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We introduce the first algorithm that we are aware of to employ Bloom filters for Longest Prefix Matching (LPM). The algorithm performs parallel queries on Bloom filters, an efficient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by prefix length. We show that use of this algorithm for Internet Protocol (IP) routing lookups results in a search engine providing better performance and scalability than TCAM-based approaches. The key feature of our technique is that the performance, as determined by the number of dependent memory accesses per lookup, can be held constant for longer address lengths or additional unique address prefix lengths in the forwarding table given that memory resources scale linearly with the number of prefixes in the forwarding table. Our approach is equally attractive for Internet Protocol Version 6 (IPv6) which uses 128-bit destination addresses, four times longer than IPv4. We present a basic version of our approach along with optimizations leveraging previous advances in LPM algorithms. We also report results of performance simulations of our system using snapshots of IPv4 BGP tables and extend the results to IPv6. Using less than 2Mb of embedded RAM and a commodity SRAM device, our technique achieves average performance of one hash probe per lookup and a worst case of two hash probes and one array access per lookup.

No abstract

We introduce the first algorithm that we are aware of to employ Bloom filters for Longest Prefix Matching (LPM). The algorithm performs parallel queries on Bloom filters, an efficient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by prefix length. We show that use of this algorithm for Internet Protocol (IP) routing lookups results in a search engine providing better performance and scalability than TCAM-based approaches. The key feature of our technique is that the performance, as determined by the number of dependent memory accesses per lookup, can be held constant for longer address lengths or additional unique address prefix lengths in the forwarding table given that memory resources scale linearly with the number of prefixes in the forwarding table. Our approach is equally attractive for Internet Protocol Version 6 (IPv6) which uses 128-bit destination addresses, four times longer than IPv4. We present a basic version of our approach along with optimizations leveraging previous advances in LPM algorithms. We also report results of performance simulations of our system using snapshots of IPv4 BGP tables and extend the results to IPv6. Using less than 2Mb of embedded RAM and a commodity SRAM device, our technique achieves average performance of one hash probe per lookup and a worst case of two hash probes and one array access per lookup.

We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

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We present a novel method for recovering the 3D-line structure of a scene from multiple widely separated views. Traditional optimization-based approaches to line-based structure from motion minimize the error between measured line segments and the projections of corresponding 3D lines. In such a case, 3D lines can be optimized using a minimum of 4 parameters. We show that this number of parameters can be further reduced by introducing additional constraints on the orientations of lines in a 3D scene.In our approach, 2D-lines are automatically detected in images with the assistance of an EM-based vanishing point estimation method which assumes the existence of edges along mutally orthogonal vanishing directions. Each detected line is automatically labeled with the orientation (e.g. vertical, horizontal) of the 3D line which generated the measurement, and it is this additional knowledge that we use to reduce the number of degrees of freedom of 3D lines during optimization. We present 3D reconstruction results for urban scenes based on manually established feature correspondences across images.

Biosequence similarity search is an important application in modern molecular biology. Search algorithms aim to identify sets of sequences whose extensional similarity suggests a common evolutionary origin or function. The most widely used similarity search tool for biosequences is BLAST, a program designed to compare query sequences to a database. Here, we present the design of BLASTN, the version of BLAST that searches DNA sequences, on the Mercury system, an architecture that supports high-volume, high-throughput data movement off a data store and into reconfigurable hardware. An important component of application deployment on the Mercury system is the functional decomposition of the application onto both the reconfigurable hardware and the traditional processor. Both the Mercury BLASTN application design and its performance analysis are described. 1: IntroductionComputational search through large databases of DNA and protein sequence is a fundamental tool of modern molecular biology. Rapid advances in the speed and cost-effectiveness of DNA sequencing have led to an explosion in the rate at which new sequences, including entire mammalian genomes [35], are being generated. To understand the function and evolutionary history of an organism, biologists now seek to identify discrete biologically meaningful features in its genome sequence. A powerful approach to identify such features is comparative annotation, in which a query sequence, such as new genome, is compared to a large database of known biosequences. Database sequences exhibiting high similarity to the query, as measured by string edit distance [31], are hypothesized to derive from the same ancestral sequence as the query and in many cases to have the same biological function.BLAST, the Basic Local Alignment Search Tool [1], is the most widely used software for rapidly comparing a query sequence to a biosequence database. Although BLAST's algorithms are highly optimized for efficient similarity search, growth in the databases it uses is outpacing speed improvements in general-purpose computing hardware. For example, the National Center for Biological Information (NCBI) Genbank database grew exponentially between 1992 and 2003 with a doubling time of 12-16 months [24]. The problem is particularly acute for BLASTN, the BLAST variant used to compare DNA sequences, because each new genome sequenced from animals or higher plants produces between 10 8 and 10 10 bytes of new DNA sequence.One response to runaway growth in biosequence databases has been to distribute BLAST searches across multiple computers, each responsible for searching only part of a database. This approach requires both a substantial hardware investment and the ability to coordinate a {praveenk, jbuhler, roger, jbf, kg2, jarpith, jmlancas}@cse.wustl.edu. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript search across processors. An alternate approach that makes more parsimonious use of hardware is to build a specialized BLAST accelerator. By using an applic...