Prefix-suffix duplication

We consider a bio-inspired formal operation on words called prefix-suffix duplication which consists in the duplication of a prefix or suffix of a given word. The class of languages defined by the iterated application of the prefix-suffix duplication to a word is considered. We show that such a language is context-free if and only if the initial word contains just one letter. Moreover, every language in this class is semilinear and belongs to NL. We propose a 0(n2 logn) time and 0(n2 ) space recognition algorithm. Two algorithms are further proposed for computing the prefix-suffix duplication distance between two words, defined as the minimal number of prefix-suffix duplications applied to one of them in order to get the other one. The first algorithm runs in cubic time and uses quadratic space while the second one is more efficient, having 0(n2 logn) time complexity, but needs 0(n2 logn) space

Multiple jet impingement heat transfer characteristic: Experimental investigation of in-line and staggered arrays with crossflow

Heat transfer characteristics were obtained for configurations designed to model the impingement cooled midchord region of air cooled gas turbine airfoils. The configurations tested were inline and staggered two-dimensional arrays of circular jets with ten spanwise rows of holes. The cooling air was constrained to exit in the chordwise direction along the channel formed by the jet orifice plate and the heat transfer surface. Tests were run for chordwise jet hole spacings of five, ten, and fifteen hole diameters; spanwise spacings of four, six, and eight diameters; and channel heights of one, two, three, and six diameters. Mean jet Reynolds numbers ranged from 5000 to 50,000. The thermal boundary condition at the heat transfer test surface was isothermal. Tests were run for sets of geometrically similar configurations of different sizes. Mean and chordwise resolved Nusselt numbers were determined utilizing a specially constructed test surface which was segmented in the chordwise direction

Modeling Algorithm Performance on Highly-threaded Many-core Architectures

The rapid growth of data processing required in various arenas of computation over the past decades necessitates extensive use of parallel computing engines. Among those, highly-threaded many-core machines, such as GPUs have become increasingly popular for accelerating a diverse range of data-intensive applications. They feature a large number of hardware threads with low-overhead context switches to hide the memory access latencies and therefore provide high computational throughput. However, understanding and harnessing such machines places great challenges on algorithm designers and performance tuners due to the complex interaction of threads and hierarchical memory subsystems of these machines. The achieved performance jointly depends on the parallelism exploited by the algorithm, the effectiveness of latency hiding, and the utilization of multiprocessors (occupancy). Contemporary work tries to model the performance of GPUs from various aspects with different emphasis and granularity. However, no model considers all of these factors together at the same time. This dissertation presents an analytical framework that jointly addresses parallelism, latency-hiding, and occupancy for both theoretical and empirical performance analysis of algorithms on highly-threaded many-core machines so that it can guide both algorithm design and performance tuning. In particular, this framework not only helps to explore and reduce the runtime configuration space for tuning kernel execution on GPUs, but also reflects performance bottlenecks and predicts how the runtime will trend as the problem and other parameters scale. The framework consists of a pair of analytical models with one focusing on higher-level asymptotic algorithm performance on GPUs and the other one emphasizing lower-level details about scheduling and runtime configuration. Based on the two models, we have conducted extensive analysis of a large set of algorithms. Two analysis provides interesting results and explains previously unexplained data. In addition, the two models are further bridged and combined as a consistent framework. The framework is able to provide an end-to-end methodology for algorithm design, evaluation, comparison, implementation, and prediction of real runtime on GPUs fairly accurately. To demonstrate the viability of our methods, the models are validated through data from implementations of a variety of classic algorithms, including hashing, Bloom filters, all-pairs shortest path, matrix multiplication, FFT, merge sort, list ranking, string matching via suffix tree/array, etc. We evaluate the models\u27 performance across a wide spectrum of parameters, data values, and machines. The results indicate that the models can be effectively used for algorithm performance analysis and runtime prediction on highly-threaded many-core machines

Computing regularities in strings: A survey

The aim of this survey is to provide insight into the sequential algorithms that have been proposed to compute exact “regularities” in strings; that is, covers (or quasiperiods), seeds, repetitions, runs (or maximal periodicities), and repeats. After outlining and evaluating the algorithms that have been proposed for their computation, I suggest possibly productive future directions of research