In this paper we consider the problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles. This is a common operation that arises in many agent-based simulation studies, and is of central importance in the context of High Level Architecture (HLA), where it is at the core of the Data Distribution Management (DDM) service. Several realizations of the DDM service have been proposed; however, many of them are either inefficient or inherently sequential. We propose a parallel version of the Sort-Based Matching algorithm for shared-memory multiprocessors. Sort-Based Matching is one of the most efficient serial algorithms for the DDM problem, but is quite difficult to parallelize because of data dependencies. We describe the algorithm and compute its asymptotic running time; we complete the analysis by assessing its performance and scalability through extensive experiments on two commodity multicore systems based on a dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.
Among the many matching algorithms that have been proposed for enumerating all intersections among subscription and update extents, the SBM @cite_16 proved to be very efficient. SBM solves the region matching problem in one dimension; SBM first sorts the endpoints, and then scans the sorted set. @cite_13 , SBM has been extended to deal with dynamic environments in which extents are dynamic (both in terms of placement and size). On the other hand, SBM has the drawback that it can not be trivially parallelized due to the presence of a sequential scan phase that is intrinsically serial. This is a serious limitation since the most of modern processing architectures are multi or many-cores.
@cite_16 @cite_13
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In distribution simulation based on High-level architecture (HLA), data distribution management (DDM) is one of HLA services for the purpose of filtering the unnecessary data transferring over the network. DDM admits the sending federates and the receiving federates to express their interest using update regions and subscription regions in a multidimensional routing space. There are several matching algorithms to obtain overlap information between the update regions and subscription regions. When the number of regions increase sharply, the matching process is time consuming. However, the existing algorithms is hard to be parallelized to take advantage of the computing capabilities of multi-core processors. To reduce the computational overhead of region matching, we propose a parallel algorithm based on order relation to accelerate the matching process. The new matching algorithm adopts divide-and-conquer approach to divide the regions into multiple region bound sublists, each of which comprises parts of region bounds. To calculate the intersection inside and amongst the region bound sublists, two matching rules are presented. This approach has good performance since it performs region matching on the sublists parallel and does not require unnecessary comparisons within regions in different sublists. Theoretical analysis has been carried out for the proposed algorithm and experimental result shows that the proposed algorithm has better performance than major existing DDM matching algorithms. Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching(ITM) algorithm for computing intersections among d-rectangles. ITMis based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions(brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching, moreover, the parallel implementation provides good speed upon multicore processors. This paper proposes a new approach for the discovery of common patterns in a small set of images by region matching. The issues in feature robustness, matching robustness and noise artifact are addressed to delve into the potential of using regions as the basic matching unit. We novelly employ the many-to-many (M2M) matching strategy, specifically with the Earth Mover's Distance (EMD), to increase resilience towards the structural inconsistency from improper region segmentation. However, the matching pattern of M2M is dispersed and unregulated in nature, leading to the challenges of mining a common pattern while identifying the underlying transformation. To avoid analysis on unregulated matching, we propose localized matching for the collaborative mining of common patterns from multiple images. The patterns are refined iteratively using the expectation-maximization algorithm by taking advantage of the ''crowding'' phenomenon in the EMD flows. Experimental results show that our approach can handle images with significant image noise and background clutter. To pinpoint the potential of Common Pattern Discovery (CPD), we further use image retrieval as an example to show the application of CPD for pattern learning in relevance feedback. We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method lies in invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a window of successive frames. This enables the algorithm to run in real-time, without fluctuations in the latency over long durations. We evaluate the method in large indoor and outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a low-power, mobile computer suitable for onboard computation on a quadrotor micro aerial vehicle.