A mathematical model is developed and numerical modeling is performed to solve a scientific and industrial problem in the field of studying mass transfer processes in the "fracture set - matrix" system in a ...
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This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need f...
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This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark's in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making.
The minimum cut and minimum length linear arrangement problems usually occur in solving wiring problems and have a lot in common with job sequencing questions. Both problems are NP-complete for general graphs and in P...
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The minimum cut and minimum length linear arrangement problems usually occur in solving wiring problems and have a lot in common with job sequencing questions. Both problems are NP-complete for general graphs and in P for trees. We present here two parallel algorithms for the CREW PRAM. The first solves the minimum length linear arrangement problem for trees and the second solves the minimum cut arrangement for trees. We prove that the first problem belongs to NC for trees, and the second problem is in NC for bounded degree trees. To the best of our knowledge, these are the first parallel algorithms for the minimum length and the minimum cut linear arrangement problems.
We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solving some algorithmic problems in graphs, including split decompositi...
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We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solving some algorithmic problems in graphs, including split decomposition. We show that efficient parallel split decomposition induces an efficient parallel parity graph recognition algorithm. This is a consequence of the result of S. Cicerone and D. Di Stefano [7] that parity graphs are exactly those graphs that can be split decomposed into cliques and bipartite graphs, (C) 2000 Academic Press.
A bus system whose configuration can be dynamically changed is called reconfigurable bus system. In this paper, parallel algorithms for generating combinations, subsets, and binary trees on Linear processor array with...
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A bus system whose configuration can be dynamically changed is called reconfigurable bus system. In this paper, parallel algorithms for generating combinations, subsets, and binary trees on Linear processor array with reconfigurable bus systems (PARBS) are presented.
The geometric connected component labeling (GCCL) problem occurs as an important subproblem when parallel algorithms are sought for problems like VLSI circuit extraction. parallel algorithms for this problem on a hype...
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The geometric connected component labeling (GCCL) problem occurs as an important subproblem when parallel algorithms are sought for problems like VLSI circuit extraction. parallel algorithms for this problem on a hypercube multiprocessor can be designed by dividing the domain, consisting of a number of rectangles, into regions using a Slice or Rectangular partitioning Scheme. Each processor in the hypercube is assigned one partition. The processor determines the connected sets of rectangles in its partition. The connected sets at different processors have to then be combined across processors into globally connected sets. This merging problem is defined as the GCCL problem. In this paper, we present different algorithms for the GCCL problem. Each of the algorithms involves d stages of message passing, for a d-dimensional hypercube. The basic idea in these algorithms is that in each stage a processor increases its knowledge of the domain. The algorithms described in this paper differ in their run time, memory requirements, and message complexity. These algorithms have been implemented on an Intel iPSC2/D4/MX hypercube and the results are described in the paper.
parallel algorithms are presented for modules of learning automata with the objective of improving their speed of convergence without compromising accuracy. A general procedure suitable for parallelizing a large class...
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parallel algorithms are presented for modules of learning automata with the objective of improving their speed of convergence without compromising accuracy. A general procedure suitable for parallelizing a large class of sequential learning algorithms on a shared memory system is proposed. Results are derived to shea the quantitative improvements in speed obtainable using parallelization, The efficacy of the procedure is demonstrated by simulation studies on algorithms for common payoff games, parametrized learning automata and pattern classification problems with noisy classification of training samples.
We present new local-memory multiprocessor algorithms for solving sparse triangular systems of equations that arise in the context of Cholesky factorization. Unlike in the existing algorithms, we use the notion of the...
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We present new local-memory multiprocessor algorithms for solving sparse triangular systems of equations that arise in the context of Cholesky factorization. Unlike in the existing algorithms, we use the notion of the elimination tree and achieve significant improvement in the performance of both the forward and backward substitution phases. Our algorithms also incorporate the generalization of an important technique of Li and Coleman that gave rise to the best performance for dense triangular system solution.
In this paper, we first demonstrate that the classical Purcell's vector method when combined with row pivoting yields a consistently small growth factor in comparison to the well-known Gauss elimination method, th...
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In this paper, we first demonstrate that the classical Purcell's vector method when combined with row pivoting yields a consistently small growth factor in comparison to the well-known Gauss elimination method, the Gauss-Jordan method and the Gauss-Huard method with partial pivoting. We then present six parallel algorithms of the Purcell method that may be used for direct solution of linear systems. The algorithms differ in ways of pivoting and load balancing. We recommend algorithms V and VI for their reliability and algorithms III and IV for good load balance if local pivoting is acceptable. Some numerical results are presented. (C) 2002 Elsevier Science B.V. All rights reserved.
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