Given n malleable and non-preemptable parallel jobs that arrive for execution at time 0, we examine and compare two job scheduling strategies that allocate m identical processors among the n competing jobs. In all cas...
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ISBN:
(纸本)0780378407
Given n malleable and non-preemptable parallel jobs that arrive for execution at time 0, we examine and compare two job scheduling strategies that allocate m identical processors among the n competing jobs. In all cases, n less than or equal to m. the first strategy is based on the heuristic paradigm of equipartitioning, and the second is based on the notion of marginal analysis. Equipartitioning uses no a priori information when processor allocations are made to parallel jobs. Marginal analysis, on the other hand, assumes full a priori information in order to maximize processor utility. In this paper, we compare both strategies with respect to average time-to-completion (system performance) and overall time-to-completion (system efficiency). Using a simple job model characterized by sequential time-to-completion and degree of parallelism, it is demonstrated via simulation that in most cases, the uninformed strategy of equipartitioning outperforms marginal analysis with respect to system performance and without a commensurate degradation in system efficiency.
In addition to being a quality symbolic debugger for serial IA32 and IPF Linux applications written in C, C++, and Fortran, the Intel74; Debugger is also capable of debugging parallelapplications of Pthreads, Open...
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Most of the popular data mining algorithms are designed to work for centralized data and they often do not pay attention to the resource constraints of distributed and mobile environments. In support of the third gene...
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ISBN:
(纸本)0780378407
Most of the popular data mining algorithms are designed to work for centralized data and they often do not pay attention to the resource constraints of distributed and mobile environments. In support of the third generation of data mining systems on distributed and massive data, we proposed an efficient distributed and mobile algorithm for global association rule mining, which does not need to ship all of local data to one site thereby not causing excessive network communication cost. the algorithm is implemented in PL/SQL for coupling association rule mining with relational database system, well-used in organizations and communities. the experiments show that this algorithm implemented in PL/SQL beats classic Apriori algorithm for large problem sizes, by factors ranging from 2 to more than 20, and this gap grows wider when the volume of transactions further grows up.
Supporting availability, integrity, and confidentiality of data is crucial for parallel computer systems. the parallel computer systems require to encode and distribute data over multiple storage nodes to survive fail...
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Although parallel and distributed computing for a large-scale simulation has many advantages in speed and efficiency, it is difficult for parallel and distributed application to achieve its expected performance, becau...
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In this paper we propose two associative parallel algorithms for the edge update of a minimum spanning tree when an edge is deleted or inserted in the underlying graph. these algorithms are represented as the correspo...
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In this paper we present interval approach to hybrid system verification. When studying a system behavior we suggest keeping track of the minimum and maximum possible value of each continuous variable as well as the p...
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Clustering can be defined as the process of partitioning aset of patterns into disjoint and homogeneous meaningful groups (clusters).there is a growing need for parallel algorithms in this field sincedatabases of huge...
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Using space-filling curves to partition unstructured finite element meshes is a widely applied strategy when it comes to distributing load among several computation nodes. Compared to more elaborated graph partitionin...
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MC# is a programming language for cluster- and GRID-architectures based on asynchronous parallel programming model accepted in Polyphonic C# language (***, ***, ***;Microsoft Research, Cambridge, UK). Asynchronous met...
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ISBN:
(纸本)3540406735
MC# is a programming language for cluster- and GRID-architectures based on asynchronous parallel programming model accepted in Polyphonic C# language (***, ***, ***;Microsoft Research, Cambridge, UK). Asynchronous methods of Polyphonic C# play two major roles in MC#: 1) as autonomous methods executed on remote machines, and 2) as methods used for delivering messages. the former are identified in MC# as the "movable methods", and the latter form a special syntactic class withthe elements named "channels". Similar to Polyphonic C#, chords are used for defining the channels and as a synchronization mechanism. the MC# channels are generalised naturally to "bidirectional channels", which may be used both for sending and receiving messages in the movable methods. the runtime-system of MC# has as the basic operation a copying operation for the object which is scheduled for execution on remote machine. this copy is "dead" after the movable method has finished its work, and all changes of this remote copy are not transferred to the original object. Arguments of the movable method are copied together with an original object, but the passing of bidirectional channels is realised through transferring the proxies for such channels. By way of experiments in MC#, we have written a series of parallel programs such as a computation of Fibonacci numbers, walking through binary tree, computation of primes by Eratosthenes sieve, calculation of Mandelbrot set, modeling the Conway's game "Life", etc. In all these cases, we got the easy readable and compact code. Also we have an experimental implementation in which the compiler is written in ***, and the execution of movable methods on remote machines is based on the Reflection library of NET platform.
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