Noninvasive rapid detection of metabolic adaptation in activated human T lymphocytes by hyperpolarized 13 C magnetic resonance

Abstract: The metabolic shift induced in human CD4+ T lymphocytes by stimulation is characterized by an upregulation of glycolysis, leading to an augmentation in lactate production. This adaptation has already been highlighted with various techniques and reported in several previous studies. We herein propose a method to rapidly and noninvasively detect the associated increase in flux from pyruvate to lactate catalyzed by lactate dehydrogenase using hyperpolarized 13C magnetic resonance, a technique which can be used for in vivo imaging. It was shown that the conversion of hyperpolarized 13C-pyruvate to 13C-lactate during the one-minute measurement increased by a mean factor of 3.6 in T cells stimulated for 5 days as compared to resting T cells. This method can be extended to other metabolic substrates and is therefore a powerful tool to noninvasively analyze T cell metabolism, possibly in vivo

The LHCb upgrade I

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software

Shael Herman. — Medieval Usury and the Commercialization of Feudal Bonds. Berlin, Duncker & Humblot, 1993.

Couturier Arnaud. Shael Herman. — Medieval Usury and the Commercialization of Feudal Bonds. Berlin, Duncker & Humblot, 1993.. In: Cahiers de civilisation médiévale, 38e année, supplément annuel 1995. Comptes Rendus. pp. 39-40

Best effort strategy and virtual load for asynchronous iterative load balancing

International audienceMost of the time, asynchronous load balancing algorithms are extensively studied from a theoretical point of view. The Bertsekas and Tsitsiklis’ algorithm [1] is undeniably the best known algorithm for which the asymptotic convergence proof is given. From a practical point of view, when a node needs to balance a part of its load to some of its neighbors, the algorithm's description is unfortunately too succinct, and no details are given on what is really sent and how the load balancing decisions are made. In this paper, we propose a new strategy called best effort which aims at balancing the load of a node to all its less loaded neighbors while ensuring that all involved nodes by the load balancing phase have the same amount of load. Moreover, since asynchronous iterative algorithms are less sensitive to communication delays and their variations [2], both load transfer and load information messages are dissociated. To speedup the convergence time of the load balancing process, we propose a clairvoyant virtual load heuristic. This heuristic allows a node receiving a load information message to integrate the future virtual load (if any) in its load's list, even if the load has not been received yet. This leads to have predictive snapshots of nodes’ loads at each iteration of the load balancing process. Consequently, the notified node sends a real part of its load to some of its neighbors, taking into account the virtual load it will receive in the subsequent time-steps. Based on the SimGrid simulator, some series of test-bed scenarios are considered and several QoS metrics are evaluated to show the usefulness of the proposed algorithm

Parallel sparse linear solver with GMRES method using minimization techniques of communications for GPU clusters

International audienceIn this paper, we aim at exploiting the power computing of a graphics processing unit (GPU) cluster for solving large sparse linear systems. We implement the parallel algorithm of the generalized minimal residual iterative method using the Compute Unified Device Architecture programming language and the MPI parallel environment. The experiments show that a GPU cluster is more efficient than a CPU cluster. In order to optimize the performances, we use a compressed storage format for the sparse vectors and the hypergraph partitioning. These solutions improve the spatial and temporal localization of the shared data between the computing nodes of the GPU cluster

Optimizing the energy consumption of message passing applications with iterations executed over grids

International audienceIn recent years, green computing has become an important topic in the supercomputing research domain. However, the computing platforms are still consuming more and more energy due to the increasing number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them. It can be used to reduce the power consumption of the CPU while computing, by lowering its frequency. However, lowering the frequency of a CPU may increase the execution time of an application running on that processor. Therefore, the frequency that gives the best trade-off between the energy consumption and the performance of an application must be selected. In this paper, a new online frequency selecting algorithm for grids, composed of heterogeneous clusters, is presented. It selects the frequencies and tries to give the best trade-off between energy saving and performance degradation, for each node computing the message passing application with iterations. The algorithm has a small overhead and works without training or profiling. It uses a new energy model for message passing applications with iterations running on a grid. The proposed algorithm is evaluated on a real grid, the Grid’5000 platform, while running the NAS parallel benchmarks. The experiments on 16 nodes, distributed on three clusters, show that it reduces on average the energy consumption by 30% while the performance is on average only degraded by 3.2%. Finally, the algorithm is compared to an existing method. The comparison results show that it outperforms the latter in terms of energy consumption reduction and performance

Energy Consumption Reduction for Asynchronous Message Passing Applications

International audienceIt is widely accepted that the asynchronous parallel methods are more suitable than the synchronous ones on a grid architecture. Indeed, they outperform the synchronous methods, because they overlap the communications of the synchronous methods with computations. However, they also usually execute more iterations than the synchronous ones and thus consume more energy. To reduce the energy consumption of the CPUs executing such methods, the Dynamic voltage and frequency scaling technique can be used. It lowers the frequency of a CPU to reduce its energy consumption, but it also decreases its computing power. Therefore, the frequency that gives the best trade-off between energy consumption and performance must be selected. This paper presents a new online frequency selecting algorithm for parallel iterative asynchronous methods running over grids. It selects a vector of frequencies that gives the best trade-off between energy consumption and performance. New energy and performance models were used in this algorithm to predict the execution time and the energy consumption of synchronous, asynchronous, or hybrid iterative applications running over grids. The proposed algorithm was evaluated on the SimGrid simulator. The experiments showed that synchronously applying the proposed algorithm to the asynchronous version of the application reduces on average its energy consumption by 22% and speeds it up by 5.72%. Finally, the proposed algorithm was also compared to a method that uses the well-known energy and delay product and the comparison results showed that it outperforms this method in terms of energy consumption and performance

Parallel sparse linear solver with GMRES method using minimization techniques of communications for GPU clusters

International audienceIn this paper, we aim at exploiting the power computing of a graphics processing unit (GPU) cluster for solving large sparse linear systems. We implement the parallel algorithm of the generalized minimal residual iterative method using the Compute Unified Device Architecture programming language and the MPI parallel environment. The experiments show that a GPU cluster is more efficient than a CPU cluster. In order to optimize the performances, we use a compressed storage format for the sparse vectors and the hypergraph partitioning. These solutions improve the spatial and temporal localization of the shared data between the computing nodes of the GPU cluster