A nonoscillatory, characteristically convected, finite volume scheme for multidimensional convection problems

A new, nonoscillatory upwind scheme is developed for the multidimensional convection equation. The scheme consists of an upwind, nonoscillatory interpolation of data to the surfaces of an intermediate finite volume; a characteristic convection of surface data to a midpoint time level; and a conservative time integration based on the midpoint rule. This procedure results in a convection scheme capable of resolving discontinuities neither aligned with, nor convected along, grid lines

A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems

Among the algorithms that are likely to play a major role in future exascale computing, the fast multipole method (FMM) appears as a rising star. Our previous recent work showed scaling of an FMM on GPU clusters, with problem sizes in the order of billions of unknowns. That work led to an extremely parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This paper reports on a a campaign of performance tuning and scalability studies using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were parallelized using OpenMP, and a test using 10^7 particles randomly distributed in a cube showed 78% efficiency on 8 threads. Tuning of the particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel scalability was studied in both strong and weak scaling. The strong scaling test used 10^8 particles and resulted in 93% parallel efficiency on 2048 processes for the non-SIMD code and 54% for the SIMD-optimized code (which was still 2x faster). The weak scaling test used 10^6 particles per process, and resulted in 72% efficiency on 32,768 processes, with the largest calculation taking about 40 seconds to evaluate more than 32 billion unknowns. This work builds up evidence for our view that FMM is poised to play a leading role in exascale computing, and we end the paper with a discussion of the features that make it a particularly favorable algorithm for the emerging heterogeneous and massively parallel architectural landscape

Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches

The objective of this work is to model the driver behaviour in the area of route selection. The research focus on an optimum route search function in a typical in-car navigation system or dynamic route guidance (DRG) system. In this work, we want to emphasize the need to orientate the route selection method on the driver's preference. Each route candidate has a set of attributes. A fuzzy-neural approach is used to represent the correlation of the attributes with the driver's route selection. A recommendation or route ranking can be provided to the driver. Based on a training of the fuzzy-neural net on the driver's choice, the route selection function can be made adaptive to the decision-making of the driver.published_or_final_versio

CTAD as a universal anticoagulant

The feasibility of CTAD (a mixture of citrate, theophylline, adenosine and dipyridamole) as a new anticoagulant for medical laboratory use was studied prospectively. Whole blood anticoagulated with CTAD exhibited results very similar to those of blood anticoagulated with EDTA on complete blood count and automated white cell differential except for a slight decrease in platelet count and mean platelet volume. Chemistry test data for plasma obtained from CTAD whole blood were close to those obtained for matched sera. Among coagulation tests, prothrombin time, activated partial thromboplastin time and fibrinogen concentrations were close to those obtained with citrate plasma. Based on the results, CTAD was judged to be a good candidate as a new anticoagulant