University of Grenoble

About: University of Grenoble is a education organization based out in Saint-Martin-d'Hères, France. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 25658 authors who have published 45143 publications receiving 909760 citations.

High-spin phenomena in atomic-nuclei

Abstract: A description of high-spin phenomena in atomic nuclei is presented from both the experimental and theoretical points of view. The characteristic features of collective nuclear motion, such as rotational bands, band crossings, and backbending, as well as noncollective aspects, like high-spin isomers and irregular decay patterns, are discussed in detail. Recent achievements of the cranking model including the independent quasiparticle and the shell correction methods are reviewed. Changes in the structure of nuclei excited up to the highest possible angular momenta are analyzed; in particular, angular momentum alignment effects, shape changes, possible phase transitions, and sudden rearrangements in the single-particle structure are discussed. Phenomena related to the nuclear quasicontinuum spectra are also examined.

XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Abstract: Most recent HPC platforms have heterogeneous nodes composed of multi-core CPUs and accelerators, like GPUs. Programming such nodes is typically based on a combination of OpenMP and CUDA/OpenCL codes; scheduling relies on a static partitioning and cost model. We present the XKaapi runtime system for data-flow task programming on multi-CPU and multi-GPU architectures, which supports a data-flow task model and a locality-aware work stealing scheduler. XKaapi enables task multi-implementation on CPU or GPU and multi-level parallelism with different grain sizes. We show performance results on two dense linear algebra kernels, matrix product (GEMM) and Cholesky factorization (POTRF), to evaluate XKaapi on a heterogeneous architecture composed of two hexa-core CPUs and eight NVIDIA Fermi GPUs. Our conclusion is two-fold. First, fine grained parallelism and online scheduling achieve performance results as good as static strategies, and in most cases outperform them. This is due to an improved work stealing strategy that includes locality information; a very light implementation of the tasks in XKaapi; and an optimized search for ready tasks. Next, the multi-level parallelism on multiple CPUs and GPUs enabled by XKaapi led to a highly efficient Cholesky factorization. Using eight NVIDIA Fermi GPUs and four CPUs, we measure up to 2.43 TFlop/s on double precision matrix product and 1.79 TFlop/s on Cholesky factorization; and respectively 5.09 TFlop/s and 3.92 TFlop/s in single precision.

Unpacking ecosystem service bundles : Towards predictive mapping of synergies and trade-offs between ecosystem services

Abstract: Multiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science. Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge.

Gaia Data Release 2. Properties and validation of the radial velocities

Abstract: Context. For Gaia DR2, 280 million spectra collected by the Radial Velocity Spectrometer instrument on board Gaia were processed, and median radial velocities were derived for 9.8 million sources brighter than G RVS = 12 mag.Aims. This paper describes the validation and properties of the median radial velocities published in Gaia DR2.Methods. Quality tests and filters were applied to select those of the 9.8 million radial velocities that have the quality to be published in Gaia DR2. The accuracy of the selected sample was assessed with respect to ground-based catalogues. Its precision was estimated using both ground-based catalogues and the distribution of the Gaia radial velocity uncertainties. Results. Gaia DR2 contains median radial velocities for 7 224 631 stars, with T eff in the range [3550, 6900] K, which successfully passed the quality tests. The published median radial velocities provide a full-sky coverage and are complete with respect to the astrometric data to within 77.2% (for G ≤ 12.5 mag). The median radial velocity residuals with respect to the ground-based surveys vary from one catalogue to another, but do not exceed a few 100 m s−1 . In addition, the Gaia radial velocities show a positive trend as a function of magnitude, which starts around G RVS ~ 9 mag and reaches about + 500 m s−1 at G RVS = 11.75 mag. The origin of the trend is under investigation, with the aim to correct for it in Gaia DR3. The overall precision, estimated from the median of the Gaia radial velocity uncertainties, is 1.05 km s−1 . The radial velocity precision is a function of many parameters, in particular, the magnitude and effective temperature. For bright stars, G RVS ∈ [4, 8] mag, the precision, estimated using the full dataset, is in the range 220–350 m s−1 , which is about three to five times more precise than the pre-launch specification of 1 km s−1 . At the faint end, G RVS = 11.75 mag, the precisions for T eff = 5000 and 6500 K are 1.4 and 3.7 km s−1 , respectively.