Heterogeneous computing scheduling with evolutionary algorithms

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.

Over the past several years, a heterogeneous computing (HC) system has become more competative as a commercial computing platform than a homogeneous system. With the growing scale of HC systems, network failures become inevitable. To achieve high performance, communication reliability should be considered while designing reliability-aware task scheduling algorithms. In this paper, we propose a new algorithm called RMSR (Replication-based scheduling for Maximizing System Reliability), which incorporates task communication into system reliability. To maximize communication reliability, an improved algorithm which searches all optimal reliability communication paths for current tasks is proposed. During the task replication phase, the task reliability threshold is determined by users and each task has dynamic replicas. Our comparative studies based on randomly generated graphs show that our RMSR algorithm outperforms existing scheduling algorithms in terms of system reliability. Several factors affecting the performance are analyzed in the paper.

This article presents the application of a parallel evolutionary algorithm implemented in both CPU and Graphic Processing Units (GPU), to solve large instances of the noisy OneMax problem with up to one billion variables. Actually, new GPU platforms provide the computing power needed to apply massively parallel strategies to solve large problems. We report here the experimental evaluation of both CPU and GPU implementations for a compact evolutionary algorithm. the proposed method demonstrates a high problem solving efficacy and shows a good scalability behavior when facing high dimension instances of the noisy OneMax problem, improving the computational efficiency and the results reported in previous similar approaches developed on CPU.

Ideally, a heterogeneous computing (HC) environment, is a well-orchestrated and coordinates suite of high-performance machines that provides support for computationally intensive applications with diverse computing requirements. Such an HC system includes a heterogeneous suite of machines, high-speed interconnections, interfaces, operating systems, communication protocols, and programming environments. HC is the effective use of these diverse hardware and software components to meet the distinct and varied computational requirements of a given application. Implicit in this concept of HC is the idea that subtasks with different machine architectural requirements are embedded in the applications executed by the HC system. Two types of HC systems, mixed-mode machines and mixed-machine systems, are discussed. The goals of and open problems in HC are overviewed.

article Free Access Share on Software support for heterogeneous computing Authors: Howard Jay Siegel Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , Henry G. Dietz Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , John K. Antonio Texas Tech University, Lubbock Texas Tech University, LubbockView Profile Authors Info & Claims ACM Computing SurveysVolume 28Issue 1March 1996 pp 237–239https://doi.org/10.1145/234313.234411Published:01 March 1996Publication History 21citation711DownloadsMetricsTotal Citations21Total Downloads711Last 12 Months67Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.

In this article, evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed to cope with the high computational requirements of such a problem. Here, we develop and evaluate a set of sequential and parallel genetic algorithms (GAs) to solve the RND problem efficiently. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster.

After more than fifteen years, JavaScript has finally risen as a popular language for implementing all kind of applications, from server-based to rich internet applications. The fact that it is implemented in the browser and in server-side tools makes it interesting for designing evolutionary algorithm frameworks that encompass both tiers, but besides, they allow a change in paradigm that goes beyond the canonical evolutionary algorithm. In this paper we will experiment with different architectures, client-server and peer to peer to assess which ones offer most advantages in terms of performance, scalability and ease of use for the computer scientist. All implementations have been released as open source, and besides showing that the concept of working with evolutionary algorithms in JavaScript can be done efficiently, we prove that a master-slave parallel architecture offers the best combination of time and algorithmic improvements in a parallel evolutionary algorithm that leverages JavaScript implementation features.

The paper presents an approach to design optimization using parallel evolutionary algorithms. The only use of a simple evolutionary algorithm in order to generate the optimal solution for complex problems can be ineffective due to long calculation time. Thus a tournament evolutionary algorithm (EA) and a parallel computation method are proposed and used. The proposed EA does not require an analysis of the optimization model for each potential solution from evolutionary populations. The second element of the method consists in parallel running of evolutionary algorithms using multi-threads approach. The experiments were carried out for many different single design optimization problems and two of them are presented in the paper. The first problem considers a task of robot gripper mechanism optimization and the second one deals with the optimization of a shaft based on Finite Element Method analysis. From the generated results it is clear that proposed approach is a very effective tool for solving fairly complicated tasks considering both the accuracy and the time of calculation.

The article presents a comparative analysis of the implementation of parallel algorithms on the central processors of automation systems in agriculture. Modern automation systems impose increased requirements on the reliability of the implementation of parallel algorithms in real time. It is proposed to use models for the development, analysis and comparison of parallel algorithms on GPUs. The proposed model of parallel computing on GPUs is designed to simplify the development of parallel algorithms for a heterogeneous CPU-GPU environment. Those, with this model, you can: develop parallel algorithms that use data parallelism, while applying the existing experience in creating parallel algorithms for the PRAM machine; evaluate the running time of the parallel algorithm and analyze which part of it is the most resource-intensive and requires optimization; compare parallel algorithms according to some parameters of the model, and, if required, select the best one according to these parameters in real time for automation in agriculture.

Analysis of speedups in parallel evolutionary algorithms and [formula omitted] EAs for combinatorial optimization

Due to high operational cost, the problem of scheduling a batch of tasks (BoT) applications on heterogeneous computing system (HCS) remains a challenging problem. Accordingly, a plethora of evolutionary algorithms (EAs) and non-EAs have been proposed as solutions. Due to the ability of exploration of major solution space, EAs have been proven to be very effective in addressing the job scheduling problem. This work proposes two hybrid bio-inspired scheduling algorithms VPG and VDG featuring the combined best properties of VNS, PSO, DE and GA. The expected-time-to-compute (ETC) benchmark have been used to first present the performance of eight non-EAs viz. MCT, MinMin, MaxMin, Sufferage, HLTF, relative cost, MINMin and MINSuff in terms of makespan and energy consumption. The study is then extended to evaluate the performance of VPG, VDG and their seeded variants with GA, PSO and DE. Simulation study establishes the superior performance of VDG over peers.

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

This work presents novel parallel biologically inspired hybrid heuristics for task scheduling in distributed heterogeneous computing and grid environments, and NP-hard problems with capital relevance in distributed computing. Firstly, sequential hybrid metaheuristics based on artificial immune systems (AIS) are developed to provide a good scheduler in reduced execution time and improved resource utilization. In the new AIS, affinities of the antibody’s genes are also effectively evaluated and regarded as memes from population real-time evolution; self-organized gene–meme co-evolution is simulated to improve population convergence; and appropriate Lyapunov functions inspired by interactive activation and competition neural networks are constructed to balance exploration and exploitation. Secondly, parallelization of the AIS-based algorithm is hierarchically designed and integrates with the two traditional parallel models (master–slave models and island models). The method has been specifically implemented on the newly developed supercomputer platform of hybrid multi-core CPU+GPU using C-CUDA for solving large-sized realistic instances. Numerical experiments are performed on both well known problem instances and large instances that model medium-sized grid environments. The comparative study shows that the proposed parallel approach is able to achieve high solving efficacy, outperforming previous results reported in the related literature, and also showing good scalability behaviour when facing high-dimension problem instances.

As more generators and loads are integrated by power electronic converters with complicated controls, electromagnetic transients (EMTs) simulation becomes an important tool for studying dynamic characteristics of large‐scale power systems. To accelerate system‐level EMT simulations, a fine‐grained parallel algorithm on graphics processing units (GPU) is proposed. By decomposing the computational models of the EMT simulation into heterogeneous, homogeneous and network solution computations, the simulations are mapped into three unified GPU kernels. To incorporate control signals and non‐linear features of electrical components, heterogeneous computations are formulated as layered direct acyclic graphs (LDAG) of primitive operations. An LDAG kernel is designed to carry out theses primitive operations efficiently by grouped threads. Then, homogeneous computations for state updates of electrical components are modelled as sets of fused multiply‐add (FMA) operations, which are concurrently processed by an FMA kernel. Moreover, a hybrid network solution kernel is designed to solve the network equations, which can adaptively select dense or sparse solvers. Large‐scale test systems are created and simulated on an NVIDIA K20x GPU. The results show that the proposed GPU‐based EMT simulations are accurate and achieve 10x speedups over the CPU‐based ones.