Abstract: Adjoints are an important computational tool for large-scale sensitivity evaluation, uncertainty quantification, and derivative-based optimization. An essential component of their performance is the storage/recomputation balance in which efficient adjoint checkpointing strategies play a key role. We introduce a novel asynchronous two-level adjoint checkpointing scheme for numerical time discretizations targeted at large-scale numerical simulations. The checkpointing scheme combines bandwidth-limited disk checkpointing and binomial memory checkpointing. Based on assumptions about the target petascale systems, which we later demonstrate to be realistic on the IBM Blue Gene/Q system Mira, we create a model of the predicted performance of the adjoint computation and validate it using the highly scalable Navier-Stokes spectral-element solver Nek5000 on small to moderate subsystems of the Mira supercomputer.

Abstract: The k-medoids methods for modeling clustered data have many desirable properties such as robustness to noise and the ability to use non-numerical values, however, they are typically not applied to large datasets due to their associated computational complexity. In this paper, we present AGORAS, a novel heuristic algorithm for the k-medoids problem where the algorithmic complexity is driven by, k, the number of clusters, rather than, n, the number of data points. Our algorithm attempts to isolate a sample from each individual cluster within a sequence of uniformly drawn samples taken from the complete data. As a result, computing the k-medoids solution using our method only involves solving k trivial sub-problems of centrality. This allows our algorithm to run in comparable time for arbitrarily large datasets with same underlying density distribution. We evaluate AGORAS experimentally against PAM and CLARANS -- two of the best-known existing algorithms for the k-medoids problem -- across a variety of published and synthetic datasets. We find that AGORAS outperforms PAM by up to four orders of magnitude for data sets with less than 10,000 points, and it outperforms CLARANS by two orders of magnitude on a dataset of just 64,000 points. Moreover, we find in some cases that AGORAS also outperforms in terms of cluster quality.

Abstract: The aim of this paper is to further develop the original method proposed by the authors in their previous publications and submitted as a patent to shape frequency response of a vibrating plate according to precisely dened demands. The method is based on modeling the plate together with additional masses and ribs, and applying a sophisticated optimization algorithm, which issues arrangement of the masses and ribs. It has a very high practical potential. It can be used to improve acoustic radiation of the plate for required frequencies or enhance acoustic isolation of noise barriers and device casings. It can be utilized for both passive and active control. For the latter case it allows at the same time to optimally arrange actuators and sensors. In the paper there are presented, compared and discussed simulation results of the method for a plate with dierent boundary conditions: simply supported, fully-clamped and elastically restrained against rotation (corresponding to a mounting in a real device casing). Proposed optimization criteria are followed from practical scenarios, where precise modication of a vibrating plate frequency response is desired. The application of the proposed method for active control is also shown. The important additional outcome of the paper are guidelines on de- signing device casings in terms of rigidity in order to obtain their required vibration and noise isolation features.

Abstract: Partial Update LMS (PU LMS) algorithms started to play an important role in adaptive processing of sound. Due to reduction of computational power demands, these algorithms allow to use longer adaptive filters, and therefore achieve better results in adaptive filtering applications, e.g., system identification, adaptive line enhancement (ALE), and active noise control (ANC). There are two main groups of PU algorithms: data-independent algorithms and data-dependent algorithms. While application of an algorithm belonging to the first group almost always results in a degradation of performance, application of data-dependent PU algorithms may even result in an increase of the performance, compared with full parameters update. However, the latter group of algorithms requires sorting. A number of updated parameters is the factor that allows to decide how much of performance should be sacrificed to obtain computational power savings. In the extreme case only one filter tap out of possilbly hundreds is updated during each sampling period. The goal of this paper is to show extensive simulations prooving that careful selection of this one tap results in a useful and well performing algorithm, even in the demanding application of active noise control. As a final step, the simulations are confirmed in laboratory experiments

Abstract: This paper provides a comprehensive and rigorous study of a novel collision resolution algorithm for wireless sensor networks: 2CS-WSN. It is specifically designed to be used during the contention phase of IEEE 802.15.4. This algorithm has been modelled in terms of discrete time Markov chains (DTMCs) and, using the probabilistic symbolic model checker PRISM, correctness properties and different operation modes of the algorithm have been studied. Moreover, different model abstractions have been used in order to identify any inconsistencies or ambiguities, and to prove interesting properties for non-trivial, practical and relevant scenarios. Finally, since the biggest source of energy waste is the collision, this paper conducts a wide study of energy saving in this algorithm.