Limited-Precision Stochastic Rounding

Stochastic rounding (SR) is a probabilistic method used to round numbers to floating-point and fixed-point representations. In length $n$ summation, the worst-case error of SR grows as $\sqrt{n}$ with high probability, unlike for standard modes, like round-to-nearest (RN), which grows as $n$. For this reason, the former is increasingly employed in large-scale, low-precision computations as an RN alternative. Additionally, SR alleviates \emph{stagnation}, whereby relatively small summands are completely rounded off and do not contribute to the sum. We provide an update to [Croci et al., \emph{Roy.~Soc.~Open~Sci.}~9.3 (2022), pp. 1--25], a survey which discusses the development and use of SR between 1949 and 2022, citing over 100 references. Since then, there has been a surge of new research, and this update covers almost four years of further progress in applying, analysing, and implementing SR. Our main focus is \emph{limited-precision stochastic rounding}, a new variant that fixes the precision of the random numbers used. We provide insights into industrial and numerical analysis activities surrounding SR, highlighting the next possible steps in making this rounding mode more widely available in hardware

Évaluation de la cohésion de groupe : développement et validation du questionnaire de cohésion de groupe

Group cohesion is a fundamental yet complex construct shaping team performance and collaborative interactions quality. While existing theoretical models highlight its multi-dimensional nature, particularly social and task-related dimensions, they often lack practical applicability across diverse interaction contexts, including short-term or digitally mediated interactions. To address this gap, we propose a unified cohesion framework encompassing three functional dimensions: socio-affective, task, and communication. We elevate communication to a core component acknowledging its pivotal role in regulating group dynamics, maintaining shared understanding, and fostering rapport, especially in remote hybrid, or technology-supported collaborations. This perspective aligns with current needs in human-computer interaction for the design of collaborative systems. Based on this framework, we developed the Group Cohesion Questionnaire (GCQ), a concise self-report instrument assessing individual perceptions of group cohesion. The 16-item GCQ captures behavioral, affective, and metacognitive indicators and is validated across two contexts: a long-term academic project and a short-term collaborative game. Psychometric analyses confirm strong internal reliability, structural validity (via exploratory and confirmatory factor analysis), and both convergent and discriminant validity. The GCQ's brevity ensures ease of use while preserving a comprehensive and structured view of cohesion, making it a robust, versatile tool for group research and design applications.</div

Intel RAPL - Ses impacts sur l’infrastructure et son utilisation comme levier énergétique

International audienceEn France, le numérique représentait 4,4% de l’empreinte carbone en 2023, dont près dela moitié est attribuable aux centres de données. La phase d’utilisation de ces infrastruc-tures, fortement liée à l’électricité consommée, constitue à elle seule plus de 30% de cetteempreinte. Dans ce contexte, la limitation de la consommation énergétique des équipementsinformatiques apparaı̂t comme un levier majeur de réduction de l’impact environnemental.Intel RAPL est aujourd’hui largement utilisé et étudié comme mécanisme de contrôle énergétiqueau niveau matériel. S’il permet d’imposer des plafonds de consommation électrique à unnœud de calcul, son effet réel sur l’exécution des charges de travail reste mal caractérisé etdifficilement prédictible. En particulier, les mécanismes d’adaptation induits par Intel RAPL,tels que la modification dynamique des fréquences du CPU et de la mémoire, dépendent forte-ment de l’architecture matérielle et du profil applicatif.Dans cette présentation, nous proposons une analyse expérimentale approfondie des effetsd’Intel RAPL sur la consommation énergétique et le comportement des composants matériels.À partir de l’exécution d’une diversité de charges de travail sur plusieurs nœuds de calculde Grid5000, couvrant différentes architectures de processeurs et versions de RAPL, nousmettons en évidence une forte variabilité des effets à une même contrainte énergétique.Nos résultats montrent que l’utilisation d’Intel RAPL ne conduit pas nécessairement à uneréduction effective de l’énergie consommée pour une charge de travail donnée.Ces observations soulignent les limites d’une utilisation générique d’Intel RAPL comme levierde soutenabilité énergétique et mettent en évidence la nécessité d’une prise en compte finedes architectures et des charges de travail. Nous concluons en discutant des conditions danslesquelles Intel RAPL peut tout de même constituer un outil pertinent pour la réduction dela consommation énergétique des centres de données

Double Strike: Breaking Approximation-Based Side-Channel Countermeasures for DNNs

International audienceDeep neural networks (DNNs), which support services such as driving assistants and medical diagnoses, undergo lengthy and expensive training procedures. Therefore, the training's outcome -the DNN weights -represents a significant intellectual property asset to protect. Side-channel analysis (SCA) has recently appeared as an effective approach to recover this confidential asset from DNN implementations. In response, researchers have proposed to defend DNN implementations through classic side-channel countermeasures, at the cost of higher energy consumption, inference time, and resource utilisation. Following a different approach, Ding et al. (HOST'25) introduced MACPRUNING, a novel SCA countermeasure based on pruning, a performance-oriented Approximate Computing technique: at inference time, the implementation randomly prunes (or skips) non-important weights (i.e., with low contribution to the DNN's accuracy) of the first layer, exponentially increasing the side-channel resilience of the protected DNN implementation. However, the original security analysis of MACPRUNING did not consider a control-flow dependency intrinsic to the countermeasure design. This dependency may allow an attacker to circumvent MACPRUNING and recover the weights important to the DNN's accuracy. This paper describes a preprocessing methodology to exploit the above-mentioned control-flow dependency. Through practical experiments on a Chipwhisperer-Lite running a MACPRUNING-protected Multi-Layer Perceptron, we target the first 8 weights of each neuron and recover 96% of the important weights, demonstrating the drastic reduction in security of the protected implementation. Moreover, we show how microarchitectural leakage improves the effectiveness of our methodology, even allowing for the recovery of up to 100% of the targeted non-important weights. Lastly, by adapting our methodology, we elaborate on how the pruning mechanism, which depends on the importance of the weights, enables the circumvention of a control-flow-free MACPRUNING implementation. With this last point, we identify the pruning mechanism underlying MACPRUNING as the root of the countermeasure's vulnerability.</div

Pea transcriptional and phytohormonal responses to adapted and non-adapted aphid biotypes at early stages of infestation

International audiencePea (Pisum sativum L.), a major legume crop, is affected by various parasites including the pea aphid (Acyrthosiphon pisum Harris). The pea aphid is composed of multiple biotypes, each one being able to feed and reproduce on one or a few legume species. To understand the pea defense mechanisms to a pea adapted and a non-adapted A. pisum biotype, we studied the early molecular responses of four pea genotypes with contrasted levels of resistance, which are controlled primarily by the ApRVII locus. We found that major defense-related phytohormones and their derivatives in pea did not show clear response to aphid infestations. Transcriptomic analyses showed that the number of differentially expressed genes (DEGs) increased over time in pea genotypes infested with pea-adapted aphids, while significantly fewer DEGs were detected in genotypes infested with non-adapted aphids. The most resistant of the four investigated pea genotypes showed the fewest DEGs to both aphid biotypes. Aphid infestation of the three other pea genotypes commonly induced down-regulation of various pathways involved in fundamental biological processes. Comparison of the transcriptional data of pea genotypes identified candidate genes potentially involved in the aphid resistance conferred by ApRVII

Optimisation et évaluation des piles logicielles intégrées HPC-Quantiques

This study was carried out in the field of Quantum Computing (QC), particularly its integration into High-Performance Computing (HPC). Quantum Computing promises improvements in computation time and/or quality of results for some problems by exploiting the behavior of quantum physical systems. We were especially interested in the integration of HPC and QC software stacks, which encompass all the tools needed to develop and execute codes: compilers, libraries, etc. However, the integration of these HPC and QC software stacks is currently understudied, and its impact on application performance is not systematically evaluated. This lack of evaluation and study is due both to the fundamental differences between HPC and QC stacks and to the novelty of hybrid HPC-QC systems. All of this can lead to difficulties during the implementation, deployment, and execution of application combining HPC and QC. Currently, the characteristics of HPC and QC systems and software stacks are evaluated separately; notably, the HPC side has been studied for decades. This means that these evaluations are not performed on the new software stacks dedicated to hybrid HPC-QC systems. Furthermore, HPC and QC software stacks involve many steps related to combinatorial optimization problems, a problem type for which multiple quantum algorithms have been developed. In spite of this, the integration of such quantum algorithm into HPC and/or QC software stacks is still seeing limited interest.All of this leads us to this study, which aims to evaluate how the integration of current and future HPC-QC software stacks could impact the execution of codes. To this end, we first developed and validated a modular benchmarking framework to evaluate the influence of software stacks on the execution speed of HPC-QC applications. This framework, which we called HPCQCMark, aims to provide an open and easy-to-use tool that allows for quickly testing many combinations of HPC-QC stacks and applications. We used this framework to evaluate the impact of various aspects of the stacks from the HPC-QC systems at CEA and Riken on the execution of micro-applications. This allowed us to highlight both bugs in the tested stacks and several links between some parts of the stacks and the execution speed of codes. Given the existence of such links, we wanted to determine whether it would be possible to improve the execution of HPC codes by integrating quantum computing into the software stack. To this end, we designed hybrid quantum-classical algorithms for CPU Register Allocation, a critical step in compilation at the heart of HPC software stacks. These algorithms rely on variational quantum algorithms to perform part of the allocation, which was modeled as a combinatorial optimization problem. Finally, we implemented these algorithms and integrated them into existing widely used HPC compilers. We were then able to evaluate the ability of the algorithms to actually improve the quality of the compiled HPC codes. This allowed us to highlight some cases where the code compiled using our method had a shorter execution time than one compiled using the classical version of the allocator.Cette étude s'est réalisée dans le domaine du calcul quantique (QC), lié en particulier à son intégration dans le calcul haute performance (HPC). Le calcul quantique promet, en exploitant le comportement de systèmes physiques quantiques, des gains en temps de calcul et/ou en qualité des résultats pour un certain nombre de problèmes. Nous nous sommes intéressés particulièrement à l'intégration des piles logicielles HPC et QC, qui englobent tous les outils et programmes qui permettent le développement et l'exécution d'un code : compilateur, bibliothèques, système d'exploitation, etc. Cependant, l'intégration de ces piles logicielles HPC et QC n'est aujourd'hui que peu étudiée et son impact sur les performances des applications n'est pas systématiquement évalué. Ce manque d'évaluation systématique est dû à la fois à la grande différence entre les piles logicielles HPC et QC ainsi qu'à la nouveauté des systèmes hybrides HPC-QC. Tout cela peut engendrer des difficultés lors de l'implémentation, du déploiement et de l'exécution d'applications combinant calcul quantique et calcul haute performance. Aujourd'hui, les caractéristiques des systèmes et piles logicielles HPC et QC sont évaluées séparément, le côté HPC est notamment étudié depuis plusieurs décennies. Cela signifie que ces évaluations ne concernent pas les nouvelles piles logicielles spécifiques aux systèmes hybrides HPC-QC. Par ailleurs, les piles logicielles HPC et QC comportent de nombreuses étapes liées à des problèmes d'optimisation combinatoire et il existe plusieurs algorithmes quantiques qui visent à résoudre ce type de problèmes. Cependant l'intégration de ce type d'algorithme quantique dans les piles logicielles HPC et QC n'est encore que peu étudiée.Tout cela justifie notre étude consistant à évaluer comment l'intégration des piles logicielles HPC-QC présentes et futures pourrait impacter l'exécution des applications. Pour cela, nous avons tout d'abord développé et validé une suite modulaire de benchmarks permettant d'évaluer l'influence des piles logicielles sur la vitesse d'exécution de codes HPC-QC. Cette suite de benchmarks, que nous avons appelée HPCQCMark, vise à fournir un outil ouvert et simple d'utilisation qui permet de rapidement tester plusieurs combinaisons de piles et d'applications HPC-QC. Nous avons ainsi évalué l'impact sur l'exécution de micro-applications de divers aspects des piles logicielles déployées sur les systèmes HPC-QC installés au CEA et au Riken. Cela nous a permis de mettre en avant à la fois des bugs dans les piles testées et plusieurs liens entre certaines caractéristiques des piles et le temps d'exécution des codes. Ayant mis en avant l'existence de ces liens, nous avons voulu déterminer s'il serait possible d'améliorer l'exécution de codes HPC en intégrant du calcul quantique dans une pile logicielle. Pour cela, nous avons conçu des algorithmes hybrides classique-quantique pour l'allocation de registres CPU, une étape importante de la compilation qui est au cœur de la pile logicielle HPC. Ces approches hybrides reposent sur l'utilisation d'algorithmes quantiques variationnels afin de réaliser une partie de l'allocation, qui est représentée sous la forme d'un problème d'optimisation combinatoire. Finalement, nous avons implémenté ces algorithmes hybrides et les avons intégrés dans des compilateurs HPC existants et utilisés par un grand nombre de développeurs. Nous avons ensuite pu évaluer leur capacité à effectivement améliorer la qualité des codes HPC compilés. Cela nous a permis de mettre en avant des situations dans lesquelles le code compilé en utilisant notre méthode hybride avait effectivement un meilleur temps d'exécution qu'en utilisant une méthode classique

Local generation of languages: the monotonic binary sequences

In a previous article, we have introduced the problem of local generation of languages, where the communication underlying the generation procedure is captured by a simplicial complex. We study in details this problem for the language of binary monotonic sequences. We prove general results and identify several classes of minimal simplicial complexes generating this language

Sulfur: a Reflective Tactic for Substitution Simplification

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Bounded Sort Polymorphism with Elimination Constraints

International audienceProof assistants based on dependent type theory-such as Agda, Lean, and Rocq-employ different universes to classify types, typically combining a predicative tower for computationally relevant types with a possibly impredicative universe for proof-irrelevant propositions. Several other universes with specific logical and computational principles have been explored in the literature. In general, a universe is characterized by its sort (e.g., Type, Prop, or SProp) and, in the predicative case, by its level. To improve modularity and better avoid code duplication, sort polymorphism has recently been introduced and integrated in the Rocq prover.However, we observe that, due to its unbounded formulation, sort polymorphism is currently insufficiently expressive to abstract over valid definitions with a single polymorphic schema. Indeed, to ensure soundness of a multi-sorted type theory, the interaction between different sorts must be carefully controlled, as exemplified by the forbidden elimination of irrelevant terms to produce relevant ones. As a result, generic functions that eliminate values of inductive types from one sort to another cannot be made polymorphic; dually, polymorphic records that encapsulate attributes of different sorts cannot be defined. This lack of expressiveness also breaks the possibility to infer principal types, which is highly desirable for both metatheoretical and practical reasons. To address these issues, we extend sort polymorphism with bounds that reflect the required elimination constraints on sort variables. We present the metatheory of bounded sort polymorphism, paying particular attention to the consistency of the resulting constraint graph. We implement bounded sort polymorphism in Rocq and illustrate its benefits through concrete examples. Bounded sort polymorphism with elimination constraints is a natural and general solution that effectively addresses current limitations and fosters the development of, and practical experimentation with, multi-sorted type theories.</p

Geometry-agnostic model reduction with GNN-generated reduced POD bases and boosted PGD enrichment for (non)linear structural elastodynamics

International audienceThis contribution proposes a new and significantly enhanced extension of a recently-introduced hybrid Graph Neural Network (GNN)-based reduced-order modeling approach for the numerical solution of time-dependent partial differential equations on non-parametric finite element meshes. Building upon previous proof-of-concept work, this more generalized framework presents a number of key novelties: tight integration of graph-based learning with physical information via direct imposition of finite element operators as node and edge level features; introduction of a Grassmannian subspace distance measure as a dedicated training objective; incorporation of a Gated Recurrent Unit (GRU) for a more efficient and lightweight architecture; hybridization with other Galerkin-based reduced-order methods such as the Proper Orthogonal Decomposition (POD); and a first treatment of nonlinear problems. A novel, on-the-fly enrichment mechanism, modified from a classical Proper General Decomposition (PGD) and dubbed "Boosted PGD", is additionally introduced to improve prediction accuracy at low computational cost via additional greedy corrective modes. The efficacy of the overall methodology is assessed on two challenging datasets featuring significant geometric and topological variations that include highly heterogeneous spatial discretizations. A variety of performance studies demonstrate very competitive accuracy and computational cost in simulating highly-dynamic behavior when compared to conventional fullorder finite element models, including a remarkable capacity to generalize to configurations well outside of the topological scope of the original training and validation sets. Results imply that solvers constructed from such an approach may enable more scalable and robust mechanical simulations for complex, real-world engineering applications related to iterative design