A parallel implementation of the Finite-Domain Time-Difference algorithm using MPJ express

This paper presents and evaluates a parallel Java imple-mentation of the Finite-Difference Time-Domain (FDTD) method, which is a widely used numerical technique in computational electrodynamics. The Java version is par-allelized using MPJ Express—a thread-safe messaging li-brary. MPJ Express provides a full implementation of the mpiJava 1.2 API specification. This specification defines a MPI-like binding for the Java language. This paper de-scribes our experiences of implementing the Java version of the FDTD method. Towards the end of this paper, we evaluate and compare the performance of the Java version against its C counterpart on a 32 processing core Linux cluster of eight compute nodes.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy

Data Processing and Investigations for the GRACE Follow-On Laser Ranging Interferometer

This thesis presents first in-depth results of the Laser Ranging Interferometer (LRI) onboard the Gravity Recovery And Climate Experiment - Follow On (GRACE-Follow On) mission. The LRI is a novel instrument, which was developed in a U.S.-German collaboration including the Albert-Einstein Institute (AEI) in Hanover. It successfully demonstrated the feasibility of ranging measurements by means of laser interferometry between two distant spacecraft and will push space-borne gravimetry missions to the next sensitivity level. The author of this thesis contributed to this project by programming a comprehensive framework for ground-processing of LRI telemetry and analyzing various kinds of instrument data streams. Therefore, the title of this thesis covers both topics, data processing and investigations within the data. Within this thesis, an introduction to laser interferometry is given and the various payloads of the GRACE-Follow On satellites are presented. Furthermore, the design of the LRI itself is discussed, in order to understand the profound causal relations when getting into the details of investigations. The various kinds of telemetry data and their processing levels are presented, giving an insight about the variety of data sets, that are downlinked from the satellites. The investigations cover various major topics. These reach from different models to assess the absolute laser frequency, which sets the scale to convert the raw phase measurements into corresponding inter-satellite displacements, and comprise a detailed investigation of the carrier to noise ratio, which provides information about the signal quality. Furthermore, the laser’s beam properties in the far-field are investigated by means of the intensity and the phasefront. These investigations even lead to a proposal for a new scan pattern, which has actually been performed. Last but not least, a comprehensive assessment of the LRI spectrum was performed, which reveals correlation between the satellite’s attitude and orbit control system (AOCS), i.e. the star cameras for attitude determination and thruster activations for attitude control, and the ranging signal, measured by the LRI. In summary, this thesis is concerned with several aspects of the LRI characterization and data analysis. Since the overall data quality and sensitivity of the LRI exceeds the needs and expectations for the current gravimetric mission, many of the discussed effects are rather of academic interest, e.g. to deepen the instrument understanding of the LRI team and for the development of future missions in the field of geodesy or the space-based gravitational wave detection (LISA mission)

Space-Efficient Algorithms and Verification Schemes for Graph Streams

Structured data-sets are often easy to represent using graphs. The prevalence of massive data-sets in the modern world gives rise to big graphs such as web graphs, social networks, biological networks, and citation graphs. Most of these graphs keep growing continuously and pose two major challenges in their processing: (a) it is infeasible to store them entirely in the memory of a regular server, and (b) even if stored entirely, it is incredibly inefficient to reread the whole graph every time a new query appears. Thus, a natural approach for efficiently processing and analyzing such graphs is reading them as a stream of edge insertions and deletions and maintaining a summary that can be (a) stored in affordable memory (significantly smaller than the input size) and (b) used to detect properties of the original graph. In this thesis, we explore the strengths and limitations of such graph streaming algorithms under three main paradigms: classical or standard streaming, adversarially robust streaming, and streaming verification. In the classical streaming model, an algorithm needs to process an adversarially chosen input stream using space sublinear in the input size and return a desired output at the end of the stream. Here, we study a collection of fundamental directed graph problems like reachability, acyclicity testing, and topological sorting. Our investigation reveals that while most problems are provably hard for general digraphs, they admit efficient algorithms for the special and widely-studied subclass of tournament graphs. Further, we exhibit certain problems that become drastically easier when the stream elements arrive in random order rather than adversarial order, as well as problems that do not get much easier even under this relaxation. Furthermore, we study the graph coloring problem in this model and design color-efficient algorithms using novel parameterizations and establish complexity separations between different versions of the problem. The classical streaming setting assumes that the entire input stream is fixed by an adversary before the algorithm reads it. Many randomized algorithms in this setting, however, fail when the stream is extended by an adaptive adversary based on past outputs received. This is the so-called adversarially robust streaming model. We show that graph coloring is significantly harder in the robust setting than in the classical setting, thus establishing the first such separation for a ``natural\u27\u27 problem. We also design a class of efficient robust coloring algorithms using novel techniques. In classical streaming, many important problems turn out to be ``intractable\u27\u27, i.e., provably impossible to solve in sublinear space. It is then natural to consider an enhanced streaming setting where a space-bounded client outsources the computation to a space-unbounded but untrusted cloud service, who replies with the solution and a supporting ``proof\u27\u27 that the client needs to verify. This is called streaming verification or the annotated streaming model. It allows algorithms or verification schemes for the otherwise intractable problems using both space and proof length sublinear in the input size. We devise efficient schemes that improve upon the state of the art for a variety of fundamental graph problems including triangle counting, maximum matching, topological sorting, maximal independent set, graph connectivity, and shortest paths, as well as for computing frequency-based functions such as distinct items and maximum frequency, which have broad applications in graph streaming. Some of our schemes were conjectured to be impossible, while some others attain smooth and optimal tradeoffs between space and communication costs

Research in Structures, Structural Dynamics and Materials, 1990

The Structural Dynamics and Materials (SDM) Conference was held on April 2 to 4, 1990 in Long Beach, California. This publication is a compilation of presentations of the work-in-progress sessions and does not contain papers from the regular sessions since those papers are published by AIAA in the conference proceedings