List Scheduling: The Price of Distribution

Classical list scheduling is a very popular and efficient technique for scheduling jobs in parallel and distributed platforms. It is inherently centralized. However, with the increasing number of processors in new parallel platforms, the cost for managing a single centralized list becomes too prohibitive. A suitable approach to reduce the contention is to distribute the list among the computational units. Thus each processor has only a local view of the work to execute. The objective of this work is to study the extra cost that must be paid when the list is distributed among the computational units. We present a general methodology for computing the expected makespan based on the analysis of an adequate potential function which represents the load unbalance between the local lists. It is applied to several scheduling problems, namely, for arbitrary divisible load, for unit independent tasks, for weighted independent tasks and for tasks with dependencies. It is presented in detail for the simplest case of divisible load, and then extended to the other cases

Proton Radiation Belt Anisotropy as Seen by ICARE-NG Head-A

International audienceThe ICARE-NG instrument onboard the Argentinian satellite SAC-D detected much more protons during descending orbits (when latitude decreases) than for ascending orbits (increasing latitudes). In this paper we will investigate on the anisotropy seen by the ICARE-NG Head-A for protons in coincidence mode from Monte-Carlo simulations performed with GEANT4. Our simulations show that the difference in the fluxes observed during ascending and descending orbits comes from the fact that the instrument observed trapped protons or not on each point of the orbits as a result of the instrument and satellite orientations. In addition, we show in this paper that the measurements performed by ICARE-NG can be used in conjunction with our GEANT4 simulations to study the anisotropy of trapped protons, i.e. their distribution relative to their equatorial pitch-angle

Processor-Oblivious Parallel Stream Computations

We study the problem of parallel stream computations on a multiprocessor architecture. Modelling the problem, we exhibit that any parallelisation introduces an arithmetic overhead related to intermediate copy operations. We pro-vide lower bounds for the parallel stream computation on p processors of different speeds with two models, a strict model and a buffered model; to our knowledge, these are new results. We introduce a new parallel algorithm called processor-oblivious: it is based on the coupling of a fast sequential algorithm with a fine-grain parallel one that is scheduled by work-stealing. This algorithm is proved asymptotically optimal. We show that our algorithm has a good experimental behaviour. 1

Landslide Detection in Real-Time Social Media Image Streams

Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. However, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence (AI) techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real time. To that end, we create a large landslide image dataset labeled by experts and conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response

Biometric Systems Private by Design: Reasoning about privacy properties of biometric system architectures

International audienceThe goal of the work presented in this paper is to show the applicability of the privacyby design approach to biometric systems and the benefit of using formal methods to this end. Webuild on a general framework for the definition and verification of privacy architectures introducedat STM 2014 and show how it can be adapted to biometrics. The choice of particular techniques andthe role of the components (central server, secure module, biometric terminal, smart card, etc.) in thearchitecture have a strong impact on the privacy guarantees provided by a biometric system. Somearchitectures have already been analysed but on a case by case basis, which makes it difficult to drawcomparisons and to provide a rationale for the choice of specific options. In this paper, we describethe application of a general privacy architecture framework to specify different design options forbiometric systems and to reason about them in a formal way

Protection de la vie privée dès la phase de conception: application à la vérification de propriétés d'architectures de systèmes biométriques

The goal of the work presented in this paper is to show the applicability of the privacy by design approach to biometric systems and the benefit of using formal methods to this end. We build on a general framework for the definition and verification of privacy architectures introduced at STM 2014 and show how it can be adapted to biometrics. The choice of particular techniques and the role of the components (central server, secure module, biometric terminal, smart card, etc.) in the architecture have a strong impact on the privacy guarantees provided by a biometric system. Some architectures have already been analysed but on a case by case basis, which makes it dicult to draw comparisons and to provide a rationale for the choice of specific options. In this paper, we describe the application of a general privacy architecture framework to specify di↵erent design options for biometric systems and to reason about them in a formal way

A Tighter Analysis of Work Stealing

Classical list scheduling is a very popular and efficient technique for scheduling jobs in parallel platforms. However, with the increasing number of processors, the cost for managing a single centralized list becomes prohibitive. The objective of this work is to study the extra cost that must be paid when the list is distributed among the processors. We present a general methodology for computing the expected makespan based on the analysis of an adequate potential function which represents the load unbalance between the local lists. A bound on the deviation from the mean is also derived. Then, we apply this technique to show that the expected makespan for scheduling W unit independent tasks on m processors is equal to W/m with an additional term in 3.65log_2 W. Moreover, simulations show that our bound is very close to the exact value, approximately 50\% off. This new analysis also enables to study the influence of the initial repartition of tasks and the reduction of the number of steals when several thieves can simultaneously steal work in the same processor's list

A gate-tunable graphene Josephson parametric amplifier

With a large portfolio of elemental quantum components, superconducting quantum circuits have contributed to dramatic advances in microwave quantum optics. Of these elements, quantum-limited parametric amplifiers have proven to be essential for low noise readout of quantum systems whose energy range is intrinsically low (tens of $\mu$eV ). They are also used to generate non classical states of light that can be a resource for quantum enhanced detection. Superconducting parametric amplifiers, like quantum bits, typically utilize a Josephson junction as a source of magnetically tunable and dissipation-free nonlinearity. In recent years, efforts have been made to introduce semiconductor weak links as electrically tunable nonlinear elements, with demonstrations of microwave resonators and quantum bits using semiconductor nanowires, a two dimensional electron gas, carbon nanotubes and graphene. However, given the challenge of balancing nonlinearity, dissipation, participation, and energy scale, parametric amplifiers have not yet been implemented with a semiconductor weak link. Here we demonstrate a parametric amplifier leveraging a graphene Josephson junction and show that its working frequency is widely tunable with a gate voltage. We report gain exceeding 20 dB and noise performance close to the standard quantum limit. Our results complete the toolset for electrically tunable superconducting quantum circuits and offer new opportunities for the development of quantum technologies such as quantum computing, quantum sensing and fundamental science

Landslide detection in real-time social media image streams

Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response