Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field. He gives clear, lucid explanations of key results and ideas, with intuitive proofs, and provides critical examples and numerous illustrations to help elucidate the algorithms. Many of the results presented have been simplified and new insights provided. Of interest to theoretical computer scientists, operations researchers, and discrete mathematicians.

Approximation Algorithms

The success of the von Neumann model of sequential computation is attributable to the fact that it is an efficient bridge between software and hardware: high-level languages can be efficiently compiled on to this model; yet it can be effeciently implemented in hardware. The author argues that an analogous bridge between software and hardware in required for parallel computation if that is to become as widely used. This article introduces the bulk-synchronous parallel (BSP) model as a candidate for this role, and gives results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.

A bridging model for parallel computation

We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.

/pdf/a-guided-tour-to-approximate-string-matching-22fowlmq5b.pdf

A guided tour to approximate string matching

An edited volume containing data structures and algorithms for information retrieved including a disk with examples written in C. For programmers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents.

https://hornad.fei.tuke.sk/~genci/Vyucba/OOBDS/Archiv/Podklady/TExtoveIS&IR/Information%20Retrieval%20Data%20Structures%20&%20Algorithms.PDF

Information Retrieval: Data Structures and Algorithms

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area

/pdf/duplicate-record-detection-a-survey-4crkywvv1d.pdf

Duplicate Record Detection: A Survey

Highly Parallelizable Problems (Extended Abstract)

The authors address two fundamental problems in parallel algorithm design-parallel prefix sums and integer sorting-and show that both of them can be approximately solved very quickly on a randomized CRCW PRAM. In the case of prefix sums the approximation is in terms of the accuracy of the sums and in the case of integer sorting it is in terms of allowing some gaps between consecutive elements in the ordered list. By introducing approximation in these ways the authors are able to solve these problems in o(lg lg n) time, and thus avoid the near-logarithmic lower bounds by Beame and Hastad that hold for the exact versions of these problems. Nevertheless, they demonstrate that these approximations are strong enough to be used as subroutines in fast randomized algorithms for some well-known problems in parallel computational geometry. Perhaps the most succinct way to describe the power of the new tools which are presented is by observing that prior to this work it was known how to solve the interval allocation problem fast. The authors show how to solve the ordered version of the problem. >

Approximate parallel prefix computation and its applications

We present novel work-optimal PRAM algorithms for Burrows-Wheeler (BW) compression and decompression of strings over a constant alphabet. For a string of length n, the depth of the compression algorithm is O(log2n), and the depth of the corresponding decompression algorithm is O(log n). These appear to be the first polylogarithmic-time work-optimal parallel algorithms for any standard lossless compression scheme.The algorithms for the individual stages of compression and decompression may also be of independent interest: 1. a novel O(log n)-time, O(n)-work PRAM algorithm for Huffman decoding; 2. original insights into the stages of the BW compression and decompression problems, bringing out parallelism that was not readily apparent. We then mapped such parallelism in interesting ways to elementary parallel routines that have O(log n)-time, O(n)-work solutions, such as: (i) prefix-sums problems with an appropriately-defined associative binary operator for several stages, and (ii) list ranking for the final stage of decompression (inverse blocksorting transform).Companion work reports empirical speedups of up to 25x for compression and up to 13x for decompression. This reflects a speedup of 70x over recent work on BW compression on GPUs.

Truly parallel burrows-wheeler compression and decompression

Many-cores, processors with 100s of cores, are becoming increasingly popular in general-purpose computing, yet power is a limiting factor in their performance. In this paper, we compare the power and performance of two design points in the many-core processor domain. The XMT general-purpose processor provides significant runtime advantage on irregular parallel programs (e.g., graph algorithms). This was previously demonstrated and tied to its architecture choices and ease-of-programming. In contrast, current commercial GPUs excel at regular parallel programs that require high processing capability. In this work, we set the power envelope as a constraint and evaluate an envisioned 1024-core XMT processor against an NVIDIA GTX280 GPU considering various scenarios for estimating the power of the XMT chip. Even under worst-case assumptions and scenarios, simulations show that the XMT processor sustains its advantage over the GPU on irregular parallel programs, while not falling significantly behind on regular programs. The total energy spent per benchmark fits a similar pattern. Given that the two architectures target different types of parallelism, a future system can potentially utilize an XMT chip and a GPU chip in complementary roles.

/pdf/power-performance-comparison-of-single-task-driven-many-4xvswway4s.pdf

Power-Performance Comparison of Single-Task Driven Many-Cores

We present a parallel solution to the problem of determining the triconnected components of an undirected graph. We obtain significant speedups over the only published optimal (linear-time) serial implementation of a triconnected components algorithm running on a modern CPU. This is accomplished on the PRAM-inspired XMT many-core architecture. To our knowledge, no other parallel implementation of a triconnected components algorithm has been published for any platform.

Uzi Vishkin

Papers

Highly Parallelizable Problems (Extended Abstract)

Approximate parallel prefix computation and its applications

Truly parallel burrows-wheeler compression and decompression

Power-Performance Comparison of Single-Task Driven Many-Cores

Brief announcement: speedups for parallel graph triconnectivity