Disk Scheduling for Intermediate Results of Large Join Queries in Shared-Disk Parallel Database Systems

In shared-disk database systems, disk access has to be scheduled properly to avoid unnecessary contention between processors. The first part of this report studies the allocation of intermediate results of join queries (buckets) on disk and derives heuristics to determine the number of processing nodes and disks to employ. Using an analytical model, we show that declustering should be applied even for single buckets to ensure optimal performance. In the second part, we consider the order of reading the buckets and demonstrate the necessity of highly dynamic load balancing to prevent excessive disk contention, especially under skew conditions

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

High-performance cluster computing, algorithms, implementations and performance evaluation for computation-intensive applications to promote complex scientific research on turbulent flows

Large-scale high-performance computing is a very rapidly growing field of research that plays a vital role in the advance of science, engineering, and modern industrial technology. Increasing sophistication in research has led to a need for bigger and faster computers or computer clusters, and high-performance computer systems are themselves stimulating the redevelopment of the methods of computation. Computing is fast becoming the most frequently used technique to explore new questions. We have developed high-performance computer simulation modeling software system on turbulent flows. Five papers are selected to present here from dozens of papers published in our efforts on complex software system development and knowledge discovery through computer simulations. The first paper describes the end-to-end computer simulation system development and simulation results that help understand the nature of complex shelterbelt turbulent flows. The second paper deals specifically with high-performance algorithm design and implementation in a cluster of computers. The third paper discusses the twelve design processes of parallel algorithms and software system as well as theoretical performance modeling and characterization of cluster computing. The fourth paper is about the computing framework of drag and pressure coefficients. The fifth paper is about simulated evapotranspiration and energy partition of inhomogeneous ecosystems. We discuss the end-to-end computer simulation system software development, distributed parallel computing performance modeling and system performance characterization. We design and compare several parallel implementations of our computer simulation system and show that the performance depends on algorithm design, communication channel pattern, and coding strategies that significantly impact load balancing, speedup, and computing efficiency. For a given cluster communication characteristics and a given problem complexity, there exists an optimal number of nodes. With this computer simulation system, we resolved many historically controversial issues and a lot of important problems

Fault-tolerant parallel applications using a network of workstations

PhD thesisIt is becoming common to employ a Network Of Workstations, often referred to as a NOW, for general purpose computing since the allocation of an individual workstation offers good interactive response. However, there may still be a need to perform very large scale computations which exceed the resources of a single workstation. It may be that the amount of processing implies an inconveniently long duration or that the data manipulated exceeds available storage. One possibility is to employ a more powerful single machine for such computations. However, there is growing interest in seeking a cheaper alternative by harnessing the significant idle time often observed in a NOW and also possibly employing a number of workstations in parallel on a single problem. Parallelisation permits use of the combined memories of all participating workstations, but also introduces a need for communication. and success in any hardware environment depends on the amount of communication relative to the amount of computation required. In the context of a NOW, much success is reported with applications which have low communication requirements relative to computation requirements. Here it is claimed that there is reason for investigation into the use of a NOW for parallel execution of computations which are demanding in storage, potentially even exceeding the sum of memory in all available workstations. Another consideration is that where a computation is of sufficient scale, some provision for tolerating partial failures may be desirable. However, generic support for storage management and fault-tolerance in computations of this scale for a NOW is not currently available and the suitability of a NOW for solving such computations has not been investigated to any large extent. The work described here is concerned with these issues. The approach employed is to make use of an existing distributed system which supports nested atomic actions (atomic transactions) to structure fault-tolerant computations with persistent objects. This system is used to develop a fault-tolerant "bag of tasks" computation model, where the bag and shared objects are located on secondary storage. In order to understand the factors that affect the performance of large parallel computations on a NOW, a number of specific applications are developed. The performance of these applications is ana- lysed using a semi-empirical model. The same measurements underlying these performance predictions may be employed in estimation of the performance of alternative application structures. Using services provided by the distributed system referred to above, each application is implemented. The implement- ation allows verification of predicted performance and also permits identification of issues regarding construction of components required to support the chosen application structuring technique. The work demonstrates that a NOW certainly offers some potential for gain through parallelisation and that for large grain computations, the cost of implementing fault tolerance is low.Engineering and Physical Sciences Research Counci

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces