Search Results - key works parallel/distributed algorithms

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method by Williams-Young, David B, Tubman, Norm M, Mejuto-Zaera, Carlos, de Jong, Wibe A

“… Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI…”
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Takagi–Sugeno fuzzy-based approach for modeling and control of an activated sludge process by Arifi, Ali, Bouallègue, Soufiene

“… Given its better performance, biological treatment via an activated sludge process (ASP) represents the key phase in the overall treatment chain…”
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An efficient theta-join query processing in distributed environment by Liu, Wenjie, Li, Zhanhuai

“… In this work, we propose a filter method for theta-join on the purpose of reducing the computation cost and achieving the minimum execution time in distributed environment…”
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Influence of the Migration Policy in Parallel Distributed GAs with Structured and Panmictic Populations by Alba, Enrique, Troya, José M.

“… The difference lies in whether any individual in the population can mate with any other one or not. In this work, they are both considered as two reproductive loop types executed in the islands of a parallel distributed GA…”
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Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments by Vu, Trong-Tuan, Derbel, Bilel

“… B&B algorithms while describing efficient techniques…”
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A Latency-Hiding Algorithm for ABMS on Parallel/Distributed Computing Environment by Li-li Chen, Jian-xin Huang, Jing Zhang

“…) on parallel/distributed computing platform is proposed. The key idea of this algorithm is using redundant computations to hide communication latencies…”
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Numerical and real time analysis of parallel distributed GAs with structured and panmictic populations by Alba, E., Cotta, C., Troya, J.M.

“… The difference relies on whether any individual in the population can mate with any other one or not. In this work they both are considered as two reproductive loop types executed in the islands of a parallel distributed GA…”
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Fast and Precise: Parallel Processing of Vehicle Traffic Videos Using Big Data Analytics by Perafan-Villota, Juan C., Mondragon, Oscar H., Mayor-Toro, Walter M.

“… Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant…”
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An efficient parallel algorithm of N-hop neighborhoods on graphs in distributed environment by LIU, Wenjie, LI, Zhanhuai

“…) on a parallel distributed graph processing framework, such as Pregel and GraphLab. However, due to the massive volume of message transfer, the BFS method results in high communication cost and has low efficiency…”
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Efficient Anytime Anywhere Algorithms for Closeness Centrality in Large and Dynamic Graphs by Santos, Eunice E., Korah, John, Murugappan, Vairavan, Subramanian, Suresh

“… In our previous work, we presented an overarching anytime anywhere framework for designing parallel and distributed social network analysis algorithms that are scalable to large network sizes and can handle dynamism…”
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Parallel Improvements of the Jaya Optimization Algorithm by Migallón, Héctor, Jimeno-Morenilla, Antonio, Sanchez-Romero, Jose-Luis

“…A wide range of applications use optimization algorithms to find an optimal value, often a minimum one, for a given function…”
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Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data by Kumar, Ankit, Varshney, Neeraj, Bhatiya, Surbhi, Singh, Kamred Udham

“… MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large datasets…”
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Scalable distributed data cube computation for large-scale multidimensional data analysis on a Spark cluster by Lee, Suan, Kang, Seok, Kim, Jinho, Yu, Eun Jung

“… Previous works have developed various algorithms for efficiently generating data cubes using MapReduce, which is a large-scale distributed parallel processing framework…”
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Load balancing in homogeneous pipeline based applications by Moreno, A., Cesar, E., Guevara, A., Sorribes, J., Margalef, T.

“… Parallel/distributed programming provides the possibility of solving highly demanding computational problems…”
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Scaling Expected Force: Efficient Identification of Key Nodes in Network-Based Epidemic Models by Labini, Paolo Sylos, Jurco, Andrej, Ceccarello, Matteo, Guarino, Stefano, Mastrostefano, Enrico, Vella, Flavio

“… In this paper, we present a novel cluster-centric, parallel algorithm enhancing ExF's efficiency and scalability…”
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Reservoir Echo State Network for Classification of Multivariate Time Series by Purkayastha, Basab Bijoy, Barma, Shovan

“… It leverages both CPU-shared memory and parallel distributed memory architecture to efficiently capture reservoir state's optimal model space representation, addressing computational challenges in MTS analysis…”
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Bandit-based Variable Fixing for Binary Optimization on GPU Parallel Computing by Yasudo, Ryota

“… In particular, we introduce a novel approach called the bandit-based variable fixing (BVF). The key idea behind BVF is to regard an execution of an arbitrary metaheuristic with a variable fixed as a play of a slot machine…”
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Singular value decomposition utilizing parallel algorithms on graphical processors by Kotas, C., Barhen, J.

“… In order to enable robust detection, one of the key processing steps requires data and replica whitening…”
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Optimizing the pollution routing problem with CUDA accelerated discrete particle swarm optimization algorithm by Davendra, Donald, Metlicka, Magdalena

“…A major contributor to global greenhouse gas (GHG) emission is the transportation sector, where vehicle load, fuel type and speed, amongst others, are key factors…”
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Optimizing Machine Learning Algorithms on Multi-Core and Many-Core Architectures Using Thread and Data Mapping by Serpa, Matheus S., Krause, Arthur M., Cruz, Eduardo H.M., Navaux, Philippe Olivier Alexandre, Pasin, Marcelo, Felber, Pascal

“… The algorithms at the core of machine learning are notoriously demanding in terms of resources…”
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