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A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks
2018
BMC Bioinformatics
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We propose a novel semi-supervised parallel enhancement of COSNET, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. ...
Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated ...
Also under the same programming paradigm, a parallel implementation of a Greedy Graph Coloring (GGC) algorithm has been developed for solving the graph coloring problem, as required by the PARCOSNET algorithm ...
doi:10.1186/s12859-018-2301-4
pmid:30367594
fatcat:gzrxnqd2r5deljttytqthe5oai
Towards Enhancing Coding Productivity for GPU Programming Using Static Graphs
2022
Electronics
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In the first test case (Conjugate Gradient) we focus on the integration of Static Graphs with CUDA. ...
only, achieving accelerations of up to more than one order of magnitude. ...
using CUDA Graph (in yellow color). ...
doi:10.3390/electronics11091307
fatcat:o3ci4jvekbee3op23hutttoenu
Parallelized solution to the asymmetric travelling salesman problem using central processing unit acceleration
2022
Indonesian Journal of Electrical Engineering and Computer Science
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salesman problem (ATSP) can be accelerated with the help of modern CPUs. ...
of multi-threading-based parallelization. ...
Experiments by Saxena et al. in [8] show that parallelization tools like OpenMP and CUDA can significantly reduce the execution time for genetic algorithms used in solving the TSP. ...
doi:10.11591/ijeecs.v25.i3.pp1795-1802
fatcat:ub37ig2ytvebxelyr5njyhr3wa
GPU computing in discrete optimization. Part II: Survey focused on routing problems
2013
EURO Journal on Transportation and Logistics
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Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. ...
Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. ...
Katz and Kider [41] describe a shared memory cache efficient CUDA implementation to solve transitive closure and the all-pairs shortest-path problem on directed graphs for large datasets. ...
doi:10.1007/s13676-013-0026-0
fatcat:zfalygsovfh5jauczny7wp2sga
GPU acceleration for statistical gene classification
2010
2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)
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Unfortunately, the cost of parallel computing platforms is usually prohibitive for both public and small private medical practices. ...
The results show that using open source CUDA programming libraries allows to obtain a significant increase in performances and therefore to shorten the gap between advanced bioinformatic tools and real ...
Bioinformatic is the computer science discipline that strives to solve these problems. ...
doi:10.1109/aqtr.2010.5520794
fatcat:6fcuv7vzkbgizbf47fffjjw6m4
Solving the Examination Timetabling Problem in GPUs
2014
Algorithms
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In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. ...
The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. ...
[18] , the techniques used to solve the ETP problem can be categorized into graph based, constraint based, local search based, population based, multi-criteria techniques and hyper-heuristics. ...
doi:10.3390/a7030295
fatcat:ajwkkqjhjzdzvghdww2pac7squ
Mathematical modeling of nonlinear effects in dynamic of interacting plankton and fish populations of Azov Sea
2019
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
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Effective parallel algorithms were developed for numerical implementation of biological kinetics problem and oriented on NVIDIA Tesla K80 graphics accelerator with the data storage format modification. ...
, taxis, catching, spatial distribution of biogenic matter and detritus based on a multispecies model of plankton and fish interaction. ...
The developed algorithm for solving the problem uses a CSR1S modified data storage format with further conversion to CSR format to solve the resulting SLAE on a graphics accelerator using NVIDIA CUDA technology ...
doi:10.23947/2587-8999-2019-2-2-83-103
fatcat:krkqmlhmbfatxjnhr4qtninoge
X-Aevol
2021
Proceedings of the Genetic and Evolutionary Computation Conference Companion
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We implement our new algorithms with CUDA programming language and test them on a representative benchmark of Aevol workloads. ...
X-Aevol is the GPU port of the Aevol model, a bio-inspired genetic algorithm designed to study the evolution of micro-organisms and its effects on their genome structure. ...
In fact, all these models are genetic algorithms. However, an important difference has to be emphasised. Classical genetic algorithms [1, 12, 24, 25] are designed to solve a given useful problem. ...
doi:10.1145/3449726.3463195
fatcat:lzio7akvdfebrpudrvbehrrcoi
Automatic discovery of algorithms for multi-agent systems
2012
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12
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Automatic algorithm generation for large-scale distributed systems is one of the holy grails of artificial intelligence and agent-based modeling. ...
NetLogo has been extensively used as a teaching and research tool by computer scientists, for example for exploring distributed algorithms. ...
Our approach to solve the problem at hand is to use Genetic Programming in order to discover algorithms that fulfill the search goal. ...
doi:10.1145/2330784.2330833
dblp:conf/gecco/BerkelTPD12
fatcat:ydknfmxbvfahjlafybajdvv6ue
GPU-accelerated parallel gene-pool optimal mixing in a gray-box optimization setting
2022
Proceedings of the Genetic and Evolutionary Computation Conference
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We test the performance of a CUDA implementation of parallel GOM on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known problem for which the dependency structure can be controlled. ...
For large GBO problems, parallelizing GOM-based variation holds greater speed-up potential, regardless of population size. ...
Therefore, we use the greedy Welsh-Powell algorithm [30] to find a graph coloring. ...
doi:10.1145/3512290.3528797
fatcat:jb57s42bivf57ju5ewdscdunhq
Parallel and Serial Graph Coloring Implementations with Tabu Search Method
2019
International journal of recent technology and engineering
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We explore both parallel and serial Tabu search algorithm for graph coloring with arbitrary number of nodes. ...
One of the well-known property of graph is graph coloring. Any two vertices of a graph are different colors such that they are adjacent to each other. ...
[2] introduced a new parallel genetic algorithm to take care of the Graph coloring problem (GCP) in view of Computer Unified Device Architecture (CUDA). ...
doi:10.35940/ijrte.b1840.078219
fatcat:nhjz6liwjvg2fn7zkpkusizvyi
Parallel Implementations of Candidate Solution Evaluation Algorithm for N-Queens Problem
2021
Complexity
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The N-Queens problem plays an important role in academic research and practical application. Heuristic algorithm is often used to solve variant 2 of the N-Queens problem. ...
In this paper, three parallel schemes based on CPU and four parallel schemes based on GPU are proposed, and a serial scheme is implemented at the baseline. ...
In this paper, we focus on how to improve the speed of the heuristic algorithms for solving variant 2 by accelerating the evaluation function. ...
doi:10.1155/2021/6694944
fatcat:aamswkg5efgvbio2k5yse6be2a
A Systematic Survey of General Sparse Matrix-Matrix Multiplication
[article]
2023
arXiv
pre-print
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2020 pre-print arXiv:2002.11273v1 fatcat:5ppccisodvaevdhvfhawvjam5qOther Versions
Existing researches have been grouped into different categories based on target architectures and design choices. ...
Many optimization techniques have been developed for different applications and computing architectures over the past decades. ...
In addition, SpGEMM is also one of the most important components for graph contraction [38] , graph matching [39] , graph coloring [13] [14] , all pairs shortest path [12] , sub-graph [15] [ ...
arXiv:2002.11273v2
fatcat:2vuftf7tvnbnhduoexofwuxizi
Population-based Gradient Descent Weight Learning for Graph Coloring Problems
[article]
2020
arXiv
pre-print
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In this work, a general population-based weight learning framework for solving graph coloring problems is presented. ...
The usefulness of the proposed approach is demonstrated by applying it to solve two typical graph coloring problems and performing large computational studies on popular benchmarks. ...
Acknowledgments We are grateful to the reviewers for their useful comments and suggestions which helped us to significantly improve the paper. ...
arXiv:1909.02261v4
fatcat:cnss2fruu5dl3pdkhmdh7adkt4
Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs
2017
Constraints
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Inference-based algorithms are powerful techniques for solving discrete optimization problems, which can be used independently or in combination with other techniques. ...
to speed up the resolution of exact and approximated inference-based algorithms for discrete optimization. ...
Conclusions and Discussions Inference-based algorithms are powerful tools for solving discrete optimization problems. ...
doi:10.1007/s10601-017-9274-1
fatcat:twvcfyzgajcilbd57lbqmxupda
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