Cluster Of Workstations Research Articles

Decongestants for clogged networks

Interconnection networks are a key element in a wide variety of systems: massive parallel processors, local and system area networks, clusters of PCs and workstations, and Internet Protocol routers. They are essential to high performance in the form of high-bandwidth communications, with low latency, "quality of service" (guaranteed service levels), efficient switching, and flexibility of network topology, as embodied in Myrinet, InfiniBand, Quadrics, Advanced Switching, and similar interconnects. But, despite all the advances that modem interconnects offer, congestion is a growing problem as "lossless" interconnection networksrdquo those that do not allow data packets to be discarded" come to the fore.

MUSKEL: an expandable skeleton environment

Programming models based on algorithmic skeletons promise to raise the level of abstraction perceived by programmers when implementing parallel applications, while guaranteeing good performance figures. At the same time, however, they restrict the freedom of programmers to implement arbitrary parallelism exploitation patterns. In fact, efficiency is achieved by restricting the parallelism exploitation patterns provided to the programmer to the useful ones for which efficient implementations, as well as useful and efficient compositions, are known. In this work we introduce muskel , a full Java library targeting workstation clusters, networks and grids and providing the programmers with a skeleton based parallel programming environment. muskel is implemented exploiting (macro) data flow technology, rather than the more usual skeleton technology relying on the use of implementation templates. Using data flow, muskel easily and efficiently implements both classical, predefined skeletons, and user-defined parallelism exploitation patterns. This provides a means to overcome some of the problems that Cole identified in his skeleton manifesto as the issues impairing skeleton success in the parallel programming arena. We discuss fully how user-defined skeletons are supported by exploiting a data flow implementation, experimental results and we also discuss extensions supporting the further characterization of skeletons with non-functional properties, such as security, through the use of Aspect Oriented Programming and annotations.

Parallel strategies for the local biological sequence alignment in a cluster of workstations

Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith–Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith–Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used.

Parallel computing for finite element structural analysis using conjugate gradient method based on domain decomposition

Parallel finite element method using domain decomposition technique is adapted to a distributed parallel environment of workstation cluster. The algorithm is presented for parallelization of the preconditioned conjugate gradient method based on domain decomposition. Using the developed code, a dam structural analysis problem is solved on workstation cluster and results are given. The parallel performance is analyzed.

Multi-objective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations

This study presents a fast and scalable multi-objective association rule mining technique using genetic algorithm from large database. The objective functions such as confidence factor, comprehensibility and interestingness can be thought of as different objectives of our association rule- mining problem and is treated as the basic input to the genetic algorithm. The outcomes of our algorithm are the set of non-dominated solutions. However, in data mining the quantity of data is growing rapidly both in size and dimensions. Furthermore, the multi-objective genetic algorithm (MOGA) tends to be slow in comparison with most classical rule mining methods. Hence, to overcome these difficulties we propose a fast and scalability technique using the inherent parallel processing nature of genetic algorithm and a homogeneous dedicated network of workstations (NOWs). Our algorithm exploit both data and control parallelism by distributing the data being mined and the population of individuals across all available processors. The experimental result shows that the algorithm has been found suitable for large database with an encouraging speed up.

Parallel Simulation Study of a Laser Surface Alloying Process

ABSTRACT Numerical simulation of transient phase-change processes is massively time consuming, and can benefit from parallel processing to reduce the computational costs. In the present work, an implicit finite-volume-based three-dimensional, transient, turbulent model is developed to describe the transport phenomena for a typical laser surface alloying process, and the code is subsequently parallelized for effective computational economy. Parallelization of the code is done following the pipelining method, which uses temporal parallelism in a distributed computing environment and runs on a cluster of workstations (COW) connected with local area networking (LAN). Equal partitioning of the spatial domain into subdomains along the longer geometric dimension is performed, in order to decompose the computational domain. The Message Passing Interface (MPI) utility is used for communications among the subdomains on each processor. To demonstrate the parallel performance, a systematic analysis is carried out, with up to 16 nodes and various grid sizes. It is found that the overall parallel performance improves up to a certain number of processors, and then degrades with an increase in number of processors, due to communication delays in the LAN and pipeline hazards.

0024 FIRST PSYCHOTIC EPISODE – SHORT TERM RESULTS OF A FOLLOW-UP STUDY

In this paper, we investigate the issues involved in developing a scalable World Wide Web (WWW) server called SWEB on a cluster of workstations. The objective is to strengthen the processing capabilities of such a server in order to match huge demands in simultaneous access requests from the Internet, especially when these requests involve delivery of large digitized documents. The scheduling component of the system actively monitors the usages of CPU, disk I/O channels and the interconnection network to effectively distribute HTTP requests across processing units to exploit task and I/O parallelism. We analyze the maximum number of requests that can be handled by the system and present several experiments to examine the performance of this system.

An empirical study of reliable multicast protocols over Ethernet-connected networks

Recent advances in multicasting over the Internet present new opportunities for improving communication performance for clusters of workstations. Realizing collective communication over reliable multicast can achieve higher performance than over reliable unicast primitives. The standard IP multicast, however, only supports unreliable multicast, which is difficult to use for building high level message passing routines. Thus, reliable multicast primitives must be implemented over the standard IP multicast to facilitate the use of multicast for high-performance communication on clusters of workstations. Although many reliable multicast protocols have been proposed for the wide area Internet environment, the impact of special features of local area networks (LANs) on the performance of the reliable multicast protocols has not been thoroughly studied. Efficient reliable multicast protocols for LANs must exploit these features to achieve the best performance. In this paper, we present our implementation of four types of reliable multicast protocols: the ACK-based protocols, the NAK-based protocols with polling, the ring-based protocols and the tree-based protocols. The protocols are implemented over IP multicast using the standard UDP interface. We evaluate the performance of the protocols over Ethernet-connected networks, study the impact of some special features of the Ethernet on the performance of the protocols and investigate the methods to exploit these features to improve communication performance.

Exploiting idle cycles to execute data mining applications on clusters of PCs

In this paper we present and evaluate Inhambu, a distributed object-oriented system that supports the execution of data mining applications on clusters of PCs and workstations. This system provides a resource management layer, built on the top of Java/RMI, that supports the execution of the data mining tool called Weka. We evaluate the performance of Inhambu by means of several experiments in homogeneous, heterogeneous and non-dedicated clusters. The obtained results are compared with those achieved by a similar system named Weka-Parallel. Inhambu outperforms its counterpart for coarse grain applications, mainly for heterogeneous and non-dedicated clusters. Also, our system provides additional advantages such as application checkpointing, support for dynamic aggregation of hosts to the cluster, automatic restarting of failed tasks, and a more effective usage of the cluster. Therefore, Inhambu is a promising tool for efficiently executing real-world data mining applications. The software is delivered at the project’s web site available at http://incubadora.fapesp.br/projects/inhambu/.

Distributed disk-based algorithms for model checking very large Markov chains

In this paper we present data structures and distributed algorithms for CSL model checking-based performance and dependability evaluation. We show that all the necessary computations are composed of series or sums of matrix-vector products. We discuss sparse storage structures for the required matrices and present efficient sequential and distributed disk-based algorithms for performing these matrix-vector products. We illustrate the effectivity of our approach in a number of case studies in which continuous-time Markov chains (generated in a distributed way from stochastic Petri net specifications) with several hundreds of millions of states are solved on a workstation cluster with 26 dual-processor nodes. We show details about the memory consumption, the solution times, and the speedup. The distributed message-passing algorithms have been implemented in a tool called PARSECS, that also takes care of the distributed Markov chain generation and that can also be used for distributed CTL model checking of Petri nets.

Improving the performance of I/O-intensive applications on clusters of workstations

Load balancing in a workstation-based cluster system has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, if a significant portion of applications running in the system is I/O-intensive, traditional load balancing policies can cause system performance to decrease substantially. In this paper, two I/O-aware load-balancing schemes, referred to as IOCM and WAL-PM, are presented to improve the overall performance of a cluster system with a general and practical workload including I/O activities. The proposed schemes dynamically detect I/O load imbalance of nodes in a cluster, and determine whether to migrate some I/O load from overloaded nodes to other less- or under-loaded nodes. The current running jobs are eligible to be migrated in WAL-PM only if overall performance improves. Besides balancing I/O load, the scheme judiciously takes into account both CPU and memory load sharing in the system, thereby maintaining the same level of performance as existing schemes when I/O load is low or well balanced. Extensive trace-driven simulations for both synthetic and real I/O-intensive applications show that: (1) Compared with existing schemes that only consider CPU and memory, the proposed schemes improve the performance with respect to mean slowdown by up to a factor of 20; (2) When compared to the existing approaches that only consider I/O with non-preemptive job migrations, the proposed schemes achieve improvements in mean slowdown by up to a factor of 10; (3) Under CPU-memory intensive workloads, our scheme improves the performance over the existing approaches that only consider I/O by up to 47.5%.

A parallel programming interface for out-of-core cluster applications

Clusters of workstations are a practical approach to parallel computing that provide high performance at a low cost for many scientific and engineering applications. In order to handle problems with increasing data sets, methods supporting parallel out-of-core computations must be investigated. Since writing an out-of-core version of a program is a difficult task and virtual memory systems do not perform well in some cases, we have developed a parallel programming interface and the support library to provide efficient and convenient access to the out-of-core data. This paper focuses on how these components extend the range of problem sizes that can be solved on the cluster of workstations. Execution time of Jacobi iteration when using our interface, virtual memory and PVFS are compared to characterize the performance for various problem sizes, and it is concluded that our new interface significantly increases the sizes of problems that can be efficiently solved.

Distributed Mining of Maximal Frequent Itemsets on a Data Grid System

In this paper, we propose a new algorithm, named Grid-based Distributed Max-Miner (GridDMM), for mining maximal frequent itemsets from databases on a Data Grid. A frequent itemset is maximal if none of its supersets is frequent. GridDMM is specifically suitable for use in Grid environments due to low communication and synchronization overhead. GridDMM consists of a local mining phase and a global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix-tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. We built a Data Grid system on a cluster of workstations using the open-source Globus Toolkit, and evaluated the GridDMM algorithm in terms of performance, scalability, and the overhead of communication and synchronization. GridDMM demonstrates better performance than other sequential and parallel algorithms, and its performance is scalable in terms of the database size and the number of nodes.

Explicit nonlinear dynamic finite element analysis on homogeneous/heterogeneous parallel computing environment

This paper presents parallel computational strategies to implement explicit nonlinear finite element analysis code onto distributed memory parallel computers for solving large-scale problems in structural dynamics. Implementation details on both homogeneous and heterogeneous parallel processing environments are considered in detail in this paper. Implementation of an explicit nonlinear finite element dynamic analysis code on homogeneous systems is discussed first and this is later moved onto heterogeneous systems. Domain decomposition with explicit message passing is preferred for parallel implementation. The message passing implementation in the parallel algorithm is based on MPI (Message Passing Interface) libraries. Implementation aspects of overlapped, non-overlapped domain decomposition techniques, Dynamic Task Allocation (DTA) and clustering techniques for DTA and their relative merits are presented. The interprocessor communications are optimised by overlapping with computations to improve the performance of the domain decomposition based explicit dynamic analysis finite element code. The issues related to implementation of finite element code for nonlinear dynamic analysis on heterogeneous parallel computing environment are later presented. A new dynamic load-balancing algorithm is developed for this purpose and it is integrated with the domain decomposition based parallel explicit finite element code to test our algorithms on a coarse grain heterogeneous cluster of workstations. Numerical experiments have been carried out on PARAM-10000, an Indian parallel computer and also on cluster of Unix workstations.

A parallel cost‐based abductive reasoning system on workstation cluster

AbstractThis paper proposes a method for dynamic load balancing which works efficiently in the workstation cluster environment. A parallel abductive reasoning system based on the proposed method is also proposed. In the workstation cluster environment, parallel processing is performed using workstations with various processing abilities and loads. Thus, dynamic load balancing must consider the performance and loading conditions of the workstations. This paper proposes a method of estimating the processing ability of the workstation during operation, and also a dynamic load balancing algorithm to balance the estimated conditions. Using the proposed method, a parallel abductive reasoning system which can derive the most preferable explanation for a given observation is implemented. The results of experiments on the system are also reported. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(3): 80–89, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10190

Discretionary Caching for I/O on Clusters

I/O bottlenecks are already a problem in many large-scale applications that manipulate huge datasets. This problem is expected to get worse as applications get larger, and the I/O subsystem performance lags behind processor and memory speed improvements. At the same time, off-the-shelf clusters of workstations are becoming a popular platform for demanding applications due to their cost-effectiveness and widespread deployment. Caching I/O blocks is one effective way of alleviating disk latencies, and there can be multiple levels of caching on a cluster of workstations.Previous studies have shown the benefits of caching—whether it be local to a particular node, or a shared global cache across the cluster—for certain applications. However, we show that while caching is useful in some situations, it can hurt performance if we are not careful about what to cache and when to bypass the cache. This paper presents compilation techniques and runtime support to address this problem. These techniques are implemented and evaluated on an experimental Linux/Pentium cluster running a parallel file system. Our results using a diverse set of applications (scientific and commercial) demonstrate the benefits of a discretionary approach to caching for I/O subsystems on clusters, providing as much as 48% savings in overall execution time over indiscriminately caching everything in some applications.

Trends in high - performance computing

In this article, major recent trends and changes in the high-performance computing (HPC) market place were analyzed. Within the first half of this decade clusters of PCs and workstations have become the prevalent architecture for many HPC application areas in all ranges of performance. However, the Earth Simulator vector system demonstrated that many scientific applications could benefit greatly from other computer architectures. At the same time, there is renewed broad interest in the scientific HPC community for new hardware architectures and new programming paradigms. The IBM BlueGene/L system is one early example of a shifting design focus for large-scale system. The DARPA HPCS program has the declared goal of building a Petaflops computer system by the end of the decade using novel computer architectures.

Parallelising the MARS15 code with MPI for shielding applications

The MARS15 Monte Carlo code capabilities to deal with time-consuming deep penetration shielding problems and other computationally tough tasks in accelerator, detector and shielding applications, have been enhanced by a parallel processing option. It has been developed, implemented and tested on the Fermilab Accelerator Division Linux cluster and network of Sun workstations. The code uses a message passing interface MPI. It is scalable and demonstrates good performance. The general architecture of the code, specific uses of message passing and effects of a scheduling on the performance and fault tolerance are described.

MMR: A MultiMedia Router architecture to support hybrid workloads

During the past few years, multimedia traffic with quality of service (QoS) requirements has become of widespread use. Media servers are usually built as clusters of workstations connected by a high-performance interconnection network. However, these high-performance networks do not usually offer differentiated support for multimedia traffic. The MultiMedia Router (MMR) is a proposal to address the QoS issue in cluster networks within a compact architecture, while also integrating conventional best-effort traffic. This paper describes the main architectural features of the MMR, such as the use of a hybrid switching technique, credit-based flow control, or small input buffers. Also, the main design parameters are tuned by means of simulation. It can be seen how proper differentiation among the different traffic classes is achieved, while retaining a compact design with small buffers.

Cluster Computing for Determining Three-Dimensional Protein Structure

Determining the three-dimensional structure of proteins is crucial to efficient drug design and understanding biological processes. One successful method for computing the molecule's shape relies on inter-atomic distance bounds provided by Nuclear Magnetic Resonance spectroscopy. The accuracy of computed structures as well as the time required to obtain them are greatly improved if the gaps between the upper and lower distance-bounds are reduced. These gaps are reduced most effectively by applying the tetrangle inequality, derived from the Cayley-Menger determinant, to all atom-quadruples. However, tetrangle-inequality bound-smoothing is an extremely computation intensive task, requiring O(n4) time for an n-atom molecule. To reduce computation time, we propose a novel coarse-grained parallel algorithm intended for a Beowulf-type cluster of PCs. The algorithm employs p ? n/6 processors and requires O(n4/p) time and O(p2) communications, where n is the number of atoms in a molecule. The number of communications is at least an order of magnitude lower than in the earlier parallelizations. Our implementation utilized processors with at least 59% efficiency (including the communication overhead)--an impressive figure for a non-embarrassingly parallel problem on a cluster of workstations.