OPTIMIZATION APPROACHES TO MPI AND AREA MERGING-BASED PARALLEL BUFFER ALGORITHM

On buffer zone construction, the rasterization-based dilation method inevitablyintroduces errors, and the double-sided parallel line method involves a series ofcomplex operations. In this paper, we proposed a parallel buffer algorithm based onarea merging and MPI (Message Passing Interface) to improve the performances ofbuffer analyses on processing large datasets. Experimental results reveal that thereare three major performance bottlenecks which significantly impact the serial andparallel buffer construction efficiencies, including the area merging strategy, thetask load balance method and the MPI inter-process results merging strategy.Corresponding optimization approaches involving tree-like area merging strategy, the vertex number oriented parallel task partition method and the inter-processresults merging strategy were suggested to overcome these bottlenecks. Experimentswere carried out to examine the performance efficiency of the optimized parallelalgorithm. The estimation results suggested that the optimization approaches couldprovide high performance and processing ability for buffer construction in a clusterparallel environment. Our method could provide insights into the parallelization ofspatial analysis algorithm

Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function - i.e. a ratio of two polynomials - of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGSs performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa

A Mobile Wireless Channel State Recognition Algorihm: Introduction, Definition, and Verification - Sensing for Cognitive Environmental Awareness

This research includes mobile wireless systems limited by time and frequency dispersive channels. A blind mobile wireless channel (MWC) state recognition (CSR) algorithm that detects hidden coherent nonselective and noncoherent selective processes is verified. Because the algorithm is blind, it releases capacity based on current channel state that traditionally is fixed and reserved for channel gain estimation and distortion mitigation. The CSR algorithm enables cognitive communication system control including signal processing, resource allocation/deallocation, or distortion mitigation selections based on channel coherence states. MWC coherent and noncoherent states, ergodicity, stationarity, uncorrelated scattering, and Markov processes are assumed for each time block. Furthermore, a hidden Markov model (HMM) is utilized to represent the statistical relationships between hidden dispersive processes and observed receive waveform processes. First-order and second-order statistical extracted features support state hard decisions which are combined in order to increase the accuracy of channel state estimates. This research effort has architected, designed, and verified a blind statistical feature recognition algorithm capable of detecting coherent nonselective, single time selective, single frequency selective, or dual selective noncoherent states. A MWC coherence state model (CSM) was designed to represent these hidden dispersive processes. Extracted statistical features are input into a parallel set of trained HMMs that compute state sequence conditional likelihoods. Hard state decisions are combined to produce a single most likely channel state estimate for each time block. To verify the CSR algorithm performance, combinations of hidden state sequences are applied to the CSR algorithm and verified against input hidden state sequences. State sequence recognition accuracy sensitivity was found to be above 99% while specificity was determined to be above 98% averaged across all features, states, and sequences. While these results establish the feasibility of a MWC blind CSR algorithm, optimal configuration requires future research to further improve performance including: 1) characterizing the range of input signal configurations, 2) waveform feature block size reduction, 3) HMM parameter tracking, 4) HMM computational complexity and latency reduction, 5) feature soft decision combining, 6) recursive implementation, 7) interfacing with state based mobile wireless communication control processes, and 8) extension to wired or wireless waveform recognition

Sorting and Order Statistics

We will analyze the number of comparisons needed to solve the problem of sorting n distinct items into ascending order, and the related problem of selecting the kth smallest item from an unsorted list of n items. Section 1 examines mergesort, an algorithm with excellent worst case performance, and Section 2 examines quicksort, an algorithm with excellent average case performance. Section 3 contains some decision tree lower bounds on the number of comparisons used by any comparison-based sorting algorithm in the worst and average cases. Section 4 contains two linear-time selection algorithms, one average case and one worst case, and a lower bound based on the adversary method. 1 Mergesort Mergesort is a sorting algorithm that runs in O(n log n) time and makes ndlog ne + O(n) comparisons in the worst case. We begin in Section 1.1 by deriving a recurrence relation for the number of comparisons used by mergesort. We then apply some standard techniques for bounding recurrence relations that contain oors and ceilings to get a sequence of successively better approximations to the number of comparisons used by mergesort. Rounding n up to the next power of 2 in Section 1.2 will give usanupperboundofof2ndlog ne +O(n) comparisons. Techniques for simplifying expressions with multiple oor and ceiling operators in Section 1.3 will give us an upper bound of ndlog ne comparisons. Finally, in Section 1.4 we will verify by mathematical induction that the exact number of comparisons used by mergesort is exactly ndlog ne;2 dlog ne +1. 1.1 The Algorithm Consider the following algorithm for sorting a list L of elements from a totally ordered set: divide the list as evenly as possible into two sublists L 1 and L 2, sort each of L 1 and L 2 recursively, and then merge them together. function mergesort(L � n) comment sort a list L of n objects 1. if n 1 then return(L) else 2. break L into 2 lists L 1�L 2 of almost equal size 3. return(merge(mergesort(L 1 � dn=2e), mergesort(L 2 � bn=2c))