The last 20 years have seen enormous progress in the design of algorithms, but very little of it has been put into practice, even within academia; indeed, the gap between theory and practice has continuously widened over these years. Moreover, many of the recently developed algorithms are very hard to characterize theoretically and, as initially described, suffer from large running-time coefficients. Thus the algorithms and data structures community needs to return to implementation as the standard of value; we call such an approach Experimental Algorithmics. Experimental Algorithmics studies algorithms and data structures by joining experimental studies with the more traditional theoretical analyses. Experimentation with algorithms and data structures is proving indispensable in the assessment of heuristics for hard problems, in the design of test cases, in the characterization of asymptotic behavior of complex algorithms, in the comparison of competing designs for tractabl...

A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool in biological, pharmaceutical, and medical research. Phylogeny reconstruction from molecular data is very difficult: almost all optimization models give rise to NP-hard (and thus computationally intractable) problems. Yet approximations must be of very high quality in order to avoid outright biological nonsense. Thus many biologists have been willing to run farms of processors for many months in order to analyze just one dataset. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing phylogenetic algorithms, as well as help designers produce better algorithms. We present an overview of algorithm engineering techniques, illustrating them with an application to the "breakpoint analysis" method of Sankoff et al., which resulted in the GRAPPA software suite. GRAPPA demonstrated a speedup in running time by over eight orders of magnitude over the original implementation on a variety of real and simulated datasets. We show how these algorithmic engineering techniques are directly applicable to a large variety of challenging combinatorial problems in computational biology.

The last twenty years have seen enormous progress in the design of algorithms, but little of it has been put into practice. Because many recently developed algorithms are hard to characterize theoretically and have large running-time coefficients, the gap between theory and practice has widened over these years. Experimentation is indispensable in the assessment of heuristics for hard problems, in the characterization of asymptotic behavior of complex algorithms, and in the comparison of competing designs for tractable problems. Implementation, although perhaps not rigorous experimentation, was characteristic of early work in algorithms and data structures. Donald Knuth has throughout insisted on testing every algorithm and conducting analyses that can predict behavior on actual data; more recently, Jon Bentley has vividly illustrated the difficulty of implementation and the value of testing. Numerical analysts have long understood the need for standardized test suites to ensure robustness, precision and efficiency of numerical libraries. It is only recently, however, that the algorithms community has shown signs of returning to implementation and testing as an integral part of algorithm development. The emerging disciplines of experimental algorithmics and algorithm engineering have revived and are extending many of the approaches used by computing pioneers such as Floyd and Knuth and are placing on a formal basis many of Bentley's observations. We reflect on these issues, looking back at the last thirty years of algorithm development and forward to new challenges: designing cache-aware algorithms, algorithms for mixed models of computation, algorithms for external memory, and algorithms for scientific research.

We report on implementation and a modest experimental evaluation of a recently introduced priority-queue data structure. The new data structure is designed to take advantage of fast operations on machine words and, as appropriate, reduced key-universe size and/or tolerance of approximate answers to queries. In addition to standard priority-queue operations, the data structure also supports successor and predecessor queries. Our results suggest that the data structure is practical and can be faster than traditional priority queues when holding a large number of keys, and that tolerance for approximate answers can lead to signi cant increases in speed.

this paper we study the performance of the very first tournament based complete binary tree. We focus on discrete-event simulation and our results show that this unknown predecessor of heaps can be a more efficient alternative to the fastest pending event set implementations reported in the literature. We also extend the idea of binary tournaments to a (2; L)-tournament structure which exhibits the property of delaying the processing of events with larger timestamps whilst it keeps similar theoretical performance bounds to the native (2; 1)-structure or CBT. This property can be certainly useful in systems where many pending events are expected to be deleted or rescheduled during the simulation. 2 Tournament trees

Computer-based discrete event simulation is an important design and analysis tool in many different application areas. Traditionally, discrete event simulation has been performed in a sequential manner, but the size and complexity of many of today's simulation models demand a move towards parallel execution. Distributed simulation explores the potential parallelism inherent in many simulation applications by modelling events as time-stamped messages which are exchanged between the logical processes that represent the physical objects of the application. This paper describes the porting of the Time Warp Operating System (TWOS) onto a network of transputers. TWOS is a special purpose operating system, designed to support distributed simulation, originally developed at the Jet Propulsion Laboratory. The paper discusses the way in which particular features of the transputer make it suitable for distributed simulation and presents experimental results which demonstrate the spe...

For many years, there has been tremendous interest in methods to make computation more reliable. In this thesis, we explore various techniques that can be implemented in software to help insure the correctness of the output of a program. The basic tool we use is a generalization of the notion of a program checker called a certifier. A certifier is given intermediate computations from a program computing an answer in an effort to simplify the checking process. The certifier is constructed in such a way that even if the intermediate computations it is given are incorrect, the certifier will never accept an incorrect output. We have constructed certifiers and program checkers for several common abstract data types including mergeable priority queues and splittable priority queues. We have also constructed a certifier for an abstract data type that allows approximate nearest neighbor queries to be performed efficiently. We have implemented and experimentally evaluated some of these algorithms. In the parallel domain, we have developed both general and problem specific techniques for certifying parallel computation. Lastly, we have formally proven correct a certifier for sorting, and have analyzed the advantages of using certifiers in conjunction with formal program verification techniques. This work forms a thesis presented by Jonathan D. Bright to the faculty of the Department of Computer Science, at the Johns Hopkins University, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, under the supervision of Professor Gregory F. Sullivan. iii Acknowledgements I would like to thank my advisor, Gregory Sullivan, for giving me an excellent research topic for my thesis, and for vastly improving my writing skills during my stay at Hopkins. Also, ...

The splay-prefix algorithm is one of the simplest and fastest adaptive data compression algorithms based on the use of a prefix code. The data structures used in the splay-prefix algorithm can also be applied to arithmetic data compression. Applications of these algorithms to encryption and image processing are suggested.