing. A preliminary study has been done recently by our team with the target of predicting the job running times (called wall times). We succeeded to improve significantly in average the reference EASY Back Filling algorithm by estimating the wall time of the jobs, however, this method leads to big delay for the stretch of few jobs. Even if we succeed in determining more precisely hidden parameters, like the wall time of the jobs, this is not enough to determine an optimized solution. The shift is not only to learn on dedicated parameters but also on the scheduling policy. The data collected from the accounting and profiling of jobs can be used to better understand the needs of the jobs and through learning to propose adaptations for future submissions. The goal is to propose extensions to further improve the job scheduling and improve the performance and energy efficiency of the application. For instance preference learning may enable to compute on-line new priorities to back-fill the ready jobs.

4.1.5 Multi-objective Optimization

Several optimization questions that arise in allocation and scheduling problems lead to the study of several objectives at the same time. The goal is then not a single optimal solution, but a more complicated mathematical object that captures the notion of trade-off. In broader terms, the goal of multi-objective optimization is not to externally arbitrate on disputes between entities with different goals, but rather to explore the possible solutions to highlight the whole range of interesting compromises. A classical tool for studying such multi-objective optimization problems is to use Pareto curves. However, the full description of the Pareto curve can be very hard because of both the number of solutions and the hardness of computing each point. Addressing this problem will opens new methodologies for the analysis of algorithms.

To further illustrate this point here are three possible case studies with emphasis on conflicting interests measured with different objectives. While these cases are good representatives of our HPC context, there are other pertinent trade-offs we may investigate depending on the technology evolution in the coming years. This enumeration is certainly not limitative.

Energy versus Performance. The classical scheduling algorithms designed for the purpose of performance can no longer be used because performance and energy are contradictory objectives to some extent. The scheduling problem with energy becomes a multi-objective problem in nature since the energy consumption should be considered as equally important as performance at exascale. A global constraint on energy could be a first idea for determining trade-offs but the knowledge of the Pareto set (or an approximation of it) is also very useful.

Administrators versus application developers. Both are naturally interested in different objectives: In current algorithms, the performance is mainly computed from the point of view of administrators, but the users should be in the loop since they can give useful information and help to the construction of better schedules. Hence, we face again a multi-objective problem where, as in the above case, the approximation of the Pareto set provides the trade-off between the administrator view and user demands. Moreover, the objectives are usually of the same nature. For example, max stretch and average stretch are two objectives based on the slowdown factor that can interest administrators and users, respectively. In this case the study of the norm of stretch can be also used to describe the trade-off (recall that the $L_1$-norm corresponds to the average objective while the $L_{\infty }$-norm to the max objective). Ideally, we would like to design an algorithm that gives good approximate solutions at the same time for all norms. The $L_2$ or $L_3$-norm are useful since they describe the performance of the whole schedule from the administrator point of view as well as they provide a fairness indication to the users. The hard point here is to derive theoretical analysis for such complicated tools.

Resource Augmentation. The classical resource augmentation models, i.e. speed and machine augmentation, are not sufficient to get good results when the execution of jobs cannot be frequently interrupted. However, based on a resource augmentation model recently introduced, where the algorithm may reject a small number of jobs, some members of our team have given the first interesting results in the non-preemptive direction. In general, resource augmentation can explain the intuitive good behavior of some greedy algorithms while, more interestingly, it can give ideas for new algorithms. For example, in the rejection context we could dedicate a small number of nodes for the usually problematic rejected jobs. Some initial experiments show that this can lead to a schedule for the remaining jobs that is very close to the optimal one.

4.2.1 Workload Traces with Resource Consumption

Workload traces are the base element to capture the behavior of complete systems composed of submitted jobs, running applications, and operating tools. These traces must be obtained on production platforms to provide relevant and representative data. To get a better understanding of the use of such systems, we need to look at both, how the jobs interact with the job management system, and how they use the allocated resources. We propose a general workload trace format that adds jobs resource consumption to the commonly used Standard Workload Format workload trace format. This requires to instrument the platforms, in particular to trace resource consumptions like CPU, data movements at memory, network and I/O levels, with an acceptable performance impact. In a previous work we studied and proposed a dedicated job monitoring tool whose impact on the system has been measured as lightweight (0.35$\%$ speed-down) with a 1 minute sampling rate. Other tools also explore job monitoring, like TACC Stats. A unique feature from our tool is its ability to monitor distinctly jobs sharing common nodes.

Collected workload traces with jobs resource consumption will be publicly released and serve to provide data for works presented in Section 4.1. The trace analysis is expected to give valuable insights to define models encompassing complex behaviours like network topology sensitivity, network congestion and resource interferences.

We expect to join efforts with partners for collecting quality traces (ATOS/Bull, Ciment meso center, Joint Laboratory on Extreme Scale Computing) and will collaborate with the INRIA team POLARIS for their analysis.

4.2.2 Simulation

Simulations of large scale systems are faster by multiple orders of magnitude than real experiments. Unfortunately, replacing experiments with simulations is not as easy as it may sound, as it brings a host of new problems to address in order to ensure that the simulations are closely approximating the execution of typical workloads on real production clusters. Most of these problems are actually not directly related to scheduling algorithms assessment, in the sense that the workload and platform models should be defined independently from the algorithm evaluations, in order to ensure a fair assessment of the algorithms' strengths and weaknesses. These research topics (namely platform modeling, job models and simulator calibration) are addressed in the other subsections.

We developed an open source platform simulator within DataMove (in conjunction with the OAR development team) to provide a widely distributable test bed for reproducible scheduling algorithm evaluation. Our simulator, named Batsim, allows to simulate the behavior of a computational platform executing a workload scheduled by any given scheduling algorithm. To obtain sound simulation results and to broaden the scope of the experiments that can be done thanks to Batsim, we did not chose to create a (necessarily limited) simulator from scratch, but instead to build on top of the SimGrid simulation framework.

To be open to as many batch schedulers as possible, Batsim decouples the platform simulation and the scheduling decisions in two clearly-separated software components communicating through a complete and documented protocol. The Batsim component is in charge of simulating the computational resources behaviour whereas the scheduler component is in charge of taking scheduling decisions. The scheduler component may be both a resource and a job management system. For jobs, scheduling decisions can be to execute a job, to delay its execution or simply to reject it. For resources, other decisions can be taken, for example to change the power state of a machine i.e. to change its speed (in order to lower its energy consumption) or to switch it on or off. This separation of concerns also enables interfacing with potentially any commercial RJMS, as long as the communication protocol with Batsim is implemented. A proof of concept is already available with the OAR RJMS.

Using this test bed opens new research perspectives. It allows to test a large range of platforms and workloads to better understand the real behavior of our algorithms in a production setting. In turn, this opens the possibility to tailor algorithms for a particular platform or application, and to precisely identify the possible shortcomings of the theoretical models used.

4.2.3 Job and Platform Models

The central purpose of the Batsim simulator is to simulate job behaviors on a given target platform under a given resource allocation policy. Depending on the workload, a significant number of jobs are parallel applications with communications and file system accesses. It is not conceivable to simulate individually all these operations for each job on large plaforms with their associated workload due to implied simulation complexity. The challenge is to define a coarse grain job model accurate enough to reproduce parallel application behavior according to the target platform characteristics. We will explore models similar to the BSP (Bulk Synchronous Program) approach that decomposes an application in local computation supersteps ended by global communications and a global synchronization. The model parameters will be established by means of trace analysis as discussed previously, but also by instrumenting some parallel applications to capture communication patterns. This instrumentation will have a significant impact on the concerned application performance, restricting its use to a few applications only. There are a lot of recurrent applications executed on HPC platform, this fact will help to reduce the required number of instrumentations and captures. To assign each job a model, we are considering to adapt the concept of application signatures as proposed in. Platform models and their calibration are also required. Large parts of these models, like those related to network, are provided by Simgrid. Other parts as the filesystem and energy models are comparatively recent and will need to be enhanced or reworked to reflect the HPC platform evolutions. These models are then generally calibrated by running suitable benchmarks.

4.2.4 Emulation and Reproducibility

The use of coarse models in simulation implies to set aside some details. This simplification may hide system behaviors that could impact significantly and negatively the metrics we try to enhance. This issue is particularly relevant when large scale platforms are considered due to the impossibility to run tests at nominal scale on these real platforms. A common approach to circumvent this issue is the use of emulation techniques to reproduce, under certain conditions, the behavior of large platforms on smaller ones. Emulation represents a natural complement to simulation by allowing to execute directly large parts of the actual evaluated software and system, but at the price of larger compute times and a need for more resources. The emulation approach was chosen in to compare two job management systems from workload traces of the CURIE supercomputer (80000 cores). The challenge is to design methods and tools to emulate with sufficient accuracy the platform and the workload (data movement, I/O transfers, communication, applications interference). We will also intend to leverage emulation tools like Distem from the MADYNES team. It is also important to note that the Batsim simulator also uses emulation techniques to support the core scheduling module from actual RJMS. But the integration level is not the same when considering emulation for larger parts of the system (RJMS, compute node, network and filesystem).

Replaying traces implies to prepare and manage complex software stacks including the OS, the resource management system, the distributed filesystem and the applications as well as the tools required to conduct experiments. Preparing these stacks generate specific issues, one of the major one being the support for reproducibility. We propose to further develop the concept of reconstructability to improve experiment reproducibility by capturing the build process of the complete software stack. This approach ensures reproducibility over time better than other ways by keeping all data (original packages, build recipe and Kameleon engine) needed to build the software stack.

In this context, the Grid'5000 (see Sec. 7.2) experimentation infrastructure that gives users the control on the complete software stack is a crucial tool for our research goals. We will pursue our strong implication in this infrastructure.

4.3.1 Programming Model and Software Architecture

In situ creates a tighter loop between the scientist and her/his simulation. As such, an in situ framework needs to be flexible to let the user define and deploy its own set of analysis. A manageable flexibility requires to favor simplicity and understandability, while still enabling an efficient use of parallel resources. Visualization libraries like VTK or Visit, as well as domain specific environments like VMD have initially been developed for traditional post-mortem data analysis. They have been extended to support in situ processing with some simple resource allocation strategies but the level of performance, flexibility and ease of use that is expected requires to rethink new environments. There is a need to develop a middleware and programming environment taking into account in its fundations this specific context of high performance scientific analytics.

Similar needs for new data processing architectures occurred for the emerging area of Big Data Analytics, mainly targeted to web data on cloud-based infrastructures. Google Map/Reduce and its successors like Spark or Stratosphere/Flink have been designed to match the specific context of efficient analytics for large volumes of data produced on the web, on social networks, or generated by business applications. These systems have mainly been developed for cloud infrastructures based on commodity architectures. They do not leverage the specifics of HPC infrastructures. Some preliminary adaptations have been proposed for handling scientific data in a HPC context. However, these approaches do not support in situ processing.

Following the initial development of FlowVR, our middleware for in situ processing, we will pursue our effort to develop a programming environment and software architecture for high performance scientific data analytics. Like FlowVR, the map/reduce tools, as well as the machine learning frameworks like TensorFlow, adopted a dataflow graph for expressing analytics pipe-lines. We are convinced that this dataflow approach is both easy to understand and yet expresses enough concurrency to enable efficient executions. The graph description can be compiled towards lower level representations, a mechanism that is intensively used by Stratosphere/Flink for instance. Existing in situ frameworks, including FlowVR, inherit from the HPC way of programming with a thiner software stack and a programming model close to the machine. Though this approach enables to program high performance applications, this is usually too low level to enable the scientist to write its analysis pipe-line in a short amount of time. The data model, i.e. the data semantics level accessible at the framework level for error check and optimizations, is also a fundamental aspect of such environments. The key/value store has been adopted by all map/reduce tools. Except in some situations, it cannot be adopted as such for scientific data. Results from numerical simulations are often more structured than web data, associated with acceleration data structures to be processed efficiently. We will investigate data models for scientific data building on existing approaches like Adios or DataSpaces.

4.3.2 Resource Sharing

To alleviate the I/O bottleneck, the in situ paradigm proposes to start processing data as soon as made available by the simulation, while still residing in the memory of the compute node. In situ processings include data compression, indexing, computation of various types of descriptors (1D, 2D, images, etc.). Per se, reducing data output to limit I/O related performance drops or keep the output data size manageable is not new. Scientists have relied on solutions as simple as decreasing the frequency of result savings. In situ processing proposes to move one step further, by providing a full fledged processing framework enabling scientists to more easily and thoroughly manage the available I/O budget.

The most direct way to perform in situ analytics is to inline computations directly in the simulation code. In this case, in situ processing is executed in sequence with the simulation that is suspended meanwhile. Though this approach is direct to implement and does not require complex framework environments, it does not enable to overlap analytics related computations and data movements with the simulation execution, preventing to efficiently use the available resources. Instead of relying on this simple time sharing approach, several works propose to rely on space sharing where one or several cores per node, called helper cores, are dedicated to analytics. The simulation responsibility is simply to handle a copy of the relevant data to the node-local in situ processes, both codes being executed concurrently. This approach often lead to significantly beter performance than in-simulation analytics.

For a better isolation of the simulation and in situ processes, one solution consists in offloading in situ tasks from the simulation nodes towards extra dedicated nodes, usually called staging nodes. These computations are said to be performed in-transit. But this approach may not always be beneficial compared to processing on simulation nodes due to the costs of moving the data from the simulation nodes to the staging nodes.

FlowVR enables to mix these different resources allocation strategies for the different stages of an analytics pile-line. Based on a component model, the scientist designs analytics workflows by first developing processing components that are next assembled in a dataflow graph through a Python script. At runtime the graph is instantiated according to the execution context, FlowVR taking care of deploying the application on the target architecture, and of coordinating the analytics workflows with the simulation execution.

But today the choice of the resource allocation strategy is mostly ad-hoc and defined by the programmer. We will investigate solutions that enable a cooperative use of the resource between the analytics and the simulation with minimal hints from the programmer. In situ processings inherit from the parallelization scale and data distribution adopted by the simulation, and must execute with minimal perturbations on the simulation execution (whose actual resource usage is difficult to know a priori). We need to develop adapted scheduling strategies that operate at compile and run time. Because analysis are often data intensive, such solutions must take into consideration data movements, a point that classical scheduling strategies designed first for compute intensive applications often overlook. We expect to develop new scheduling strategies relying on the methodologies developed in Sec. 4.1.5. Simulations as well as analysis are iterative processes exposing a strong spatial and temporal coherency that we can take benefit of to anticipate their behavior and then take more relevant resources allocation strategies, possibly based on advanced learning algorithms or as developed in Section 4.1.

In situ analytics represent a specific workload that needs to be scheduled very closely to the simulation, but not necessarily active during the full extent of the simulation execution and that may also require to access data from previous runs (stored in the file system or on specific burst-buffers). Several users may also need to run concurrent analytics pipe-lines on shared data. This departs significantly from the traditional batch scheduling model, motivating the need for a more elastic approach to resource provisioning. These issues will be conjointly addressed with research on batch scheduling policies (Sec. 4.1).

4.3.3 Co-Design with Data Scientists

Given the importance of users in this context, it is of primary importance that in situ tools be co-designed with advanced users, even if such multidisciplinary collaborations are challenging and require constant long term investments to learn and understand the specific practices and expectations of the other domain.

We will tightly collaborate with scientists of some application domains, like molecular dynamics or fluid simulation, to design, develop, deploy and assess in situ analytics scenarios.

7.1.1 FlowVR

• Scientific Description:

  FlowVR adopts the "data-flow" paradigm, where your application is divided as a set of components exchanging messages (think of it as a directed graph). FlowVR enables to encapsulate existing codes in components, interconnect them through data channels, and deploy them on distributed computing resources. FlowVR takes care of all the heavy lifting such as application deployment and message exchange.

  The base entity, called a module or component, is an autonomous process, potentially multi-threaded with tools like OpenMP, TBB, or deferring computations to a GPU or Xeon Phi. This module processes data coming from input ports and write data on output ports. A module has no global insight on where the data comes from or goes to. The programming interface is designed to limit code refactoring, easing turning an existing code into a FlowVR component. The three main functions are:

  wait(): Blocking function call that waits for the availability of new messages on input ports. get(): Retrieve a handle to access the message received at the previous wait() call on a given input port. put(): Notify FlowVR that a new message on a given output port is ready for dispatch. FlowVR manages data transfers. Intra-node communications between two components take place through a shared memory segment, avoiding copies. Once the sender has prepared the data in a shared memory segment, it simply handles a pointer to the destination that can directly access them. Inter-node communications extend this mechanism, FlowVR taking care of packing and transferring the data from the source shared memory segment to the destination shared memory segment.

  Assembling components to build an application consists in writing a Python script, instanciate it according to the target machine. FlowVR will process it and prepare everything so that in one command line you can deploy and start your application.

• Functional Description:
  FlowVR adopts the "data-flow" paradigm, where your application is divided as a set of components exchanging messages (think of it as a directed graph). FlowVR enables to encapsulate existing codes in components, interconnect them through data channels, and deploy them on distributed computing resources. FlowVR takes care of all the heavy lifting such as application deployment and message exchange.
• URL:
  http://­flowvr.­sf.­net
• Contact:
  Bruno Raffin
• Participants:
  Bruno Raffin, Clément Ménier, Emmanuel Melin, Jean Denis Lesage, Jérémie Allard, Jérémy Jaussaud, Matthieu Dreher, Sébastien Limet, Sophie Robert, Valérie Gourantou

7.1.2 OAR

• Keywords:
  HPC, Cloud, Clusters, Resource manager, Light grid
• Scientific Description:
  This batch system is based on a database (PostgreSQL (preferred) or MySQL), a script language (Perl) and an optional scalable administrative tool (e.g. Taktuk). It is composed of modules which interact mainly via the database and are executed as independent programs. Therefore, formally, there is no API, the system interaction is completely defined by the database schema. This approach eases the development of specific modules. Indeed, each module (such as schedulers) may be developed in any language having a database access library.
• Functional Description:
  OAR is a versatile resource and task manager (also called a batch scheduler) for HPC clusters, and other computing infrastructures (like distributed computing experimental testbeds where versatility is a key).
• URL:
  http://­oar.­imag.­fr
• Contact:
  Olivier Richard
• Participants:
  Bruno Bzeznik, Olivier Richard, Pierre Neyron
• Partners:
  LIG, CNRS, Grid'5000, CIMENT, UAR GRICAD

7.1.3 MELISSA

• Keywords:
  Sensitivity Analysis, HPC, Data assimilation, Exascale
• Functional Description:
  Melissa is a middleware framework for on-line processing of data produced from large scale ensemble runs (parameter sweep data analysis). Initial developments focused on sensibility analysis, Melissa relying on iterative statistics to provide a file avoiding, fault tolerant and elastic framework. Largest runs so far involved up to 30k core, executed 80 000 parallel simulations, and generated 288 TB of intermediate data that did not need to be stored on the file system. Melissa was next extended to large scale data assimilation, with the integration of a simulation (or member) virtualization mechanism that enables to abstract the number of members from the actual resource allocations, further improving execution efficiency and elasticity. Latest contribution is support for training deep surrogate models with support for Phytorch and Tensorflow
• News of the Year:
  Melissa has been extended for supporting large scale data assimilation (EnkF and Particle Filters), as well as online training of deep surrogate models
• URL:
  https://­gitlab.­inria.­fr/­melissa
• Publications:
  hal-01383860, hal-01607479, hal-03017033, hal-03927612, hal-03842106
• Authors:
  Theophile Terraz, Bruno Raffin, Alejandro Ribes, Bertrand Iooss
• Contact:
  Bruno Raffin
• Partner:
  Edf

7.1.4 Batsim

• Keywords:
  Simulation, Distributed systems
• Functional Description:
  BatSim is a Resource and Job Management System (RJMS) framework simulator based on SimGrid. It aims at taking into account platform's hardware capabilities and impacts in simulations. Also, schedulers parts are plugable through a comprehensive API and they are seen as external component of the framework.
• Release Contributions:
  see https://batsim.readthedocs.io/en/latest/changelog.html
• URL:
  https://­batsim.­readthedocs.­io/­en/­latest/
• Contact:
  Olivier Richard
• Partner:
  IRIT

7.2.1 SILECS/Grid'5000 and Meso Center Ciment

We are very active in promoting the factorization of compute resources at a regional and national level. We have a three level implication, locally to maintain a pool of very flexible experimental machines (hundreds of cores), regionally through the CIMENT meso center, and nationally by contributing to the SILECS/Grid'5000 platform, our local resources being included in this platform. Olivier Richard is member of SILECS/Grid'5000 scientific committee. The OAR scheduler in particular is deployed on both infrastructures. DataMove is hosting several ingineers dedicated to Grid'5000 support.

8.1.1 Energy Saving and sustainability of information technologies

The question of energy consumption of computing platforms has become a major topic for DataMove team in the last years.

The energy consumption of computing platforms is one of the major concerns for sustainability of information technology. A new study 14 has evaluated the energy consumption of numerous Deep Learning models considering their accuracy and duration of training. The question of energy consumption measurement of an on-going project 47 that compares the accuracy of different tools.

The question of energy consumption can also be studied considering its footprint. In 35, a scheduling problem is considered on a cloud geo-distributed platform supplied by both the electric grid and a photovoltaic farm. The objective is to allocate the workload to minimize the grid electricity consumption. This requests to predict the future solar irradiation and to avoid traffic congestion at communication network level.

The question of sustainability of computing science research community has been studied by 39 to evaluate the footprint of conferences travel and the impact of virtual conferences based on Euro-Par conference data.

8.1.2 On-line Scheduling Using Resource Augmentation

Resource augmentation is a well-established (and powerful) model for analyzing algorithms, particularly in online setting. It has been successfully used for providing theoretical evidence for several heuristics in scheduling with good performance in practice. According to this model, the algorithm is applied to a more powerful environment than that of the adversary. Several types of resource augmentation for scheduling problems have been proposed up to now, including speed augmentation, machine augmentation and more recently rejection. We presented a framework that unifies the various types of resource augmentation. It allows to generalize the notion of resource augmentation for other types of resources. Our framework is based on Dual Fitting. It consists of extending the domain of feasible solutions for the algorithm with respect to the domain of an adversary. This, in turn, allows the natural concept of duality for mathematical programming to be used as a tool for the analysis of the algorithm performance. As an illustration of the above ideas, we apply this framework, and we propose a primal-dual algorithm for the online scheduling problem of minimizing the total weighted flow time of jobs on unrelated machines when the preemption of jobs is not allowed.

We derived 40 a collection of results on on-line non-preemptive scheduling algorithms: Targeting the minimization of the weighted flow time on unrelated machines, we considered a version where the online algorithm can reject some ${\epsilon }_r>0$ fraction (by weight) of the jobs and have machines that are $1+{\epsilon }_s$ as fast as the offline machines, for some ${\epsilon }_s>0$. We proved that this is already enough to achieve a competitive ratio of $O(1/\left({\epsilon }_s{\epsilon }_r\right))$. More recently, we continued the study showing that it is sufficient to reject a $2\epsilon$ fraction of the total number of jobs to achieve a competitive ratio of $2\left(\frac{1+\epsilon }{\epsilon }\right)$ . We also considered the speed scaling model, in which machines can be sped up if additional energy is invested, and the goal is to minimize the total weighted flow time plus energy usage. If the power function of machine $i$ is given by $P\left(s_i\left(t\right)\right)=s_i{\left(t\right)}^{\alpha }$, where $s_i\left(t\right)$ is the current speed of machine $i$, there is an algorithm which is $O({(1+1/\epsilon )}^{\alpha /(\alpha -1))}$-competitive that rejects jobs of total weight at most a fraction $\epsilon$ of the total weight of all the jobs. We also derived a positive result for jobs with hard deadlines, where the objective is to minimize the total energy usage and no job may be rejected. Finally, we closed the story in generalizing these results by showing that rejection alone is sufficient for an algorithm to be competitive even for weighted flow time. They presented an $O(1/{\epsilon }^3)$-competitive algorithm that rejects at most $O\left(\epsilon \right)$ of the total weight of the jobs.

8.1.3 Scheduling for Edge Infrastructures

Internet Of Things is a new step in IT intrusion in everyday life. Edge computing uses these components as a new scale of parallel computing platform. Federated learning (FL) is an approach that enables collaborative machine learning (ML) without sharing data over the network. We developed 22 a prototype that enables distributed ML model deployment, federated task orchestration, and monitoring of system state and model performance. The method was applied to predictive maintenance that aims to anticipate industrial equipment failures in order to allow early scheduling of corrective actions.

Another study 26 has considered the operational cost of edge computing platforms. It aims to provide decision aid support to a technical smart buildings manager to potentially reduce the emission of data produced by sensors inside a building and, more generally, to acquire knowledge on the data produced in the facility. The description and the construction of learning models over data sets are crucial in engineering studies to advance critical analysis and serve diverse researchers' communities, such as architects or data scientists.

8.1.4 Gradient algorithms for online decentralized optimization

A new research topic started in 2022 in DataMove, following the arrival of Kim Thang Nguyen. The question of decentralized online optimization problems that is frequent in scheduling environment was study in 11, 21. The approach consists in a stochastic conditional gradient approach using the Frank-Wolfe algorithm. The resulting method permits a an asymptotically tight regret guarantee of $O\left(\sqrt{T}\right)$ with $T$ the time horizon. It also allows to address the uncertainty of settings, partial information for agents and low resources for communications and computations. An application was run on a smart building context 20 that permit to enhance the applicability of the method.

8.2.1 A Methodology to Scale Containerized HPC Infrastructures in the Cloud

We developped a generic method to scale HPC clusters on top of the Kubernetes cloud orchestrator. Users define their targeted infrastructure with the usual Kubernetes syntax for recipes, and our approach automatically translates the description to a full-fledged containerized HPC cluster. Moreover, resource extensions or shrinks are handled, allowing a dynamic resize of the containerized HPC cluster without disturbing its running. The Kubernetes orchestrator acts as a provisioner. We applied the generic method to three orthogonal architectural designs Open Source HPC schedulers: SLURM, OAR, and OpenPBS. Through a series of experiments, the paper demonstrates the potential of our approach regarding the scalability issues of HPC clusters and the simultaneous deployment of several job schedulers in the same physical infrastructure. It should be noticed that our plan does not require any modification either in the containers orchestrator or in the HPC schedulers. Our proposal is a step forward to reconciling the two ecosystems of HPC and cloud. It also calls for new research directions and concrete implementations for the dynamic consolidation of servers or sober placement policies at the orchestrator level. The works contribute a new approach to running HPC clusters in a cloud environment and test the technique on robustness by adding and removing nodes on the fly.

8.2.2 Model-free control for resource harvesting in computing grids

Cloud and High-Performance Computing (HPC) systems are increasingly facing issues of dynamic variability, in particular w.r.t. performance and power consumption. They are becoming less predictable, and therefore demand more runtime management by feedback loops. In this work, we describe results addressing autonomic administration in HPC systems through a control theoretical approach. We more specifically consider the need for controllers that can adapt to variations a long time in the behavior of controlled systems, but also to being reused on different systems and processors. We therefore explore the application of Model-Free Control (MFC) in the context of resource harvesting in a Computing Grid, by regulating the injection of flexible jobs while limiting perturbation of the prioritary applications.

8.2.3 Painless Transposition of Reproducible Distributed Environments with NixOS Compose

Development of environments for distributed systems is a tedious and time-consuming iterative process. The reproducibility of such environments is a crucial factor for rigorous scientific contributions. We think that being able to smoothly test environments both locally and on a target distributed platform makes development cycles faster and reduces the friction to adopt better experimental practices. To address this issue, this paper introduces the notion of environment transposition and implements it in NixOS Compose, a tool that generates reproducible distributed environments. It enables users to deploy their environments on virtualized (docker, QEMU) or physical (Grid'5000) platforms with the same unique description of the environment. We show that NixOS Compose enables to build reproducible environments without overhead by comparing it to state-of-the-art solutions for the generation of distributed environments (EnOSlib and Kameleon). NixOS Compose actually enables substantial performance improvements on image building time over Kameleon (up to 11x faster for initial builds and up to 19x faster when building a variation of an existing environment).

8.2.4 Reproducibility

The ability to reproduce an experiment is fundamental in this research axis. Existing approaches focus on repeatability, but this is only the first step to reproducibility: continuing a scientific work from a previous experiment requires to be able to modify it. We called this ability reproducibility with variation. We have shown that capturing the environment of execution is necessary but not sufficient ; we also need the environment of development. The variation also implies that those environments are subject to evolution, so the whole software development lifecycle needs to be considered. To take into account these evolutions, software environments need to be clearly defined, reconstructible with variation, and easy to share. We proposed to leverage functional package managers to achieve this goal.

8.3.1 Data analysis for a single simulation run.

We are working with CEA (PhD of Amal Gueroudji) to enable in situ processing for the Dask distributed task programming environment. We developped a hybrid model called DEISA, that supports coupling MPI parallel codes with analyses written using Dask. This implementation requires minimal modifications of both the simulation and analysis codes compared to their post hoc counterpart. It give access to an already existing rich ecosystem to be used in situ such as the parallel versions of Numpy, Pandas and scikit-learn. Experiments in configurations up to 1024 cores show that DEISA can improve the simulation wallclock time (excluding analysis) by a factor up to 3 and the total experiment (including analysis) hour.core cost by a factor of up to 5 compared to parallel post hoc with plain Dask while requiring the modification of only two lines of python code, three of YAML, and none at all in a C simulation code already instrumented with PDI Data Interface. Index Terms—In situ processing, code coupling, task-based programming, MPI, Dask

8.3.2 Data analysis for ensemble simulation runs.

We put significant efforts in investigating in situ processing beyond a single large-scale simulation, considering use cases where the analysis needs to combine data from multiple simulation runs (also commonly called ensemble run). Such use-cases are becoming more common with the need to sample the simulation behavior within some parameter ranges for extracting knowledge using statistical or machine learning based methods, combined with the availability of large supercomputers capable today of running thousands of large simulation instances. Each simulation being potentially large, the amount of data generated by multiple runs is huge, leading to a pressing need for frugal I/O solutions. Initial work focused on sensibility analysis, where the data produced by the simulation are aggregated to compute statistics. We developed Melissa, a file avoiding, fault tolerant and elastic framework. Melissa is built around two key concepts: iterative (sometimes also called incremental) statistics algorithms and asynchronous client/server model for data transfer. Simulation outputs are never stored on disc. They are sent by the simulations to a parallel server, which aggregates them to the statistic fields in an iterative fashion, and then throw them away. This allows to compute oblivious statistics maps on every mesh element for every time step on a full scale study. Largest runs so far involved up to 30k core, executed 80 000 parallel simulations, and generated 288 TB of intermediate data that did not need to be stored on the file system.

Then we extended this work in two directions:

• Instead of computing statistics we are investigating how to train on-line surrogate models using deep learning. This join work with EDF Lab led to (PhD Lucas Meyer) led to revisite the classical epoch based training process to adapt it to the online setup enabled by Melissa. First investigation point is to add the necessary mechnisms to ensure that the no biais is intrudced due to the specificity of the online training: order of data generation that is dicted by the simulation its self, the resources available on the compute machine to run various simulations concurrently, and the limited amount of memory avaiable for storing data 32.
• The other direction focused on extending Melissa for ensemble based Data Assimilation (DA) (Sebastian Friedemann PhD). Data assimilation is more demanding than open-loop data processing, as the analysis results are used to steer the simulations progresses. The general approach consists in periodically correcting the simulations trajectories by minimizing the global error obtained from the combination of observation data, typically acquired by on-the-field sensors, and simulation data. Data assimilation is particularly used in domains like weather forecast and climate simulation where numerical models are highly sensible to parameter values. Our solution extends Melissa with simulation virtualization and dynamics load balancing, further improving the elasticity and efficiency of executions. Experiments run on up to 16240 cores, to propagate 16384 members using the ParFlow hydrology simulation code 9

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

• Associate Team UNIFY (Intelligent Unified Data Services for Hybrid Workflows Combining Compute-Intensive Simulations and Data-Intensive Analytics at Extreme Scales)
• Partners:
  • INRIA teams: KerData, DataMove
  • Argonne National Lab (Tom PETERKA)
• Duration: 2019-2022
• Project providing travel money, mainly untouched due to Covid.

10.1.2 STIC/MATH/CLIMAT AmSud projects

10.2.1 Visits of international scientists

10.3.1 H2020 projects

10.4.1 BPI

• Projet AMI Cloud OTPaaS (2021-2024). Aims at offering a new Cloud offer, compatible with Gaia-X and easy to use, that could favour the massive digital transition of companies. Datamove Budget: 110 Keuro.

10.4.2 ANR

• PPR Océan et Climat MEIDATION (2022-2030). Methodological developments for a robust and efficient digital twin of the ocean. Pi: INRIA team AIRSEA. Partners: INRIA, CNRS, IFREMER, IRD, Université Aix-Marseille, Institut National Polytechnique de Toulouse, Ecole Nationale Supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, Service Hygrodgraphique et Océanographique de la Marine, Université Grenoble Alpes, Météo-France-DESR-Centre National de Recherches Météorologiques. Total budget: 2,4 Meuros. Datamove Budget: 110 Keuros. CO-lead of the WP Leveraging AI and HPC for Digital Twins of the Ocean.
• ANR grant Energumen (2018-2022). Resource management: malleable jobs for a better use of the resources along with energy optimization. Coordinator: Denis Trystram. Partners: Grenoble-INP, IRIT, Sorbonne Université.

10.4.3 INRIA

• INRIA Challenge HPC-BigData (2018-2022). Convergence between HPC, Big Data and AI. Coordinator: Bruno Raffin. Partners: the INRIA teams Zenith, Kerdata, Datamove, Tadaam, SequeL, Parietal, Tau, and the external partners ATOS, ANL, IBPC, ESI-Group. See Web Site

10.4.4 Univ. Grenoble Alpes

• Edge Intelligence chair of the Institute of Artificial Intelligence of Univ. Grenoble Alpes (MIA@Grenoble-Alpes) (2019-2023). PI: Denis Trystram. The challenges are to design new machine learning methods that fully exploit the distributed character of the edge and to develop algorithms and subsequent pieces of software that will allow the deployment of the edge/fog hybrid infrastructures. The research agenda is two-fold. In the first hand, we study new methods for distributed machine learning and data analytic. In the second hand, we develop the models and mechanisms for the orchestration of efficient local resource management. Budget: 335K euro
• IRS SoSCloud. Dimensioning of green energy in Clouds, 2020-2023. UGA Grant. PhD funding. Co-advised by D. Cordeiro, USP, Brasil. Budget : 120 Keuros

11.1.1 Scientific events: organisation

11.1.2 Journal

11.1.3 Invited talks

• Keynote Speaker at the 19th Annual IFIP International Conference on Network and Parallel Computing (NPC 2022), Jinan, China on Sep. 2022.
• Le numérique face au réchauffement climatique : opportunité ou handicap ?. Keynote at ROADEF anual conference, Lyon,Fev. 2022.
• Carbon cost of Euro-Par. Invited talk Euro-Par 2022, Glasgow, Aug. 2022.

11.1.4 Leadership within the scientific community

• Member of the advisory committee of IEEE Cloud Computing ;

11.2.1 Teaching

• Denis Trystram. 200 hours per year, ENSIMAG, Grenoble-INP, Master
• Fanny Dufossé. 20 hours per year, Algorithmic, Licence. Univ. Grenoble-Alpes and Licence Ensimag, Combinatorial scientific computing, Master, ENS Lyon.
• Pierre-François Dutot. 226 hours per year. Licence (first and second year) at IUT2/UPMF (Institut Universitaire Technologique de Univ. Grenoble-Alpes) and 9 hours Master M2R-ISC Informatique-Systèmes-Communication at Univ. Grenoble-Alpes.
• Grégory Mounié is responsible for the first year (M1) of the international Master of Science in Informatics at Grenoble (MOSIG-M1). 317 hours per year. Master (M1 and M2 year) at Engineering school ENSIMAG, Grenoble-INP, Univ Grenoble Alpes.
• Bruno Raffin is responsible of the Distributed Computing Track, Master international Science in Informatics at Grenoble (MoSIG), UGA. 35 hours per year (M2 - Parallel Systems, L3 - Big Data Processing).
• Olivier Richard is responsible for the third year of the computer science department of Grenoble INP. 222 hours per year. Master at Engineering school Polytech-Grenoble, Univ. Grenoble-Alpes.
• Frédéric Wagner. 220 hours per year. Engineering school ENSIMAG, Grenoble-INP, Master (M1/2nd year and M2/3rd year).
• Yves Denneulin. 192 hours per year. Engineering school ENSIMAG, Grenoble-INP, Master (M1/2nd year and M2/3rd year).

11.2.2 Juries

• Jury member of Yishu DU, Fault-tolerant algorithms for iterative applications and batch schedulers, Dec. 6th, Lyon.

11.2.3 Internal or external Inria responsibilities

• France Exascale Project:
  • Co-coordinator of the SUN (Sciences et Usages du Numérique) axis (Les applications françaises face à l’exascale).
  • INRIA representative at GENCI for the sub-project 3 (SP3).

International journals

• 5 articleJ.Jules Bertrand, F.Fanny Dufossé, S.Somesh Singh and B.Bora Uçar. Algorithms and Data Structures for Hyperedge Queries.ACM Journal of Experimental Algorithmics27December 2022, 1-23
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• 6 articleC.Christophe Cérin, K.Keiji Kimura and M.Mamadou Sow. Data stream clustering for low-cost machines.Journal of Parallel and Distributed Computing166August 2022, 57-70
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• 7 articleF.Fanny Dufossé, C.Christoph Dürr, N.Noël Nadal, D.Denis Trystram and Ó.Óscar Vásquez. Scheduling with a processing time oracle.Applied Mathematical Modelling104April 2022, 701-720
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• 8 articleF.Fanny Dufossé, K.Kamer Kaya, I.Ioannis Panagiotas and B.Bora Uçar. Scaling matrices and counting the perfect matchings in graphs.Discrete Applied Mathematics308February 2022, 130--146
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• 9 articleS.Sebastian Friedemann and B.Bruno Raffin. An elastic framework for ensemble-based large-scale data assimilation.International Journal of High Performance Computing Applications36June 2022, 1-37
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• 10 articleY.Yuegang Li, D.Dongyang Ou, X.Xin Zhou, C.Congfeng Jiang and C.Christophe Cérin. Scalability and performance analysis of BDPS in clouds.Computing1046June 2022, 1425-1460
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• 11 articleN. K.Nguyễn Kim Thắng, A.Abhinav Srivastav, D.Denis Trystram and P.Paul Youssef. A stochastic conditional gradient algorithm for decentralized online convex optimization.Journal of Parallel and Distributed Computing169November 2022, 334-351
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• 12 articleS.Salah Zrigui, R. y.Raphael y de Camargo, A.Arnaud Legrand and D.Denis Trystram. Improving the Performance of Batch Schedulers Using Online Job Runtime Classification.Journal of Parallel and Distributed Computing164February 2022, 83-95
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International peer-reviewed conferences

• 13 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, 1-19
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• 14 inproceedingsD.Danilo Carastan dos Santos and T. H.Thi Hoang Thi Pham. Understanding the Energy Consumption of HPC Scale Artificial Intelligence.CARLA 2022 - Latin America High Performance Computing ConferencePorto Alegre, BrazilSeptember 2022, 1-14
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• 15 inproceedingsO.Oscar Carrillo, C. J.Carlos Jaime Barrios Hernandez, F.Frederic Le Mouel, H.Harold Barrera, Y.Yves Denneulin, J. T.Jose Tiberio Hernandez, F. J.Fernando Jimenez Vargas, L. X.Lola Xiomara Bautista Rozo, C.Claudia Roncancio and M.Michel Riveill. Scalable Architectures to Support Sustainable Advanced Information Technologies.IEEE XploreCLUSTER 2022 - 2022 IEEE International Conference on Cluster ComputingHeidelberg, GermanyIEEESeptember 2022, 512-515
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• 16 inproceedingsE.Ernest Foussard, M.-L.Marie-Laure Espinouse, G.Grégory Mounié and M.Margaux Nattaf. Ordonnancement de la production et de la maintenance sur une machine multicomposant: étude de complexité.23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionVilleurbanne - Lyon, FranceFebruary 2022
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• 17 inproceedingsQ.Quentin Guilloteau, J.Jonathan Bleuzen, M.Millian Poquet and O.Olivier Richard. Painless Transposition of Reproducible Distributed Environments with NixOS Compose.CLUSTER 2022 - IEEE International Conference on Cluster ComputingCLUSTER 2022 - IEEE International Conference on Cluster ComputingHeidelberg, GermanySeptember 2022, 1-12
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• 18 inproceedingsQ.Quentin Guilloteau, B.Bogdan Robu, C.Cédric Join, M.Michel Fliess, É.Éric Rutten and O.Olivier Richard. Model-free control for resource harvesting in computing grids.CCTA 2022 - Conference on Control Technology and Applications, CCTA 2022Trieste, ItalyIEEEAugust 2022
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• 19 inproceedingsM.Mathilde Jay, V.Vladimir Ostapenco, L.Laurent Lefèvre, D.Denis Trystram, A.-C.Anne-Cécile Orgerie and B.Benjamin Fichel. An experimental comparison of software-based power meters: focus on CPU and GPU.CCGrid 2023 - 23rd IEEE/ACM international symposium on cluster, cloud and internet computingBangalore, IndiaIEEE2023, 1-13
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• 20 inproceedingsA.Angan Mitra, N. K.Nguyen Kim Thang, T.-A.Tuan-Anh Nguyen, D.Denis Trystram and P.Paul Youssef. Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-building.Global IoT Conference (GloTS 2022)13533Lecture Notes in Computer ScienceDublin, IrelandJune 2022, 43--54
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• 21 inproceedingsT.-A.Tuan-Anh Nguyen, N. K.Nguyen Kim Thang and D.Denis Trystram. One gradient frank-wolfe for decentralized online convex and submodular optimization.ACML 2022 - 14th Asian Conference in Machine LearningHyderabad, IndiaDecember 2022, 1-33
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• 22 inproceedingsH.Hamza Safri, M. M.Mohamed Mehdi Kandi, Y.Youssef Miloudi, C.Christophe Bortolaso, D.Denis Trystram and F.Frederic Desprez. Towards Developing a Global Federated Learning Platform for IoT.Proceedings of IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)Bologna, ItalyIEEEJuly 2022, 1312-1315
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• 23 inproceedingsM.Miguel Vasconcelos, D.Daniel Cordeiro and F.Fanny Dufossé. Dimensioning of multi-clouds with follow-the-renewable approaches for environmental impact minimization.23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionVilleurbanne - Lyon, FranceFebruary 2022
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National peer-reviewed Conferences

• 24 inproceedingsV.Vincent Fagnon, G.Giorgio Lucarelli, C.Clement Mommessin and D.Denis Trystram. Ordonnancement Deux-Agents avec Augmentation de Ressources.23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionVilleurbanne - Lyon, FranceFebruary 2022
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• 25 inproceedingsT.Ting Zhang, D.Dongyang Ou, Z.Zhefeng Ge, C.Congfeng Jiang, C.Christophe Cérin and L.Longchuan Yan. Interference-aware Workload Scheduling in Co-located Data Centers.Network and Parallel Computing: 19th IFIP WG 10.3 International Conference, NPC 202213615Lecture Notes in Computer ScienceJinan, ChinaSpringer Nature SwitzerlandDecember 2022, 69-82
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Conferences without proceedings

• 26 inproceedingsL.Louis Closson, C.Christophe Cérin, D.Didier Donsez and D.Denis Trystram. Towards a Methodology for the Characterization of IoT Data Sets of the Smart Building Sector.2022 IEEE International Smart Cities Conference (ISC2)Pafos, CyprusIEEE; IEEEOctober 2022, 1-7
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• 27 inproceedingsE.Ernest Foussard, G.Gérémi Bridonneau, M.-L.Marie-Laure Espinouse, G.Grégory Mounié and M.Margaux Nattaf. Génération de colonnes pour le Bin-Packing avec seuils.ROADEF 2023 - 24ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionRennes, FranceFebruary 2023
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• 28 inproceedingsV.Virginie Grandgirard, K.Kevin Obrejan, D.Dorian Midou, Y.Y Asahi, P.-E.P-E Bernard, J.J Bigot, E.E Bourne, J.J Dechard, G.G Dif- Pradalier, P.P Donnel, X.X Garbet, A.A Gueroudji, G.G Hager, H.H Murai, Y.Yacine Ould-Ruis, T.T Padioleau, L.L Nguyen, M.M Peybernes, Y.Y Sarazin, M.M Sato, M.M Tsuji and P.P Vezolle. New advances to prepare GYSELA-X code for exascale global gyrokinetic plasma turbulence simulations: porting on GPU and ARM architectures.PASC22 - Conderence on The Platform for Advanced Scientific ComputingBâle (virtual event), SwitzerlandJune 2022, 1-21
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• 29 inproceedingsN.Nicolas Greneche, T.Tarek Menouer, C.Christophe Cérin and O.Olivier Richard. A Methodology to Scale Containerized HPC Infrastructures in the Cloud.Euro-Par 2022: Parallel Processing: 28th International Conference on Parallel and Distributed Computing13440Lecture Notes in Computer ScienceGlasgow, United KingdomSpringer International PublishingAugust 2022, 203-217
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• 30 inproceedingsQ.Quentin Guilloteau, J.Jonathan Bleuzen, M.Millian Poquet and O.Olivier Richard. Transposition d'environnements distribués reproductibles avec NixOS Compose.COMPAS 2022 - Conférence francophone d'informatique en Parallélisme, Architecture et SystèmeAmiens, FranceJuly 2022, 1-9
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• 31 inproceedingsQ.Quentin Guilloteau, O.Olivier Richard and É.Éric Rutten. Étude des applications Bag-of-Tasks du méso-centre Gricad.COMPAS 2022 - Conférence francophone d'informatique en Parallélisme, Architecture et SystèmeAmiens, FranceJuly 2022, 1-7
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• 32 inproceedingsL.Lucas Meyer, A.Alejandro Ribés and B.Bruno Raffin. Simulation-Based Parallel Training.NeurIPS 2022 - AI for Science WorkshopNew Orleans, United StatesNovember 2022
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• 33 inproceedingsA.Alejandro Ribés, R.Ruben Persicot, L.Lucas Meyer and J.-P.Jean-Pierre Ducreux. A hybrid Reduced Basis and Machine-Learning algorithm for building Surrogate Models: a first application to electromagnetism.Workshop on Machine Learning and the Physical Sciences workshop at NeurIPS 2022New Orleans, United StatesDecember 2022, 1-6
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• 34 inproceedingsH.Hamza Safri, M. M.Mohamed Mehdi Kandi, Y.Youssef Miloudi, C.Christophe Bortolaso, D.Denis Trystram and F.Frederic Desprez. A Federated Learning Framework for IoT: Application to Industry 4.0.2022 22nd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)Taormina, ItalyIEEEMay 2022, 565-574
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• 35 inproceedingsM. F.Miguel Felipe Silva Vasconcelos, D.Daniel Cordeiro and F.Fanny Dufossé. Indirect Network Impact on the Energy Consumption in Multi-clouds for Follow-the-renewables Approaches.Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS11th International Conference on Smart Cities and Green ICT SystemsOnline Streaming, BrazilSCITEPRESS - Science and Technology PublicationsApril 2022, 44-55
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Scientific book chapters

• 36 inbookA.Alejandro Ribés, T.Théophile Terraz, Y.Yvan Fournier, B.Bertrand Iooss and B.Bruno Raffin. Unlocking Large Scale Uncertainty Quantification with In Transit Iterative Statistics.In Situ Visualization for Computational ScienceMathematics and VisualizationSpringer International PublishingMay 2022, 113-136
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Reports & preprints

• 37 miscL.Luc Angelelli, A. A.Anderson Andrei Da Silva, M.Michael Mercier, G.Grégory Mounié, D.Denis Trystram and Y.Yiannis Georgiou. Towards a Multi-objective Scheduling Policy for Serverless-based Edge-Cloud Continuum.May 2023
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• 38 reportJ.Jules Bertrand, F.Fanny Dufossé, S.Somesh Singh and B.Bora Uçar. Algorithms and data structures for hyperedge queries.RR-9390Inria Grenoble Rhône-AlpesApril 2022, 28
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• 39 miscD.Danilo Carastan dos Santos, K.Krzysztof Rzadca, L.Leonel Sousa and D.Denis Trystram. A community-guided discussion about social and environmental effects of post-COVID-19 Computer Science conferencing.2023
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• 40 miscV.Vincent Fagnon, G.Giorgio Lucarelli, C.Clément Mommessin and D.Denis Trystram. Two-Agent Scheduling with Resource Augmentation on Multiple Machines.May 2022
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• 41 miscS.Sebastian Friedemann, K.Kai Keller, Y.-S.Yen-Sen Lu, B.Bruno Raffin and L.Leonardo Bautista Gomez. A Framework for Large Scale Particle Filters Validated with Data Assimilation for Weather Simulation.2023
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• 42 miscQ.Quentin Guilloteau, O.Olivier Richard, R.Raphaël Bleuse and E.Eric Rutten. Folding a Cluster containing a Distributed File-System.2023
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• 43 miscQ.Quentin Guilloteau, O.Olivier Richard and É.Éric Rutten. Étude des applications Bag-of-Tasks du méso-centre Gricad.July 2022
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• 44 miscL.Laurent Lefèvre, A.-L.Anne-Laure Ligozat, D.Denis Trystram, S.Sylvain Bouveret, A.Aurélie Bugeau, J.Jacques Combaz, E.Emmanuelle Frenoux, G.Gaël Guennebaud, J.Julien Lefèvre, J.-P.Jean-Philippe Nicolaï and K.Karin Dassas. Environmental assessment of projects involving AI methods.January 2023
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• 45 reportL.Laurent Lefèvre, A.-L.Anne-Laure Ligozat, D.Denis Trystram, S.Sylvain Bouveret, A.Aurélie Bugeau, J.Jacques Combaz, E.Emmanuelle Frenoux, G.Gaël Guennebaud, J.Julien Lefèvre and J.-P.Jean-Philippe Nicolaï. Proposition de document de cadrage Évaluation environnementale de projets impliquant des méthodes d'IA.EcoInfo2022, 1-8
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• 46 miscL.Lucas de Sousa Rosa, D.Danilo Carastan-Santos and A.Alfredo Goldman. A multiple linear regression approach for understanding the trade-offs in learning HPC job scheduling heuristics.2023
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Other scientific publications

• 47 inproceedingsM.Mathilde Jay, L.Laurent Lefèvre and D.Denis Trystram. Comparaison expérimentale de logiciels de mesure d'énergie : côté GPU.Compas 2022Amiens, FranceJuly 2022
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• 48 articleA.Amina Khedimi, T.Tarek Menouer, C.Christophe Cérin and M.Mourad Boudhar. A cloud weather forecasting service and its relationship with anomaly detection.Service Oriented Computing and Applications163September 2022, 191-208
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Scientific popularization

• 49 miscD.Denis Trystram. Calcul haute performance et ordinateurs superpuissants : la course à l’« exascale ».December 2022
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Softwares

• 50 softwareM.Mathilde Jay, V.Vladimir Ostapenco, L.Laurent Lefèvre, D.Denis Trystram, A.-C.Anne-Cécile Orgerie and B.Benjamin Fichel. Artifact: An experimental comparison of software-based power meters.December 2022 lic: CC BY-SA 4.0.
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