List-based Algorithm Research Articles

An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

Efficient approach of recent high utility stream pattern mining with indexed list structure and pruning strategy considering arrival times of transactions

One of various pattern mining techniques, the High Utility Pattern Mining (HUPM) is a method for finding meaningful patterns from non-binary databases by considering the characteristics of the items. Recently, new data continues to flow over time in diverse fields such as sales data of market, heartbeat sensor data, and social network service. Since these data have a feature that recently generated data have higher influence than the old data, research has been focused on how to efficiently extract hidden knowledge from time-sensitive databases. In this paper, we propose indexed list-based algorithm that mines recent high utility pattern considering the arrival time of inserted data in an environment where new data is continuously accumulated. In other words, to treat the importance of recent data higher than the that of old data, our algorithms reduces the utility values of old transactions according to the time the data is inserted by applying damped window model concept. Moreover, we carry out various experiments to compare our method with state-of-the-art algorithms using real and synthetic datasets in diverse circumstances. Experimental results show that our algorithm outperforms competitors in terms of execution time, memory usage, and scalability test.

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability.

Multi-agent list-based noising algorithm for protein structure prediction

Protein structure prediction (PSP) with ab initio model problem is still a challenge in bioinformatics on account of high computational complexity. To solve this problem within a limited time and resource, a multi-agent list-based nosing (MLBN) algorithm is presented. MLBN contains three main features. First, a flexible noising list is designed to adjust the solution acceptance condition according to the convergence. An adaptive multiple sampling strategy is included to provide a strong exploitation. A parallel framework explores the searching space in a more effective way. Compared to traditional Simulated Annealing (SA) algorithm, MLBN introduces only one extra parameter for the length of noising list and it is insensitive to specific problems. Conducted experiments in a range of protein sequences indicate MLBN performs better than, or at least is comparable with, several state-of-the-art algorithms for PSP.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

A Tabu List-Based Algorithm for Capacitated Multilevel Lot-Sizing with Alternate Bills of Materials and Co-Production Environments

The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.

Performance Analysis of List Scheduling Algorithms by Random Synthetic DAGs

Efficient utilization of high performance heterogeneous parallel computing systems requires efficient application scheduling algorithms. List based scheduling algorithms have the ability to generate the most efficient schedules. This type of task scheduling algorithm can be considered as NP-Complete problem. The applications are assumed as multiple tasks interconnected to each other with precedence constraint. Directed Acyclic Graph (DAG) is used to present these tasks. Each of these tasks can be executed on any processor with its own execution timeline. In this paper, four list-based algorithms (HEFT, CPOP, PEFT, EXT_HEFT) have been considered for performance analysis by considering randomly generated synthetic DAGs as input. Further, these algorithms have been compared in terms of the Schedule Length Ratio (SLR), speedup, efficiency, and running time of algorithms. We have also considered the effects on SLR value of algorithms by changing the value of CCR (Communication to Computation Ratio) of the graphs.

Analysing Big Social Graphs Using a List-based Graph Folding Algorithm

In this paper, we explore the ways to represent big social graphs using adjacency lists and edge lists. Furthermore, we describe a list-based algorithm for graph folding that makes possible to analyze conditionally infinite social graphs on resource constrained mobile devices. The steps of the algorithm are (a) to partition, in a certain way, the graph into clusters of different levels, (b) to represent each cluster of the graph as an edge list, and (c) to absorb the current cluster by the cluster of the next level. The proposed algorithm is illustrated by the example of a sparse social graph.

Communication-aware task scheduling algorithm for heterogeneous computing

In this paper, we propose a new task scheduling algorithm for heterogeneous computing platforms, called communication-aware earliest finish time (CEFT). It combines the features of list-scheduling and task-duplication, where task priority is assigned according to communication ratio (CR), a notion defined to represent communication cost. We also present a duplication mechanism, which cooperates with CR to reduce the communication overhead. The time complexity of the algorithm is O(v2 • p), for v tasks and p processors. The experiment results show that CEFT algorithm improves performance by 11% compared with the state-of-the-art list-based algorithm PEFT, and 15.6% compared with duplication-based algorithm HDCPD, in terms of scheduling length ratio. CEFT is the best algorithm compared with PEFT, HDCPD and HEFT regarding to efficiency and average surplus time as well.

Communication-aware task scheduling algorithm for heterogeneous computing

In this paper, we propose a new task scheduling algorithm for heterogeneous computing platforms, called communication-aware earliest finish time (CEFT). It combines the features of list-scheduling and task-duplication, where task priority is assigned according to communication ratio (CR), a notion defined to represent communication cost. We also present a duplication mechanism, which cooperates with CR to reduce the communication overhead. The time complexity of the algorithm is O(v2 • p), for v tasks and p processors. The experiment results show that CEFT algorithm improves performance by 11% compared with the state-of-the-art list-based algorithm PEFT, and 15.6% compared with duplication-based algorithm HDCPD, in terms of scheduling length ratio. CEFT is the best algorithm compared with PEFT, HDCPD and HEFT regarding to efficiency and average surplus time as well.

A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.

Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.

Multi-agent list-based threshold-accepting algorithm for numerical optimisation

Traditional list-based threshold-accepting LBTA algorithm is similar with simulated annealing SA algorithm, depends on an intense local search method, and utilises a list filling procedure with threshold values to search the solution space effectively. Inspired by the learning ability of particle swarm optimisation PSO, multi-agent LBTA MLBTA involves the learning knowledge to guide its sampling, explores the solution space in a co-evolution mode. Compare with multi-agent SA MSA algorithm adapting the same local search version, MLBTA incorporates a dynamic list of threshold values which is adapted according to the topology of the solution space and tunes only one parameter. Dispense with sophisticated parameters as MSA, MLBTA balances the intensification and diversification iteratively. Computational results on functions optimisation and protein structure prediction PSP problems show that MLBTA algorithm achieves better or comparable performances with MSA.

List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table

Efficient application scheduling algorithms are important for obtaining high performance in heterogeneous computing systems. In this paper, we present a novel list-based scheduling algorithm called Predict Earliest Finish Time (PEFT) for heterogeneous computing systems. The algorithm has the same time complexity as the state-of-the-art algorithm for the same purpose, that is, O(v2.p) for v tasks and p processors, but offers significant makespan improvements by introducing a look-ahead feature without increasing the time complexity associated with computation of an optimistic cost table (OCT). The calculated value is an optimistic cost because processor availability is not considered in the computation. Our algorithm is only based on an OCT that is used to rank tasks and for processor selection. The analysis and experiments based on randomly generated graphs with various characteristics and graphs of real-world applications show that the PEFT algorithm outperforms the state-of-the-art list-based algorithms for heterogeneous systems in terms of schedule length ratio, efficiency, and frequency of best results.

A hybrid heuristic–genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.

A list-based algorithm for evaluation of large deviation functions

As the analog of the free energy for dynamical trajectories, the large deviation functionplays a central role in the statistical mechanics of systems far from equilibrium. Here, weidentify numerical issues that can arise when the model of interest evolves according to acontinuous-time dynamics. This analysis motivates the introduction of an algorithm inwhich a list of previously visited states is used to resample the distribution of interest. Wediscuss the convergence properties of our algorithm in detail and demonstrate itsapplication to the single-site zero-range process and the many-site totally asymmetricexclusion process.

A BICM-IDD scheme for non-coherent MIMO communication

A bit-interleaved coded modulation (BICM) scheme with iterative (soft) demapping and decoding (IDD) is developed for non-coherent communication over a multiple-input multiple-output (MIMO) channel. The scheme exploits the underlying Grassmannian geometry of the signalling scheme that approaches the ergodic capacity of the non-coherent model at high signal-to-noise ratios. In particular, this geometry guides the construction of the constellation and the mapper at the transmitter, and gives rise to a computationally-efficient list-based demapping algorithm. The incorporation of a scheme that enables the decoder to augment the demapping list virtually eliminates the mild performance degradation of the efficient demapper. Simulation results demonstrate that at high data rates the proposed scheme can provide significantly better performance than several training-based BICM-IDD schemes.

Algorithms for Deterministic Incremental Dependency Parsing

Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combined with treebank-induced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependency-based syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stack-based and list-based algorithms. In the former family, which is restricted to projective dependency structures, we describe an arc-eager and an arc-standard variant; in the latter family, we present a projective and a non-projective variant. For each of the four algorithms, we give proofs of correctness and complexity. In addition, we perform an experimental evaluation of all algorithms in combination with SVM classifiers for predicting the next parsing action, using data from thirteen languages. We show that all four algorithms give competitive accuracy, although the non-projective list-based algorithm generally outperforms the projective algorithms for languages with a non-negligible proportion of non-projective constructions. However, the projective algorithms often produce comparable results when combined with the technique known as pseudo-projective parsing. The linear time complexity of the stack-based algorithms gives them an advantage with respect to efficiency both in learning and in parsing, but the projective list-based algorithm turns out to be equally efficient in practice. Moreover, when the projective algorithms are used to implement pseudo-projective parsing, they sometimes become less efficient in parsing (but not in learning) than the non-projective list-based algorithm. Although most of the algorithms have been partially described in the literature before, this is the first comprehensive analysis and evaluation of the algorithms within a unified framework.

Blind acquisition of short burst with per-survivor processing

In this letter, the blind acquisition performance of PSP is discussed. Based on the theory of indistinguishable sequences (K.M. Chugg, 1998), modifications to conventional PSP are suggested that improve acquisition performance significantly. The effects of tree search and list-based algorithms on PSP are also discussed. In blind channel estimation, the performance of list-based algorithms is better than trellis-based algorithms and can be further enhanced using the proposed techniques. The blind acquisition techniques described here can be used for other applications like multi-user detection or space-time trellis codes

Kinetic Monte Carlo simulations with minimal searching.

Kinetic Monte Carlo (KMC) simulations are used to simulate epitaxial crystal growth. Presently, the fastest reported methods use binary trees to search through a list of rates in O(log(2) M) time, where M is the number of rates. These methods are applicable to an arbitrary set of rates, but typical KMC bond-counting schemes involve only a finite set of distinct rates. This allows one to construct a faster list-based algorithm with a computation time that is essentially independent of M. It is found that this algorithm typically reduces computation time by between 30% and 50% for typical simulations, with this factor increasing for larger simulations.