Toward an operating system that supports parallel processing on nondedicated clusters

Present operating systems are not built to support parallel computing on clusters - they do not provide services to manage parallelism, i.e., to manage parallel processes and cluster resources. They do not provide support for both programming paradigms, Message Passing (MP) or Distributed Shared Memory (DSM). Due to poor operating systems, users must deal with computers of a cluster rather than to see this cluster as a single powerful computer. There is a need for cluster operating systems. We claim that it is possible to develop a cluster operating system that is able to efficiently manage parallelism, support MP and DSM and offer transparency. To substantiate this claim the first version of a cluster operating system managing parallelism and offering transparency, called GENESIS, has been developed.

Cluster operating system support for parallel autonomic computing

The aim of this paper is to show a general design of autonomic elements and initial implementation of a cluster operating system that moves parallel processing on clusters to the computing mainstream using the autonomic computing vision. The significance of this solution is as follows. Autonomic Computing was identified by IBM as one of computing's Grand Challenges. The human body was used to illustrate an Autonomic Computing system that possesses self-knowledge, self-configuration, self optimization, self-healing, and self-protection, knowledge of its environment and user friendliness properties. One of the areas that could benefit from the comprehensive approach created by the autonomic computing vision is parallel processing on non-dedicated clusters. Many researchers and research groups have responded positively to the challenge by initiating research around one or two of the characteristics identified by IBM as the requirements for autonomic computing. We demonstrate here that it is possible to satisfy all Autonomic Computing characteristics.

Investigation of an optimistic based concurrency control mechanism for parallel file systems

Parallel file systems offer improved performance over traditional file systems by allowing processes to access file data, stored over a number of disks, in parallel. When this file is to be accessed by many processes at the same time for reading and writing, the problem of maintaining file consistency is introduced. The general approach to maintaining file consistency is through the use locks which grant a single process exclusive access to the file. Existing studies have found parallel file systems and associated applications rarely experience conflict, resulting in unnecessary overheads from locks being acquired. This paper examines an alternate approach to maintaining file consistency, which is based on optimistic concurrency control. This approach was shown to have a much smaller overhead when compared with traditional lock-based approaches; especially in situations when there is little contention. This paper presents the design, implementation and initial testing results of an optimistic based concurrency control mechanism.

Performance evaluation of the concurrent execution of NAS parallel benchmarks with BYTE sequential benchmarks on a cluster

Computers of a non-dedicated cluster are often idle (users attend meetings, have lunch or coffee breaks) or lightly loaded (users carry out simple computations). These underutilized computers can be employed to execute parallel applications not only during weekends and at nights but also during office hours. Thus, they have to be shared by parallel and sequential applications which could lead to the improvement of their execution performance. However, there is a lack of experimental study showing the behavior and performance of parallel and sequential applications executing concurrently on clusters. We present here the result of an experimental study into load balancing based scheduling of a mixture of parallel and sequential applications on a non-dedicated cluster.

A parallel Euler approach for large-scale biological sequence assembly

Biological sequence assembly is an essential step for sequencing the genomes of organisms. Sequence assembly is very computing intensive especially for the large-scale sequence assembly. Parallel computing is an effective way to reduce the computing time and support the assembly for large amount of biological fragments. Euler sequence assembly algorithm is an innovative algorithm proposed recently. The advantage of this algorithm is that its computing complexity is polynomial and it provides a better solution to the notorious “repeat” problem. This paper introduces the parallelization of the Euler sequence assembly algorithm. All the Genome fragments generated by whole genome shotgun (WGS) will be assembled as a whole rather than dividing them into groups which may incurs errors due to the inaccurate group partition. The implemented system can be run on supercomputers, network of workstations or even network of PC computers. The experimental results have demonstrated the performance of our system.

Robust parallel filtering for mobile agent tracking

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

Parallel I/O scheduling in the presence of data duplication on multiprogrammed cluster computing systems

The widespread adoption of cluster computing as a high performance computing platform has seen the growth of data intensive scientific, engineering and commercial applications such as digital libraries, climate modeling, computational chemistry, computational fluid dynamics and image repositories. However, I/O subsystem performance has not been keeping pace with processor and memory performance, and is fast becoming the dominant factor in overall system performance.  Thus, parallel I/O has become a necessity in the face of performance improvements in other areas of computing systems. This paper addresses the problem of parallel I/O scheduling on cluster computing systems in the presence of data replication.  We propose two new I/O scheduling algorithms and evaluate the relative performance of the proposed policies against two existing approaches.  Simulation results show that the proposed policies perform substantially better than the baseline policies.

Robust parallel filtering design of a swarm tracking system

Swarming networks of mobile autonomous agents require inter-agent position information in order perform various tasks. The primary control input for the majority of current control strategies is inter-agent distance information. In this paper we provide a robust parallel filter based tracking scheme that allows a mobile agent to track other multiple mobile agents. The distance, angle, and relative position is given in a direct target tracking output. This allows the mobile agent to decide which information is best suited for the particular objective

High-precision five-axis machine for high-speed material processing using linear motors and parallel-serial kinematics

The paper describes some details of the mechanical and kinematics design of a five-axis mechanism. The design has been utilized to physically realize an industrial-scale five-axis milling machine that can carry a three KW spindle. However, the mechanism could be utilized in other material processing and factory automation applications. The mechanism has five rectilinear joints/axes. Two of these axes are arranged traditionally, i.e. in series, and the other three axes utilize the concept of parallel kinematics. This combination results in a design that allows three translational and two rotational two-mode degrees of freedom (DOFs). The design provides speed, accuracy and cost advantages over traditional five-axis machines. All axes are actuated using linear motors.

Asymmetrical three-DOFs rotational-translational parallel-kinematics mechanisms based on lie group theory

The paper introduces four families of three-DOFs translational-rotational Parallel-Kinematics Mechanisms (PKMs) as well as the mobility analysis of such families using Lie group theory. Two of these families are mechanisms with one-rotational two-translational degrees of freedom (DOFs) and each of the other two has one-translational two-rotational DOFs. Four novel mechanisms are presented and discussed as representatives of these four families. Although these mechanisms are asymmetric, the components used to realise them are very similar and, hence, there is no great departure from the favourable modularity of parallel-kinematics mechanisms.

Contemporaneous intra-day volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.

The performance of a parallel TSP program and byte sequential benchmarks executing on a shared cluster

Although individual PCs of a cluster are used by their owners to run sequential applications (local jobs), the cluster as a whole or its subset can also be employed to run parallel applications (cluster jobs) even during working hours. This implies that these computers have to be shared by parallel and sequential applications, which could lead to the improvement of the execution performance and resource utilization. However, there is a lack of experimental study showing the behavior and performance of executing parallel and sequential applications concurrently on a non-dedicated cluster. The result of such research would be beneficial for the development of new global scheduling algorithms. We present the result of an experimental study into scheduling of a mixture of parallel and sequential applications on a non-dedicated cluster. The aim of this study is to learn how the concurrent execution of a communication intensive parallel application and sequential applications influences their execution performance and utilization of the cluster.