Parallel metaheuristic algorithm for exam timetabling

The Course Timetabling problem is one of the most difficult combinatorial problems that arises with a University. The main objective of this problem is to obtain a timetable that minimises student conflicts between assigned activities. This is a discrete combinatorial problem that can be extremely difficult to solve for a human expert so computational heuristics are usually implemented in order to find good solutions within a reasonable time. With the advent of multi-core and hyper-threading technologies, parallel heuristics can speed up the solution process and with a proper parallel design these heuristics can improve the quality of solutions with the same number of Fitness evaluations than sequential algorithms. This paper explores the implementation of a parallel set of heuristic algorithms based on genetic algorithms, Scatter Search and discrete PSO for CTTP problem. Our experiments used as benchmark set instances from ITC2007 Track 2. Also the algorithms described in this paper make use of a layer of independence called methodology of design in order to be easily adaptable to new instances. Every parallel algorithm is compared against its sequential counterpart through speed-up metrics like Weak speed-up proposed by Alba et al.

Data partitioning algorithms aiming to minimize the execution time and the energy of computations in self-adaptable data-parallel applications on modern extreme-scale multicore platforms must address two critical challenges. First, they must take into account the new complexities inherent in these platforms such as severe resource contention and non-uniform memory access. Second, they must have low practical runtime and memory costs. The sequential data partitioning algorithms addressing the first challenge have a theoretical time complexity of O( $m * m * p * p$ ) where $m$ is the number of points in the discrete speed/energy function and $p$ is the number of available processors. They, however, exhibit high practical runtime cost and excessive memory footprint, therefore, rendering them impracticable for employment in self-adaptable applications executing on extreme-scale multicore platforms. We present, in this paper, the parallel data partitioning algorithms that address both the challenges. They take as input the functional models of performance and energy consumption against problem size and output workload distributions, which are globally optimal solutions. They have a low time complexity of O( $m * m * p$ ) thereby providing a linear speedup of O( $p$ ) and low memory complexity of O( $n$ ) where $n$ is the workload size expressed as a multiple of granularity. They employ dynamic programming approach, which also facilitates the easier integration of performance and energy models of communications. We experimentally study the practical cost of application of our algorithms in two data-parallel applications, matrix multiplication and fast Fourier transform, on a cluster in Grid’5000 platform. We demonstrate that their practical runtime and memory costs are low making them ideal for employment in self-adaptable applications. We also show that the parallel algorithms exhibit tremendous speedups over the sequential algorithms. Finally, using theoretical analysis for a forecast exascale platform, we demonstrate that the parallel algorithms have negligible execution times compared to the matrix multiplication application executing on the platform.

Three diagnostic rapid test kits (Chembio HIV-1/2 Stat-Pak, Alere DetermineTM HIV-1/2, and Core HIV-1/2) for human immunodeficiency virus types 1 and 2 (HIV-1/2) antibodies detection were evaluated against a gold standard fourth generation ELISA (Dia-Pro). Hundred and seventy two serum samples were collected from Lulubriggs Health Center, University of Port Harcourt, Rivers State. All samples were tested for HIV-1/2 using the three rapid test (parallel algorithm and serial algorithm), and retested using the 4th generation ELISA. The results obtained were compared with those from the ELISA. The DetermineTM rapid strips had sensitivity of 66.7%, specificity of 100%, positive predictive value 100%, negative predictive value 89.4% and total agreement of 91.3%. Stat-pak and Core HIV-1/2 rapid strips both had sensitivity of 71.7%, specificity of 100%, positive predictive value 100%, and negative predictive value 90.7% and total agreement of 92.4%. Determine was mostly used by the participant laboratories 18 of 30 (60%). The performance evaluation of the testing algorithm showed that parallel algorithm is more accurate in comparison to serial algorithm. The result of this study reveals that most rapid test are less sensitive and the accuracy of serial testing algorithm is low, this implies that most acute HIV infection will be missed with rapid assay and false negative results will be reported as actual negative with serial algorithm.

Parallel and sequential transformations on digital images

High-resolution adaptive-optical systems with thousands to millions of pixels will most likely have to employ serial- or matrix-addressed spatial light modulators (e.g., microelectromechanical-system-on-VLSI spatial light modulators). We compare parallel gradient descent adaptive-optics algorithms with serial gradient descent algorithms running on serially addressed modulators. While serial algorithms have previously been shown to require more iterations than parallel algorithms, we show that, because of the limitations of the databus, each serial iteration of the algorithm on a serial modulator requires significantly less time to complete than a parallel iteration, thereby favoring the serial algorithm when time to convergence is used as the performance metric. Thus, such high-resolution serially addressed devices are generally better matched to the serial-update wavefront correction algorithm owing to the data load penalty imposed by the bandwidth-limited databus of these modulators.

To evaluate serologic testing algorithms for human immunodeficiency virus (HIV) based on a combination of rapid assays among persons with HIV-1 (non-B subtypes) infection, HIV-2 infection, and HIV-1-HIV-2 dual infections in Abidjan, Ivory Coast, a total of 1,216 sera with known HIV serologic status were used to evaluate the sensitivity and specificity of four rapid assays: Determine HIV-1/2, Capillus HIV-1/HIV-2, HIV-SPOT, and Genie II HIV-1/HIV-2. Two serum panels obtained from patients recently infected with HIV-1 subtypes B and non-B were also included. Based on sensitivity and specificity, three of the four rapid assays were evaluated prospectively in parallel (serum samples tested by two simultaneous rapid assays) and serial (serum samples tested by two consecutive rapid assays) testing algorithms. All assays were 100% sensitive, and specificities ranged from 99.4 to 100%. In the prospective evaluation, both the parallel and serial algorithms were 100% sensitive and specific. Our results suggest that rapid assays have high sensitivity and specificity and, when used in parallel or serial testing algorithms, yield results similar to those of enzyme-linked immunosorbent assay-based testing strategies. HIV serodiagnosis based on rapid assays may be a valuable alternative in implementing HIV prevention and surveillance programs in areas where sophisticated laboratories are difficult to establish.

Over the years many interesting and efficient parallel algorithms have been developed to solve a wide variety of problems, but not much attention has been paid to studying the inherent parallelism in sequential algorithms---i.e., understanding the depth of their dependence structure, and how shallow dependence structures might beused to develop efficient parallel implementations. In this talk I will describe recent work on analyzing the dependence depth of iterative sequential algorithms---ones that loop over a collection of elements. Many of these algorithms have deep dependence chains in the worst case, but shallow chains (polylog w.h.p.) if the elements are randomly ordered. Examples include many fundamental algorithms: the Knuth shuffle for random permutations, sorting by insertion into a binary search tree, greedy maximal independent set (MIS), greedy maximal matching, greedy graph-coloring, counting cycles in a permutation, incremental k-dimensional linear programming, and incremental 2d Delaunay triangulation. An advantage of the approach is that it can lead to very simple and efficient parallel algorithms. Our MIS algorithm, for example can be coded in a dozen or so lines, and is significantly faster than Luby's algorithm on modern multicore machines. Also the approach encourages snapping the view that sequential and parallel algorithms are distinct, and instead thinking of algorithms, in general, as collections of instructions with dependences among them.

Parallel algorithms developed for CAD problems today suffer from three important drawbacks. First, they are machine specific and tend to perform poorly on architectures other than the one for which they were designed. Second, they cannot use the latest advances in improved versions of the sequential algorithms for solving the problem. Third, the quality of results degrade significantly during parallel execution. We address these three problems for an important CAD application: standard cell placement. We have developed a parallel placement algorithm that is portable across a range of MIMD parallel architectures. The algorithm is part of the ProperCAD project which allows the development and implementation of a parallel algorithm such that it can be executed on a wide variety of parallel machines without any change to the source. The parallel placement algorithm is based on an existing implementation of the sequential simulated annealing algorithm, TimberWolfSC 6.0 (C. Sechen and A. Sangiovanni-Vincentelli, 1985).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Mainly designed for simulating the fluid motion, Smooth Particle Hydrodynamics (SPH) SPH has been widely applied to many fields, such as aerospace aircraft design and ship design. First, this paper presents an implementation of serial SPH method algorithm using the reflection method as the boundary condition. This paper describes entire process of SPH and gives detailed descriptions of some important procedures. Second, this paper realizes the corresponding parallel algorithm of SPH, which includes neighbor particle searching and building, physical attributes computation and other main SPH method procedures. Third, details of mapping different parts of the SPH serial algorithm to parallel algorithm on GPU with CUDA C as parallel programming language are also shown in this paper. Taking advantage of powerful computation ability of GPU, the experiment approximately achieves a 8X speedup that shows the high efficiency of GPU-based programming. The parallel algorithm not only achieves a high performance, but also promises a reliability result with a small error rate as the experiment result shows.

The process of merging two arbitrary partitions of a given finite set 𝒰 of n elements is known as coarsest refinement. In the COARSEST REFINEMENT PROBLEM we are given two arbitrary partitions 𝒳, 𝒴 of the set 𝒰 such that 𝒳 = {𝒳1, 𝒳2, ..., 𝒳 x} and 𝒴 = {𝒴1, 𝒴2, ..., 𝒴 y}, and determine a new partition 𝒵 = {𝒵1, 𝒵2, ..., 𝒵 z} such that each is a common non-empty subset of some 𝒳 a ∈ 𝒳 and some 𝒴 b ∈ 𝒴 and |𝒵| is as small as possible. This article describes a resource-efficient parallel algorithm to solve this problem. More specifically, we show that a coarsest refinement can be computed in O( t( n) + log n) parallel time using max{nlogn,p(n)} processors, where t( n) denotes the running time of a parallel stable sorting algorithm that uses p( n) processors on an EREW PRAM. This result depends on t( n) and p( n). We give a table that shows the best known time and processor complexities for a parallel stable sorting algorithm. If the parallel stable sorting algorithms by Ajtai et al., Cole, and Leighton are used, the coarsest refinement can be computed in O(log n) parallel time using n processors on an EREW PRAM. On the other hand, if the parallel stable sorting algorithm by Bahig et al. is used, the coarsest refinement can be computed in O(lognlog(nlogn)) parallel time using nlogn processors on an EREW PRAM. In addition, we show that on, a RAM machine, our parallel algorithm runs as asymptotically efficient as the fastest known sequential algorithm.

Electronic circuits: by E. J. Angelo, Jr. 450 pages, diagrams, 6 × 9 in. New York, McGraw-Hill Book Co. Inc., 1958.Price, $9.00.

The structure of the higher education market in 2012–2021 in 38 European countries was analyzed using concentration levels and Herfindahl-Hirschman indices based on the number of higher education institutions and their share in the QS World University Rankings, and the number of students. This market in 2021 has a low concentration: the 3 countries with the largest number of higher education institutions (Germany, Ukraine, France) covered about 36% of the market in total; the 3 countries with the largest number of universities in the QS (United Kingdom, Germany, Italy) – 5%; the 3 countries with the largest number of students (Germany, France, United Kingdom) – 37%; and the 3 countries with the largest number of foreign students (United Kingdom, Germany, France) – 5%. Using parametric and non-parametric comparison tests, it was found that although the number of higher education institutions and students does not generally depend on the population’s income level, the number of universities ranked in the QS and foreign students does. The correlation analysis revealed that GDP and GNI, population, and separately the employment and unemployment rates (for ranked universities and foreign students) are important factors that determine the uneven structure of the higher education market. The identified factors formed the basis for clustering countries using Ward’s hierarchical method, which revealed the clear existence of 3 clusters: the smallest of them accumulates the 4 largest European economies with the most ranked universities; the largest (24 countries) is quite diverse, which indicates relatively equal opportunities in the market and its unification. Acknowledgment Tetiana Vasylieva and Artem Artyukhov thank project 0122U000772, and Nadiia Artyukhova thanks project 0124U000545 for carrying out their part of this research.

In this chapter, we define the Boolean association rules and give a formulation of the problem of mining for such rules. Next, we devise sequential and parallel algorithms, based on search space partitioning, that solve this problem. In Section 2.1, we describe the basic objects involved in the problem of mining for association rules. We briefly discuss the relationship between the support/confidence measures and probability. We also discuss the relationship between the association rules and the logical implication. The procedure proposed in [S96] is considered to solve the mining problem. It consists of the following steps: (1) enumerate all frequent attribute sets, and (2) derive all association rules from this set. In Section 2.2, following and extending ideas proposed in [Z98], we identify the search space attached to the mining problem to the powerset 2 A , where A is the set of attributes involved. We define a family of equivalence relations over 2 A that are used in Section 2.3 to derive a partitioning procedure. The procedure is based on recursive bisection and consists of progressively splitting the powerset into finer and finer equivalence classes. Section 2.4 shows that the classes can be processed independently of one another. A join operator, stable with respect to the class partitioning, is proposed for this purpose. The join operator is next used within two partition-based algorithms for frequent attribute set enumeration: a sequential algorithm (Section 2.5) and a parallel one (Section 2.6). The latter requires both initial and dynamic load balancing. These issues are discussed in Section 2.6 where load balancing algorithms are given. Once the set of frequent attribute sets has been found, this set is processed in order to discover all association rules. This is the topic of Section 2.7 where two algorithms (a sequential algorithm and a parallel one) are described.

Academic institutions are moving toward automated management of the educational process. One aspect of this process is the exam scheduling. The large number of students, classes, professors, and venues renders the manual scheduling process tedious and useless. In this paper, we describe the efforts of the Arab East College for High Education in Saudi Arabia in scheduling exams in the least number of conflicts, among other constraints. We give the details for a two-stage solution approach; the first stage is a greedy algorithm and the second one is a genetic algorithm. The two algorithms work in tandem to generate the best exam timetable. Automation of this process has greatly reduced the number of conflicts, exam days, and the required venues.

Building on Kahn and Plotkin's theory of concrete data structures and sequential functions, Berry and Curien defined an intensional model of sequential algorithms between concrete data structures. In this paper we report on an attempt to develop a similar intensional model of concurrent computation. We present a notion of parallel algorithm between concrete data structures, together with suitable application and currying operations. We define an intensional strictness ordering on parallel algorithms, with respect to which application is well behaved (at first-order types). We define the input—output function computed by a parallel algorithm, and we show that every parallel algorithm computes a continuous function. Thus, a parallel algorithm may be viewed as a continuous function together with a parallel computation strategy. In contrast, a Berry—Curien sequential algorithm may be viewed as a sequential function together with a sequential computation strategy. The intensional strictness ordering on parallel algorithms corresponds to the pointwise ordering on the functions they compute, in the same sense that the set-inclusion ordering used by Berry and Curien on sequential algorithms corresponds to the stable ordering on the functions they compute. We believe that the ideas and results presented here constitute a first step towards a fuller understanding of the intensional semantics of parallelism, even though the model presented here is not yet general enough to provide a satisfactory account of higher-order algorithms, and lacks a notion of composition for algorithms. We present some ideas for overcoming these deficiencies and some directions for further research.