ProperPLACE: a portable parallel algorithm for standard cell placement

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.

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.

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.

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.

Finite state automata are of fundamental importance in theory as well as applications in computer science. This dissertation presents several parallel algorithms for processing of them, including minimizing the number of states for a given finite state automaton, finding homing sequences for a given finite state automaton, converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. Implementation of some algorithms, as well as their experimental results, are also given. For minimization of deterministic finite automata, we present a parallel algorithm and discuss its implementation on a practical parallel machine using data parallel and message passing paradigms. Its time complexity on a p processor EREW PRAM $(p\\le n)$ for inputs of size n is $O({n\\log\\sp2n\\over p}+\\log n\\ \\log p)$ uniformly on almost all instances. The work done by our algorithm is thus within a factor of $O(\\log n)$ of the best known sequential algorithm. The space used by our algorithm is linear in the input size. The actual resource requirements of our implementations are consistent with these estimates. For finding homing sequences of deterministic finite automata, we present both deterministic and randomized algorithms. Two randomized algorithms of time complexity $O(log\\sp2n)$ are given. One of the randomized algorithms produces a homing sequence of length $O(n\\ log\\sp2n)$ for almost all DFA's with n states. The deterministic algorithm is based on a well known sequential algorithm. The implementation of the deterministic parallel algorithm shows that it can handle randomly generated instances with millions of states, and all DFA's with thousands of states using currently available parallel machines. Finally, we present an efficient parallel algorithm for converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. The resulting automaton is natural representation of a regular expression and has some important and practical uses in the document processing area. The time complexity of the algorithm is $O(\\log n)$ and the number of processors used is $O(n\\sp2).$

The mathematical Е-operating method is intended for transformation of the of differential equations system to the tracking electric drive mathematical model equations system. In contrast to the known algorithms for numerical integration, for example, in the Matlab-Simulink, E-method allows to synthesize of the transfer function of tracking electric drives for both sequential and parallel differential algorithms for working in real time on the basis of both software and hardware shifting registers. It is important to note that these algorithms cannot be implemented using Matlab-Simulink. Parallel algorithm provides greater performance than serial algorithm. Parallel algorithm can be implemented "conveyor" method of using multiple coprocessors at the same time. Method is an alternative of the means available in Matlab-Simulink programme for description and analysis of the tracking electric drive model. In the first part of the article the synthesis algorithm using traditional means of differential calculus and Laplace transforms describes. In the second part of the article a solution of the same problem with the use of the Е-operating method presents. The results obtained can be used in mechatronics, robotics, automatic control theory, in the practice of building looking for tracking electric drive.

This paper presents a Parallel Data Mining Platform (PDMP), aiming at rapidly developing parallel data mining applications on cluster system. This platform consists of parallel data mining algorithm library, data warehouse, field knowledge base and platform middleware. The middleware is the kernel of the platform, which comprises data processing component, task manager, data manager, GUI, and so on. Taking advantage of cluster system, the middleware provides a convenient developing environment and effective bottom supports for implementation of parallel data mining algorithms. So far, the parallel data mining algorithm library has possessed several parallel algorithms, such as classification, clustering, association rule mining, sequence pattern mining and so on. With a register mechanism, the parallel data mining algorithm library is easy to be extended by users. Experiment results with the use of the PDMP show that there is a substantial performance improvement due to the cooperation of the middleware and corresponding parallel strategy.

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.


 
 
 
 Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.
 
 
 
 

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

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.

This paper presents a general methodology for the conversion of the sequential evolutionary algorithms into parallel evolutionary algorithms using MPI. The methodology is showed by applying it to a combinatorial optimization problem well-known as Water Distribution Networks. Also a comparison of two parallelization approaches is done, showing the advantages and disadvantages of each approach. The implementation of this parallel algorithm reduces the necessary time to obtain good solutions for NP-Complete problems as Water Distribution Networks.

In this work we describe a portable sequential and parallel algorithm based on Newton's method, for solving nonlinear systems. We used the GMRES iterative method to solve the inner iteration. To control the inner iteration as much as possible and avoid the oversolving problem, we also parallelized several forcing term criterions. We implemented the parallel algorithms using the parallel numerical linear algebra library SCALAPACK based on the MPI environment. Experimental results have been obtained using a cluster of Pentium II PC's connected through a Myrinet network. To test our algorithms we used three different test problems, the H-Chandrasekhar problem, computing the intersection point of several hyper-surfaces, and the Extended Rosenbrock Problem. The latter requires some improvements for the method to work with structured sparse matrices and chaotic techniques. The algorithm obtained shows a good scalability in most cases. This work is included in a framework tool we are developing where, given a problem that implies solving a nonlinear system, the best nonlinear method must be chosen to solve the problem. The method we present here is one of the methods we implemented.

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.