271644
RUMR: robust scheduling for divisible workloads
Divisible workload applications arise in many fields of science and engineering. They can be parallelized in master-worker fashion and relevant scheduling strategies have been proposed to reduce application markspan. Our goal is to developed a practical divisible workload scheduling strategy. This requires that previous work be revisited as several usual assumptions about the computing platform do not hold in practice. We have partially addressed this concern in a previous paper via an algorithm that achieves high performance with realistic resource latency models. In this paper we extend our approach to account for performance prediction errors, which are expected for most real-world performance and applications. In essence, we combine ideas from multiround divisible workload scheduling, for performance, and from factoring-based scheduling, for robustness. We present simulation results to quantify the benefits of our approach compared to our original algorithm and to other previously proposed algorithms.
16851151
UMR: a multi-round algorithm for scheduling divisible workloads
In this paper we present an algorithm for scheduling parallel divisible workload applications. Our algorithm uses multiple rounds to overlap communication and computation between a master and several workers. We use "uniform" rounds, i.e. a fixed amount of work is sent out to all workers at each round. This restriction makes it possible to compute an approximately optimal number of rounds, which was not possible for previously proposed algorithms. In addition, we use more realistic platform models than those used in previous works. We provide an analysis of our algorithm both for homogeneous and heterogeneous platforms and present simulation results.
8555900
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10 K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
4829760
Exploiting redundancy to implement multiobjective behavior
Teams of robots can be redundant with respect to a given task. This redundancy can be exploited to pursue additional objectives during the execution of the task. In this paper, we describe a control-based method to exploit such redundancy for the execution of additional behavior, leading to the improvement of overall performance. The control-based method provides a suitable mechanism for combining controllers with different objectives. The mechanism ensures that the subordinate controllers do not interfere with the superior controllers. Thus it allows to build controllers exhibiting complex behavior from simple primitives, while maintaining their provable performance characteristics. The effectiveness of the framework is demonstrated by experiments with a multi-robot exploration task.
6183487
Practical routing and torus assignment for RDT
The connection topology of interconnection networks and the routing algorithm often dominate system performance in MPCs. RDT has already been proved to be one of the best interconnection networks used in such systems. It supports a smaller diameter and degree than most at the size of one thousand to tens of thousands nodes. The floating vector routing algorithm is very effective for transferring messages, which also shows good performance. In this paper, a descending vector routing algorithm is proposed. We also proposed a practical torus assignment named RDT(2,2,1)/a and the deadlock-free routing algorithm. RDT(2,2,1)/a mainly focuses on the MPC that has about one thousand nodes. With a simplified simulation and comparison, the results show that RDT(2,2,1)/a has a better performance at the size of one thousand nodes.
205225
Ontological Approach Towards E-business Process Automation
This paper discusses the interoperability of e-business processes by using an ontology approach with description logic and agent systems to achieve e-business automation. An innovative e-business process modeling framework is proposed that outlines the building blocks required for Internet-based e-business in order to enable e-business process automation. The framework helps in understanding the role of many proposed standards with respect to the building blocks and in identifying both overlaps and gaps among them. The domain knowledge of e-business processes is conceptualised as an e-business process ontology that enables agents' communication in e-business application sharing and reusing. Several agent-based automation mechanisms are discussed based on the ontology that provides implementation guidelines to e-business process automation
2153539
Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine
How to predict subcellular multi-locations of proteins with machine learning techniques is a challenging problem in computational biology community. Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper. Two key points of the proposed method are to divide a seriously unbalanced multi-location problem into a number of more balanced two-class subproblems by using the part-versus-part task decomposition approach, and learn all of the subproblems by using the min-max modular support vector machine (M3-SVM). To evaluate the effectiveness of the proposed method, we perform experiments on yeast protein data set by using two kinds of task decomposition strategies and three kinds of feature extraction methods. The experimental results demonstrate that our method achieves the highest prediction accuracy, which is much better than that obtained by the existing approach based on the traditional support vector machine.