Electrical provisioning in high performance computing is transitioning from simple nameplate Thermal Design Power (TDP) models to more nuanced approaches based on expected electrical load.This paper captures current power

Uncertainty quantification (UQ) has been widely applied in numerical simulation in recent years. Among different UQ methods, the Monte Carlo (MC) method played an important role in the field of UQ due to its simplicity and robustness. However, MC suffers from a low order convergence rate and low execution efficiency in High Performance Computing (HPC) systems. This study introduces a code intrinsic UQ (CIUQ) framework to optimize the performance of MC methods on HPC systems. The CIUQ employs code to code transformation to produce uncertainty intrinsic code (UIC), which extends the uncertain variables by a stochastic dimension, capturing their uncertainty information. Unlike the standard MC, which evaluates samples one by one, the UIC processes all samples as a single input and thus needs to be executed only once. To explore the positions and interactions of uncertain variables, specific algorithms are developed based on traversing the Abstract Syntax Tree (AST) of the original code. This exploration of uncertainty propagation details leads to a comprehensive understanding of variable dynamics within the UIC. Numerical experiments demonstrate that the UIC can improve the vectorization of recursive loops, and take the advantage of single instruction multiple data schemes on different platforms. Moreover, the UIC framework can further increase the data intensity of computational kernels and thus significantly improve strong scalability. These improvements, when compared to standard MC approaches, showcase the effectiveness of the new framework across various types of parallelism, significantly enhancing both efficiency and scalability.

In this work we describe an optimization technique for PIC/MC modeling of the plasma edge using High Performance Computing. The developed code BIT1 can be used for 1D3V plasma and 2D3V neutral particle modeling with a reasonable scaling up to 1000 and more processors.

SUMMARY: The Helmholtz Network for Bioinformatics (HNB) is a joint venture of eleven German bioinformatics research groups that offers convenient access to numerous bioinformatics resources through a single web portal. The 'Guided Solution Finder' which is available through the HNB portal helps users to locate the appropriate resources to answer their queries by employing a detailed, tree-like questionnaire. Furthermore, automated complex tool cascades ('tasks'), involving resources located on different servers, have been implemented, allowing users to perform comprehensive data analyses without the requirement of further manual intervention for data transfer and re-formatting. Currently, automated cascades for the analysis of regulatory DNA segments as well as for the prediction of protein functional properties are provided. AVAILABILITY: The HNB portal is available at http://www.hnbioinfo.de

Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in two-dimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year's conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidate which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors.

The minimum bounding box, the convex hull, the minimum bounding four- and five-corner, rotated boxes, and the minimum bounding ellipses and circles convex conservative approximation methods for handling complex spatial objects in spatial access methods are discussed. Results indicate that, depending on the complexity of the objects and the type of queries, the approximations five-corner, ellipse and rotated bounding box clearly outperform the bounding box. It is the reduced number of false hits that yields a considerable improvement in total query time when using the proposed approximations.>

The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the R*-tree which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory. Using our standardized testbed in an exhaustive performance comparison, it turned out that the R*-tree clearly outperforms the existing R-tree variants. Guttman's linear and quadratic R-tree and Greene's variant of the R-tree. This superiority of the R*-tree holds for different types of queries and operations, such as map overlay, for both rectangles and multidimensional points in all experiments. From a practical point of view the R*-tree is very attractive because of the following two reasons 1 it efficiently supports point and spatial data at the same time and 2 its implementation cost is only slightly higher than that of other R-trees.